[ { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: [OpenDriveLab] # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\npatreon: # Replace with a single Patreon username\nopen_collective: # Replace with a single Open Collective username\nko_fi: # Replace with a single Ko-fi username\ntidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel\ncommunity_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry\nliberapay: # Replace with a single Liberapay username\nissuehunt: # Replace with a single IssueHunt username\notechie: # Replace with a single Otechie username\nlfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry\ncustom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']\n" }, { "path": ".gitignore", "content": "*.log\n*results.json\n*results*.json\n*.pyc\n*.out\nnohup.out\n*.out\nevents*\n*hparams.yaml\nckpt/\n*checkpoints*\n__pycache__\n*work_dirs*\n*batchscript*\nbuild\n*egg-info*\n*cpython*\ncollect_data_json/*.json\ndataset/town??_??\n*dataset_metadata.pkl*\nclosed_loop_eval_log/eval_log/*\n*old_version*" }, { "path": "LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License." }, { "path": "README.md", "content": "> [!IMPORTANT]\n> 🌟 Stay up to date at [opendrivelab.com](https://opendrivelab.com/#news)!\n\n# Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving\n\n- A SOTA Decoder for End-to-End Autonomous Driving under BEV\n- [arXiv Paper](https://arxiv.org/abs/2305.06242) (CVPR 2023)\n\n![pipeline](src/pipeline.PNG)\n \n## Demo Video\n[![Demo](src/demo_start.png)](https://youtu.be/ZhSH63O4Hsg)\n\n## Getting Started\n\n- [Installation](docs/INSTALL.md)\n- [Closed-Loop Evaluation in Carla](docs/EVAL.md)\n- [Prepare Dataset](docs/DATA_PREP.md)\n- [Train Your Own Model](docs/TRAIN.md)\n- [Calibrations for Different Camera Settings](camera_calibration/README.md) (Optional)\n\n## Quick Run in Carla\n\nInstall the environment as in [Installation](docs/INSTALL.md), download our checkpoint ([GoogleDrive](https://drive.google.com/file/d/1Y2bWf8qVwqVQxqM2GOKTiR9kE9nGtkYV/view?usp=share_link) or [BaiduYun](https://pan.baidu.com/s/1OamwKOUpqK0EOqWa1Luv_g)(提取码 m5di).) (189K frames training set), put it into **open_loop_training/ckpt**, and run:\n\n```shell\n## In the ThinkTwice/ directory\nCUDA_VISIBLE_DEVICES=0 nohup bash ./leaderboard/scripts/evaluation_town05long.sh 22023 22033 thinktwice_agent False True open_loop_training/ckpt/thinktwice.pth+open_loop_training/configs/thinktwice.py all_towns_traffic_scenarios_no256 thinktwice_town05long 2>&1 > thinktwice_town05long.log &\n```\n\nCheck [closed_loop_eval_log/eval_log](closed_loop_eval_log/eval_log) to see how our model drives in Carla! :oncoming_automobile:\n\n(In case you have a screen to see the interface of Carla simulator, you could remove *DISPLAY=* in [leaderboard/leaderboard/leaderboard_evaluator.py](leaderboard/leaderboard/leaderboard_evaluator.py) and then you could directly watch with Carla.) \n\n\n## Code Structure\n\nWe give the structure of our code. Note that we only introduce those folders/files are commonly used and modified.\n\n ThinkTwice/\n ├── agents # From Carla official\n ├── camera_calibration # When you want to use cameras with different FOV\n ├── closed_loop_eval_log # Save eval logs\n ├── collect_data_json # Save data collection logs\n ├── dataset # Data and metadata for training\n ├── leaderboard # Code for Closed-Loop Evaluation\n │ ├── data # Save routes and scenarios\n │ ├── scripts # Run with Carla\n │ ├── team_code # Your\n | | ├── roach_ap_agent_data_collection.py # Data collection\n │ | └── thinktwice_agent.py # Interface for closed-loop evaluation of our model\n │ ├── leaderboard # From Carla official\n | | └── leaderboard_evaluator.py # Entrance of closed-loop evaluation\n ├── roach # Roach for data collection\n ├── scenario_runner # From Carla official\n ├── open_loop_training # Training and Neural Network\n | ├── ckpt # Checkpoints\n | ├── work_dirs # Training Log\n | ├── code # Preprocessing, DataLoader, Model\n | │ ├── apis # Training pipeline for mmdet3D\n | │ ├── core # The hooks for mmdet3D\n | │ ├── datasets # Preprocessing and DataLoader\n | | | ├── pipelines # Functions of Preprocessing and DataLoader\n | │ | ├── samplers # For DDP\n | │ | └── carla_dataset.py # Framework of Preprocessing and DataLoading\n | │ ├── model_code # Neural Network\n | | | ├── backbones # Module of Encoder\n | | | └── dense_heads # Module of Decoder and Loss Functions\n | │ └── encoder_decoder_framework.py # Entrance of Neural Network\n | └── train.py # Entrance of Training\n\n## License\n\nAll assets and code are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.\n\n## Bibtex\nIf this work is helpful for your research, please consider citing the following BibTeX entry.\n\n```\n@inproceedings{jia2023thinktwice,\n title={Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving},\n author={Jia, Xiaosong and Wu, Penghao and Chen, Li and Xie, Jiangwei and He, Conghui and Yan, Junchi and Li, Hongyang},\n booktitle={CVPR},\n year={2023}\n}\n```\n\n## Related Resources\nMany thanks to the open-source community!\n\n[![Awesome](https://awesome.re/badge.svg)](https://awesome.re) \n- [End-to-end Autonomous Driving Survey](https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving) (:rocket:Ours!)\n- [TCP](https://github.com/OpenPerceptionX/TCP) (:rocket:Ours!)\n- [BEVFormer](https://github.com/fundamentalvision/BEVFormer) (:rocket:Ours!)\n- [UniAD](https://github.com/OpenDriveLab/UniAD) (:rocket:Ours!)\n- [ST-P3](https://github.com/OpenPerceptionX/ST-P3) (:rocket:Ours!)\n- [Carla](https://github.com/carla-simulator/carla)\n- [Roach](https://github.com/zhejz/carla-roach)\n- [Transfuser](https://github.com/autonomousvision/transfuser)\n- [CARLA_GARGE](https://github.com/autonomousvision/carla_garage)\n- [LAV](https://github.com/dotchen/LAV)\n- [IBISCape](https://github.com/AbanobSoliman/IBISCape)\n" }, { "path": "agents/__init__.py", "content": "" }, { "path": "agents/navigation/__init__.py", "content": "" }, { "path": "agents/navigation/agent.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module implements an agent that roams around a track following random\nwaypoints and avoiding other vehicles.\nThe agent also responds to traffic lights. \"\"\"\n\nimport sys\nimport math\n\nfrom enum import Enum\n\nimport carla\nfrom agents.tools.misc import is_within_distance_ahead, is_within_distance, compute_distance\n\nclass AgentState(Enum):\n \"\"\"\n AGENT_STATE represents the possible states of a roaming agent\n \"\"\"\n NAVIGATING = 1\n BLOCKED_BY_VEHICLE = 2\n BLOCKED_RED_LIGHT = 3\n\n\nclass Agent(object):\n \"\"\"Base class to define agents in CARLA\"\"\"\n\n def __init__(self, vehicle):\n \"\"\"\n Constructor method.\n\n :param vehicle: actor to apply to local planner logic onto\n \"\"\"\n self._vehicle = vehicle\n self._proximity_tlight_threshold = 5.0 # meters\n self._proximity_vehicle_threshold = 10.0 # meters\n self._local_planner = None\n self._world = self._vehicle.get_world()\n try:\n self._map = self._world.get_map()\n except RuntimeError as error:\n print('RuntimeError: {}'.format(error))\n print(' The server could not send the OpenDRIVE (.xodr) file:')\n print(' Make sure it exists, has the same name of your town, and is correct.')\n sys.exit(1)\n self._last_traffic_light = None\n\n def get_local_planner(self):\n \"\"\"Get method for protected member local planner\"\"\"\n return self._local_planner\n\n @staticmethod\n def run_step(debug=False):\n \"\"\"\n Execute one step of navigation.\n\n :param debug: boolean flag for debugging\n :return: control\n \"\"\"\n control = carla.VehicleControl()\n\n if debug:\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n control.manual_gear_shift = False\n\n return control\n\n def _is_light_red(self, lights_list):\n \"\"\"\n Method to check if there is a red light affecting us. This version of\n the method is compatible with both European and US style traffic lights.\n\n :param lights_list: list containing TrafficLight objects\n :return: a tuple given by (bool_flag, traffic_light), where\n - bool_flag is True if there is a traffic light in RED\n affecting us and False otherwise\n - traffic_light is the object itself or None if there is no\n red traffic light affecting us\n \"\"\"\n ego_vehicle_location = self._vehicle.get_location()\n ego_vehicle_waypoint = self._map.get_waypoint(ego_vehicle_location)\n\n for traffic_light in lights_list:\n object_location = self._get_trafficlight_trigger_location(traffic_light)\n object_waypoint = self._map.get_waypoint(object_location)\n\n if object_waypoint.road_id != ego_vehicle_waypoint.road_id:\n continue\n\n ve_dir = ego_vehicle_waypoint.transform.get_forward_vector()\n wp_dir = object_waypoint.transform.get_forward_vector()\n dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z\n\n if dot_ve_wp < 0:\n continue\n\n if is_within_distance_ahead(object_waypoint.transform,\n self._vehicle.get_transform(),\n self._proximity_tlight_threshold):\n if traffic_light.state == carla.TrafficLightState.Red:\n return (True, traffic_light)\n\n return (False, None)\n\n def _get_trafficlight_trigger_location(self, traffic_light): # pylint: disable=no-self-use\n \"\"\"\n Calculates the yaw of the waypoint that represents the trigger volume of the traffic light\n \"\"\"\n def rotate_point(point, radians):\n \"\"\"\n rotate a given point by a given angle\n \"\"\"\n rotated_x = math.cos(radians) * point.x - math.sin(radians) * point.y\n rotated_y = math.sin(radians) * point.x - math.cos(radians) * point.y\n\n return carla.Vector3D(rotated_x, rotated_y, point.z)\n\n base_transform = traffic_light.get_transform()\n base_rot = base_transform.rotation.yaw\n area_loc = base_transform.transform(traffic_light.trigger_volume.location)\n area_ext = traffic_light.trigger_volume.extent\n\n point = rotate_point(carla.Vector3D(0, 0, area_ext.z), math.radians(base_rot))\n point_location = area_loc + carla.Location(x=point.x, y=point.y)\n\n return carla.Location(point_location.x, point_location.y, point_location.z)\n\n def _bh_is_vehicle_hazard(self, ego_wpt, ego_loc, vehicle_list,\n proximity_th, up_angle_th, low_angle_th=0, lane_offset=0):\n \"\"\"\n Check if a given vehicle is an obstacle in our way. To this end we take\n into account the road and lane the target vehicle is on and run a\n geometry test to check if the target vehicle is under a certain distance\n in front of our ego vehicle. We also check the next waypoint, just to be\n sure there's not a sudden road id change.\n\n WARNING: This method is an approximation that could fail for very large\n vehicles, which center is actually on a different lane but their\n extension falls within the ego vehicle lane. Also, make sure to remove\n the ego vehicle from the list. Lane offset is set to +1 for right lanes\n and -1 for left lanes, but this has to be inverted if lane values are\n negative.\n\n :param ego_wpt: waypoint of ego-vehicle\n :param ego_log: location of ego-vehicle\n :param vehicle_list: list of potential obstacle to check\n :param proximity_th: threshold for the agent to be alerted of\n a possible collision\n :param up_angle_th: upper threshold for angle\n :param low_angle_th: lower threshold for angle\n :param lane_offset: for right and left lane changes\n :return: a tuple given by (bool_flag, vehicle, distance), where:\n - bool_flag is True if there is a vehicle ahead blocking us\n and False otherwise\n - vehicle is the blocker object itself\n - distance is the meters separating the two vehicles\n \"\"\"\n\n # Get the right offset\n if ego_wpt.lane_id < 0 and lane_offset != 0:\n lane_offset *= -1\n\n for target_vehicle in vehicle_list:\n\n target_vehicle_loc = target_vehicle.get_location()\n # If the object is not in our next or current lane it's not an obstacle\n\n target_wpt = self._map.get_waypoint(target_vehicle_loc)\n if target_wpt.road_id != ego_wpt.road_id or \\\n target_wpt.lane_id != ego_wpt.lane_id + lane_offset:\n next_wpt = self._local_planner.get_incoming_waypoint_and_direction(steps=5)[0]\n if target_wpt.road_id != next_wpt.road_id or \\\n target_wpt.lane_id != next_wpt.lane_id + lane_offset:\n continue\n\n if is_within_distance(target_vehicle_loc, ego_loc,\n self._vehicle.get_transform().rotation.yaw,\n proximity_th, up_angle_th, low_angle_th):\n\n return (True, target_vehicle, compute_distance(target_vehicle_loc, ego_loc))\n\n return (False, None, -1)\n\n def _is_vehicle_hazard(self, vehicle_list):\n \"\"\"\n\n :param vehicle_list: list of potential obstacle to check\n :return: a tuple given by (bool_flag, vehicle), where\n - bool_flag is True if there is a vehicle ahead blocking us\n and False otherwise\n - vehicle is the blocker object itself\n \"\"\"\n\n ego_vehicle_location = self._vehicle.get_location()\n ego_vehicle_waypoint = self._map.get_waypoint(ego_vehicle_location)\n\n for target_vehicle in vehicle_list:\n # do not account for the ego vehicle\n if target_vehicle.id == self._vehicle.id:\n continue\n\n # if the object is not in our lane it's not an obstacle\n target_vehicle_waypoint = self._map.get_waypoint(target_vehicle.get_location())\n if target_vehicle_waypoint.road_id != ego_vehicle_waypoint.road_id or \\\n target_vehicle_waypoint.lane_id != ego_vehicle_waypoint.lane_id:\n continue\n\n if is_within_distance_ahead(target_vehicle.get_transform(),\n self._vehicle.get_transform(),\n self._proximity_vehicle_threshold):\n return (True, target_vehicle)\n\n return (False, None)\n\n\n @staticmethod\n def emergency_stop():\n \"\"\"\n Send an emergency stop command to the vehicle\n\n :return: control for braking\n \"\"\"\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 1.0\n control.hand_brake = False\n\n return control\n" }, { "path": "agents/navigation/basic_agent.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module implements an agent that roams around a track following random\nwaypoints and avoiding other vehicles.\nThe agent also responds to traffic lights. \"\"\"\n\n\nimport carla\nfrom agents.navigation.agent import Agent, AgentState\nfrom agents.navigation.local_planner import LocalPlanner\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\n\nclass BasicAgent(Agent):\n \"\"\"\n BasicAgent implements a basic agent that navigates scenes to reach a given\n target destination. This agent respects traffic lights and other vehicles.\n \"\"\"\n\n def __init__(self, vehicle, target_speed=20):\n \"\"\"\n\n :param vehicle: actor to apply to local planner logic onto\n \"\"\"\n super(BasicAgent, self).__init__(vehicle)\n\n self._proximity_tlight_threshold = 5.0 # meters\n self._proximity_vehicle_threshold = 10.0 # meters\n self._state = AgentState.NAVIGATING\n args_lateral_dict = {\n 'K_P': 1,\n 'K_D': 0.4,\n 'K_I': 0,\n 'dt': 1.0/20.0}\n self._local_planner = LocalPlanner(\n self._vehicle, opt_dict={'target_speed' : target_speed,\n 'lateral_control_dict':args_lateral_dict})\n self._hop_resolution = 2.0\n self._path_seperation_hop = 2\n self._path_seperation_threshold = 0.5\n self._target_speed = target_speed\n self._grp = None\n\n def set_destination(self, location):\n \"\"\"\n This method creates a list of waypoints from agent's position to destination location\n based on the route returned by the global router\n \"\"\"\n\n start_waypoint = self._map.get_waypoint(self._vehicle.get_location())\n end_waypoint = self._map.get_waypoint(\n carla.Location(location[0], location[1], location[2]))\n\n route_trace = self._trace_route(start_waypoint, end_waypoint)\n\n self._local_planner.set_global_plan(route_trace)\n\n def _trace_route(self, start_waypoint, end_waypoint):\n \"\"\"\n This method sets up a global router and returns the optimal route\n from start_waypoint to end_waypoint\n \"\"\"\n\n # Setting up global router\n if self._grp is None:\n dao = GlobalRoutePlannerDAO(self._vehicle.get_world().get_map(), self._hop_resolution)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n self._grp = grp\n\n # Obtain route plan\n route = self._grp.trace_route(\n start_waypoint.transform.location,\n end_waypoint.transform.location)\n\n return route\n\n def run_step(self, debug=False):\n \"\"\"\n Execute one step of navigation.\n :return: carla.VehicleControl\n \"\"\"\n\n # is there an obstacle in front of us?\n hazard_detected = False\n\n # retrieve relevant elements for safe navigation, i.e.: traffic lights\n # and other vehicles\n actor_list = self._world.get_actors()\n vehicle_list = actor_list.filter(\"*vehicle*\")\n lights_list = actor_list.filter(\"*traffic_light*\")\n\n # check possible obstacles\n vehicle_state, vehicle = self._is_vehicle_hazard(vehicle_list)\n if vehicle_state:\n if debug:\n print('!!! VEHICLE BLOCKING AHEAD [{}])'.format(vehicle.id))\n\n self._state = AgentState.BLOCKED_BY_VEHICLE\n hazard_detected = True\n\n # check for the state of the traffic lights\n light_state, traffic_light = self._is_light_red(lights_list)\n if light_state:\n if debug:\n print('=== RED LIGHT AHEAD [{}])'.format(traffic_light.id))\n\n self._state = AgentState.BLOCKED_RED_LIGHT\n hazard_detected = True\n\n if hazard_detected:\n control = self.emergency_stop()\n else:\n self._state = AgentState.NAVIGATING\n # standard local planner behavior\n control = self._local_planner.run_step(debug=debug)\n\n return control\n\n def done(self):\n \"\"\"\n Check whether the agent has reached its destination.\n :return bool\n \"\"\"\n return self._local_planner.done()\n" }, { "path": "agents/navigation/behavior_agent.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\n\"\"\" This module implements an agent that roams around a track following random\nwaypoints and avoiding other vehicles. The agent also responds to traffic lights,\ntraffic signs, and has different possible configurations. \"\"\"\n\nimport random\nimport numpy as np\nimport carla\nfrom agents.navigation.agent import Agent\nfrom agents.navigation.local_planner_behavior import LocalPlanner, RoadOption\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\nfrom agents.navigation.types_behavior import Cautious, Aggressive, Normal\n\nfrom agents.tools.misc import get_speed, positive\n\nclass BehaviorAgent(Agent):\n \"\"\"\n BehaviorAgent implements an agent that navigates scenes to reach a given\n target destination, by computing the shortest possible path to it.\n This agent can correctly follow traffic signs, speed limitations,\n traffic lights, while also taking into account nearby vehicles. Lane changing\n decisions can be taken by analyzing the surrounding environment,\n such as overtaking or tailgating avoidance. Adding to these are possible\n behaviors, the agent can also keep safety distance from a car in front of it\n by tracking the instantaneous time to collision and keeping it in a certain range.\n Finally, different sets of behaviors are encoded in the agent, from cautious\n to a more aggressive ones.\n \"\"\"\n\n def __init__(self, vehicle, ignore_traffic_light=False, behavior='normal'):\n \"\"\"\n Constructor method.\n\n :param vehicle: actor to apply to local planner logic onto\n :param ignore_traffic_light: boolean to ignore any traffic light\n :param behavior: type of agent to apply\n \"\"\"\n\n super(BehaviorAgent, self).__init__(vehicle)\n self.vehicle = vehicle\n self.ignore_traffic_light = ignore_traffic_light\n self._local_planner = LocalPlanner(self)\n self._grp = None\n self.look_ahead_steps = 0\n\n # Vehicle information\n self.speed = 0\n self.speed_limit = 0\n self.direction = None\n self.incoming_direction = None\n self.incoming_waypoint = None\n self.start_waypoint = None\n self.end_waypoint = None\n self.is_at_traffic_light = 0\n self.light_state = \"Green\"\n self.light_id_to_ignore = -1\n self.min_speed = 5\n self.behavior = None\n self._sampling_resolution = 4.5\n\n # Parameters for agent behavior\n if behavior == 'cautious':\n self.behavior = Cautious()\n\n elif behavior == 'normal':\n self.behavior = Normal()\n\n elif behavior == 'aggressive':\n self.behavior = Aggressive()\n\n def update_information(self, world):\n \"\"\"\n This method updates the information regarding the ego\n vehicle based on the surrounding world.\n\n :param world: carla.world object\n \"\"\"\n self.speed = get_speed(self.vehicle)\n self.speed_limit = world.player.get_speed_limit()\n self._local_planner.set_speed(self.speed_limit)\n self.direction = self._local_planner.target_road_option\n if self.direction is None:\n self.direction = RoadOption.LANEFOLLOW\n\n self.look_ahead_steps = int((self.speed_limit) / 10)\n\n self.incoming_waypoint, self.incoming_direction = self._local_planner.get_incoming_waypoint_and_direction(\n steps=self.look_ahead_steps)\n if self.incoming_direction is None:\n self.incoming_direction = RoadOption.LANEFOLLOW\n\n self.is_at_traffic_light = world.player.is_at_traffic_light()\n if self.ignore_traffic_light:\n self.light_state = \"Green\"\n else:\n # This method also includes stop signs and intersections.\n self.light_state = str(self.vehicle.get_traffic_light_state())\n\n def set_destination(self, start_location, end_location, clean=False):\n \"\"\"\n This method creates a list of waypoints from agent's position to destination location\n based on the route returned by the global router.\n\n :param start_location: initial position\n :param end_location: final position\n :param clean: boolean to clean the waypoint queue\n \"\"\"\n if clean:\n self._local_planner.waypoints_queue.clear()\n self.start_waypoint = self._map.get_waypoint(start_location)\n self.end_waypoint = self._map.get_waypoint(end_location)\n\n route_trace = self._trace_route(self.start_waypoint, self.end_waypoint)\n\n self._local_planner.set_global_plan(route_trace, clean)\n\n def reroute(self, spawn_points):\n \"\"\"\n This method implements re-routing for vehicles approaching its destination.\n It finds a new target and computes another path to reach it.\n\n :param spawn_points: list of possible destinations for the agent\n \"\"\"\n\n print(\"Target almost reached, setting new destination...\")\n random.shuffle(spawn_points)\n new_start = self._local_planner.waypoints_queue[-1][0].transform.location\n destination = spawn_points[0].location if spawn_points[0].location != new_start else spawn_points[1].location\n print(\"New destination: \" + str(destination))\n\n self.set_destination(new_start, destination)\n\n def _trace_route(self, start_waypoint, end_waypoint):\n \"\"\"\n This method sets up a global router and returns the\n optimal route from start_waypoint to end_waypoint.\n\n :param start_waypoint: initial position\n :param end_waypoint: final position\n \"\"\"\n # Setting up global router\n if self._grp is None:\n wld = self.vehicle.get_world()\n dao = GlobalRoutePlannerDAO(\n wld.get_map(), sampling_resolution=self._sampling_resolution)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n self._grp = grp\n\n # Obtain route plan\n route = self._grp.trace_route(\n start_waypoint.transform.location,\n end_waypoint.transform.location)\n\n return route\n\n def traffic_light_manager(self, waypoint):\n \"\"\"\n This method is in charge of behaviors for red lights and stops.\n\n WARNING: What follows is a proxy to avoid having a car brake after running a yellow light.\n This happens because the car is still under the influence of the semaphore,\n even after passing it. So, the semaphore id is temporarely saved to\n ignore it and go around this issue, until the car is near a new one.\n\n :param waypoint: current waypoint of the agent\n \"\"\"\n\n light_id = self.vehicle.get_traffic_light().id if self.vehicle.get_traffic_light() is not None else -1\n\n if self.light_state == \"Red\":\n if not waypoint.is_junction and (self.light_id_to_ignore != light_id or light_id == -1):\n return 1\n elif waypoint.is_junction and light_id != -1:\n self.light_id_to_ignore = light_id\n if self.light_id_to_ignore != light_id:\n self.light_id_to_ignore = -1\n return 0\n\n def _overtake(self, location, waypoint, vehicle_list):\n \"\"\"\n This method is in charge of overtaking behaviors.\n\n :param location: current location of the agent\n :param waypoint: current waypoint of the agent\n :param vehicle_list: list of all the nearby vehicles\n \"\"\"\n\n left_turn = waypoint.left_lane_marking.lane_change\n right_turn = waypoint.right_lane_marking.lane_change\n\n left_wpt = waypoint.get_left_lane()\n right_wpt = waypoint.get_right_lane()\n\n if (left_turn == carla.LaneChange.Left or left_turn ==\n carla.LaneChange.Both) and waypoint.lane_id * left_wpt.lane_id > 0 and left_wpt.lane_type == carla.LaneType.Driving:\n new_vehicle_state, _, _ = self._bh_is_vehicle_hazard(waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 3), up_angle_th=180, lane_offset=-1)\n if not new_vehicle_state:\n print(\"Overtaking to the left!\")\n self.behavior.overtake_counter = 200\n self.set_destination(left_wpt.transform.location,\n self.end_waypoint.transform.location, clean=True)\n elif right_turn == carla.LaneChange.Right and waypoint.lane_id * right_wpt.lane_id > 0 and right_wpt.lane_type == carla.LaneType.Driving:\n new_vehicle_state, _, _ = self._bh_is_vehicle_hazard(waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 3), up_angle_th=180, lane_offset=1)\n if not new_vehicle_state:\n print(\"Overtaking to the right!\")\n self.behavior.overtake_counter = 200\n self.set_destination(right_wpt.transform.location,\n self.end_waypoint.transform.location, clean=True)\n\n def _tailgating(self, location, waypoint, vehicle_list):\n \"\"\"\n This method is in charge of tailgating behaviors.\n\n :param location: current location of the agent\n :param waypoint: current waypoint of the agent\n :param vehicle_list: list of all the nearby vehicles\n \"\"\"\n\n left_turn = waypoint.left_lane_marking.lane_change\n right_turn = waypoint.right_lane_marking.lane_change\n\n left_wpt = waypoint.get_left_lane()\n right_wpt = waypoint.get_right_lane()\n\n behind_vehicle_state, behind_vehicle, _ = self._bh_is_vehicle_hazard(waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=180, low_angle_th=160)\n if behind_vehicle_state and self.speed < get_speed(behind_vehicle):\n if (right_turn == carla.LaneChange.Right or right_turn ==\n carla.LaneChange.Both) and waypoint.lane_id * right_wpt.lane_id > 0 and right_wpt.lane_type == carla.LaneType.Driving:\n new_vehicle_state, _, _ = self._bh_is_vehicle_hazard(waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=180, lane_offset=1)\n if not new_vehicle_state:\n print(\"Tailgating, moving to the right!\")\n self.behavior.tailgate_counter = 200\n self.set_destination(right_wpt.transform.location,\n self.end_waypoint.transform.location, clean=True)\n elif left_turn == carla.LaneChange.Left and waypoint.lane_id * left_wpt.lane_id > 0 and left_wpt.lane_type == carla.LaneType.Driving:\n new_vehicle_state, _, _ = self._bh_is_vehicle_hazard(waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=180, lane_offset=-1)\n if not new_vehicle_state:\n print(\"Tailgating, moving to the left!\")\n self.behavior.tailgate_counter = 200\n self.set_destination(left_wpt.transform.location,\n self.end_waypoint.transform.location, clean=True)\n\n def collision_and_car_avoid_manager(self, location, waypoint):\n \"\"\"\n This module is in charge of warning in case of a collision\n and managing possible overtaking or tailgating chances.\n\n :param location: current location of the agent\n :param waypoint: current waypoint of the agent\n :return vehicle_state: True if there is a vehicle nearby, False if not\n :return vehicle: nearby vehicle\n :return distance: distance to nearby vehicle\n \"\"\"\n\n vehicle_list = self._world.get_actors().filter(\"*vehicle*\")\n def dist(v): return v.get_location().distance(waypoint.transform.location)\n vehicle_list = [v for v in vehicle_list if dist(v) < 45 and v.id != self.vehicle.id]\n\n if self.direction == RoadOption.CHANGELANELEFT:\n vehicle_state, vehicle, distance = self._bh_is_vehicle_hazard(\n waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=180, lane_offset=-1)\n elif self.direction == RoadOption.CHANGELANERIGHT:\n vehicle_state, vehicle, distance = self._bh_is_vehicle_hazard(\n waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=180, lane_offset=1)\n else:\n vehicle_state, vehicle, distance = self._bh_is_vehicle_hazard(\n waypoint, location, vehicle_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 3), up_angle_th=30)\n\n # Check for overtaking\n\n if vehicle_state and self.direction == RoadOption.LANEFOLLOW and \\\n not waypoint.is_junction and self.speed > 10 \\\n and self.behavior.overtake_counter == 0 and self.speed > get_speed(vehicle):\n self._overtake(location, waypoint, vehicle_list)\n\n # Check for tailgating\n\n elif not vehicle_state and self.direction == RoadOption.LANEFOLLOW \\\n and not waypoint.is_junction and self.speed > 10 \\\n and self.behavior.tailgate_counter == 0:\n self._tailgating(location, waypoint, vehicle_list)\n\n return vehicle_state, vehicle, distance\n\n def pedestrian_avoid_manager(self, location, waypoint):\n \"\"\"\n This module is in charge of warning in case of a collision\n with any pedestrian.\n\n :param location: current location of the agent\n :param waypoint: current waypoint of the agent\n :return vehicle_state: True if there is a walker nearby, False if not\n :return vehicle: nearby walker\n :return distance: distance to nearby walker\n \"\"\"\n\n walker_list = self._world.get_actors().filter(\"*walker.pedestrian*\")\n def dist(w): return w.get_location().distance(waypoint.transform.location)\n walker_list = [w for w in walker_list if dist(w) < 10]\n\n if self.direction == RoadOption.CHANGELANELEFT:\n walker_state, walker, distance = self._bh_is_vehicle_hazard(waypoint, location, walker_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=90, lane_offset=-1)\n elif self.direction == RoadOption.CHANGELANERIGHT:\n walker_state, walker, distance = self._bh_is_vehicle_hazard(waypoint, location, walker_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 2), up_angle_th=90, lane_offset=1)\n else:\n walker_state, walker, distance = self._bh_is_vehicle_hazard(waypoint, location, walker_list, max(\n self.behavior.min_proximity_threshold, self.speed_limit / 3), up_angle_th=60)\n\n return walker_state, walker, distance\n\n def car_following_manager(self, vehicle, distance, debug=False):\n \"\"\"\n Module in charge of car-following behaviors when there's\n someone in front of us.\n\n :param vehicle: car to follow\n :param distance: distance from vehicle\n :param debug: boolean for debugging\n :return control: carla.VehicleControl\n \"\"\"\n\n vehicle_speed = get_speed(vehicle)\n delta_v = max(1, (self.speed - vehicle_speed) / 3.6)\n ttc = distance / delta_v if delta_v != 0 else distance / np.nextafter(0., 1.)\n\n # Under safety time distance, slow down.\n if self.behavior.safety_time > ttc > 0.0:\n control = self._local_planner.run_step(\n target_speed=min(positive(vehicle_speed - self.behavior.speed_decrease),\n min(self.behavior.max_speed, self.speed_limit - self.behavior.speed_lim_dist)), debug=debug)\n # Actual safety distance area, try to follow the speed of the vehicle in front.\n elif 2 * self.behavior.safety_time > ttc >= self.behavior.safety_time:\n control = self._local_planner.run_step(\n target_speed=min(max(self.min_speed, vehicle_speed),\n min(self.behavior.max_speed, self.speed_limit - self.behavior.speed_lim_dist)), debug=debug)\n # Normal behavior.\n else:\n control = self._local_planner.run_step(\n target_speed= min(self.behavior.max_speed, self.speed_limit - self.behavior.speed_lim_dist), debug=debug)\n\n return control\n\n def run_step(self, debug=False):\n \"\"\"\n Execute one step of navigation.\n\n :param debug: boolean for debugging\n :return control: carla.VehicleControl\n \"\"\"\n control = None\n if self.behavior.tailgate_counter > 0:\n self.behavior.tailgate_counter -= 1\n if self.behavior.overtake_counter > 0:\n self.behavior.overtake_counter -= 1\n\n ego_vehicle_loc = self.vehicle.get_location()\n ego_vehicle_wp = self._map.get_waypoint(ego_vehicle_loc)\n\n # 1: Red lights and stops behavior\n\n if self.traffic_light_manager(ego_vehicle_wp) != 0:\n return self.emergency_stop()\n\n # 2.1: Pedestrian avoidancd behaviors\n\n walker_state, walker, w_distance = self.pedestrian_avoid_manager(\n ego_vehicle_loc, ego_vehicle_wp)\n\n if walker_state:\n # Distance is computed from the center of the two cars,\n # we use bounding boxes to calculate the actual distance\n distance = w_distance - max(\n walker.bounding_box.extent.y, walker.bounding_box.extent.x) - max(\n self.vehicle.bounding_box.extent.y, self.vehicle.bounding_box.extent.x)\n\n # Emergency brake if the car is very close.\n if distance < self.behavior.braking_distance:\n return self.emergency_stop()\n\n # 2.2: Car following behaviors\n vehicle_state, vehicle, distance = self.collision_and_car_avoid_manager(\n ego_vehicle_loc, ego_vehicle_wp)\n\n if vehicle_state:\n # Distance is computed from the center of the two cars,\n # we use bounding boxes to calculate the actual distance\n distance = distance - max(\n vehicle.bounding_box.extent.y, vehicle.bounding_box.extent.x) - max(\n self.vehicle.bounding_box.extent.y, self.vehicle.bounding_box.extent.x)\n\n # Emergency brake if the car is very close.\n if distance < self.behavior.braking_distance:\n return self.emergency_stop()\n else:\n control = self.car_following_manager(vehicle, distance)\n\n # 4: Intersection behavior\n\n # Checking if there's a junction nearby to slow down\n elif self.incoming_waypoint.is_junction and (self.incoming_direction == RoadOption.LEFT or self.incoming_direction == RoadOption.RIGHT):\n control = self._local_planner.run_step(\n target_speed=min(self.behavior.max_speed, self.speed_limit - 5), debug=debug)\n\n # 5: Normal behavior\n\n # Calculate controller based on no turn, traffic light or vehicle in front\n else:\n control = self._local_planner.run_step(\n target_speed= min(self.behavior.max_speed, self.speed_limit - self.behavior.speed_lim_dist), debug=debug)\n\n return control\n" }, { "path": "agents/navigation/controller.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module contains PID controllers to perform lateral and longitudinal control. \"\"\"\n\nfrom collections import deque\nimport math\nimport numpy as np\nimport carla\nfrom agents.tools.misc import get_speed\n\n\nclass VehiclePIDController():\n \"\"\"\n VehiclePIDController is the combination of two PID controllers\n (lateral and longitudinal) to perform the\n low level control a vehicle from client side\n \"\"\"\n\n\n def __init__(self, vehicle, args_lateral, args_longitudinal, max_throttle=0.75, max_brake=0.3, max_steering=0.8):\n \"\"\"\n Constructor method.\n\n :param vehicle: actor to apply to local planner logic onto\n :param args_lateral: dictionary of arguments to set the lateral PID controller\n using the following semantics:\n K_P -- Proportional term\n K_D -- Differential term\n K_I -- Integral term\n :param args_longitudinal: dictionary of arguments to set the longitudinal\n PID controller using the following semantics:\n K_P -- Proportional term\n K_D -- Differential term\n K_I -- Integral term\n \"\"\"\n\n self.max_brake = max_brake\n self.max_throt = max_throttle\n self.max_steer = max_steering\n\n self._vehicle = vehicle\n self._world = self._vehicle.get_world()\n self.past_steering = self._vehicle.get_control().steer\n self._lon_controller = PIDLongitudinalController(self._vehicle, **args_longitudinal)\n self._lat_controller = PIDLateralController(self._vehicle, **args_lateral)\n\n def run_step(self, target_speed, waypoint):\n \"\"\"\n Execute one step of control invoking both lateral and longitudinal\n PID controllers to reach a target waypoint\n at a given target_speed.\n\n :param target_speed: desired vehicle speed\n :param waypoint: target location encoded as a waypoint\n :return: distance (in meters) to the waypoint\n \"\"\"\n\n acceleration = self._lon_controller.run_step(target_speed)\n current_steering = self._lat_controller.run_step(waypoint)\n control = carla.VehicleControl()\n if acceleration >= 0.0:\n control.throttle = min(acceleration, self.max_throt)\n control.brake = 0.0\n else:\n control.throttle = 0.0\n control.brake = min(abs(acceleration), self.max_brake)\n\n # Steering regulation: changes cannot happen abruptly, can't steer too much.\n\n if current_steering > self.past_steering + 0.1:\n current_steering = self.past_steering + 0.1\n elif current_steering < self.past_steering - 0.1:\n current_steering = self.past_steering - 0.1\n\n if current_steering >= 0:\n steering = min(self.max_steer, current_steering)\n else:\n steering = max(-self.max_steer, current_steering)\n\n control.steer = steering\n control.hand_brake = False\n control.manual_gear_shift = False\n self.past_steering = steering\n\n return control\n\n\nclass PIDLongitudinalController():\n \"\"\"\n PIDLongitudinalController implements longitudinal control using a PID.\n \"\"\"\n\n\n def __init__(self, vehicle, K_P=1.0, K_D=0.0, K_I=0.0, dt=0.03):\n \"\"\"\n Constructor method.\n\n :param vehicle: actor to apply to local planner logic onto\n :param K_P: Proportional term\n :param K_D: Differential term\n :param K_I: Integral term\n :param dt: time differential in seconds\n \"\"\"\n self._vehicle = vehicle\n self._k_p = K_P\n self._k_d = K_D\n self._k_i = K_I\n self._dt = dt\n self._error_buffer = deque(maxlen=10)\n\n def run_step(self, target_speed, debug=False):\n \"\"\"\n Execute one step of longitudinal control to reach a given target speed.\n\n :param target_speed: target speed in Km/h\n :param debug: boolean for debugging\n :return: throttle control\n \"\"\"\n current_speed = get_speed(self._vehicle)\n\n if debug:\n print('Current speed = {}'.format(current_speed))\n\n return self._pid_control(target_speed, current_speed)\n\n def _pid_control(self, target_speed, current_speed):\n \"\"\"\n Estimate the throttle/brake of the vehicle based on the PID equations\n\n :param target_speed: target speed in Km/h\n :param current_speed: current speed of the vehicle in Km/h\n :return: throttle/brake control\n \"\"\"\n\n error = target_speed - current_speed\n self._error_buffer.append(error)\n\n if len(self._error_buffer) >= 2:\n _de = (self._error_buffer[-1] - self._error_buffer[-2]) / self._dt\n _ie = sum(self._error_buffer) * self._dt\n else:\n _de = 0.0\n _ie = 0.0\n\n return np.clip((self._k_p * error) + (self._k_d * _de) + (self._k_i * _ie), -1.0, 1.0)\n\nclass PIDLateralController():\n \"\"\"\n PIDLateralController implements lateral control using a PID.\n \"\"\"\n\n def __init__(self, vehicle, K_P=1.0, K_D=0.0, K_I=0.0, dt=0.03):\n \"\"\"\n Constructor method.\n\n :param vehicle: actor to apply to local planner logic onto\n :param K_P: Proportional term\n :param K_D: Differential term\n :param K_I: Integral term\n :param dt: time differential in seconds\n \"\"\"\n self._vehicle = vehicle\n self._k_p = K_P\n self._k_d = K_D\n self._k_i = K_I\n self._dt = dt\n self._e_buffer = deque(maxlen=10)\n\n def run_step(self, waypoint):\n \"\"\"\n Execute one step of lateral control to steer\n the vehicle towards a certain waypoin.\n\n :param waypoint: target waypoint\n :return: steering control in the range [-1, 1] where:\n -1 maximum steering to left\n +1 maximum steering to right\n \"\"\"\n return self._pid_control(waypoint, self._vehicle.get_transform())\n\n def _pid_control(self, waypoint, vehicle_transform):\n \"\"\"\n Estimate the steering angle of the vehicle based on the PID equations\n\n :param waypoint: target waypoint\n :param vehicle_transform: current transform of the vehicle\n :return: steering control in the range [-1, 1]\n \"\"\"\n v_begin = vehicle_transform.location\n v_end = v_begin + carla.Location(x=math.cos(math.radians(vehicle_transform.rotation.yaw)),\n y=math.sin(math.radians(vehicle_transform.rotation.yaw)))\n\n v_vec = np.array([v_end.x - v_begin.x, v_end.y - v_begin.y, 0.0])\n w_vec = np.array([waypoint.transform.location.x -\n v_begin.x, waypoint.transform.location.y -\n v_begin.y, 0.0])\n _dot = math.acos(np.clip(np.dot(w_vec, v_vec) /\n (np.linalg.norm(w_vec) * np.linalg.norm(v_vec)), -1.0, 1.0))\n\n _cross = np.cross(v_vec, w_vec)\n\n if _cross[2] < 0:\n _dot *= -1.0\n\n self._e_buffer.append(_dot)\n if len(self._e_buffer) >= 2:\n _de = (self._e_buffer[-1] - self._e_buffer[-2]) / self._dt\n _ie = sum(self._e_buffer) * self._dt\n else:\n _de = 0.0\n _ie = 0.0\n\n return np.clip((self._k_p * _dot) + (self._k_d * _de) + (self._k_i * _ie), -1.0, 1.0)" }, { "path": "agents/navigation/global_route_planner.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\n\"\"\"\nThis module provides GlobalRoutePlanner implementation.\n\"\"\"\n\nimport math\nimport numpy as np\nimport networkx as nx\n\nimport carla\nfrom agents.navigation.local_planner import RoadOption\nfrom agents.tools.misc import vector\n\n\nclass GlobalRoutePlanner(object):\n \"\"\"\n This class provides a very high level route plan.\n Instantiate the class by passing a reference to\n A GlobalRoutePlannerDAO object.\n \"\"\"\n\n def __init__(self, dao):\n \"\"\"\n Constructor\n \"\"\"\n self._dao = dao\n self._topology = None\n self._graph = None\n self._id_map = None\n self._road_id_to_edge = None\n self._intersection_end_node = -1\n self._previous_decision = RoadOption.VOID\n\n def setup(self):\n \"\"\"\n Performs initial server data lookup for detailed topology\n and builds graph representation of the world map.\n \"\"\"\n self._topology = self._dao.get_topology()\n self._graph, self._id_map, self._road_id_to_edge = self._build_graph()\n self._find_loose_ends()\n self._lane_change_link()\n\n def _build_graph(self):\n \"\"\"\n This function builds a networkx graph representation of topology.\n The topology is read from self._topology.\n graph node properties:\n vertex - (x,y,z) position in world map\n graph edge properties:\n entry_vector - unit vector along tangent at entry point\n exit_vector - unit vector along tangent at exit point\n net_vector - unit vector of the chord from entry to exit\n intersection - boolean indicating if the edge belongs to an\n intersection\n return : graph -> networkx graph representing the world map,\n id_map-> mapping from (x,y,z) to node id\n road_id_to_edge-> map from road id to edge in the graph\n \"\"\"\n graph = nx.DiGraph()\n id_map = dict() # Map with structure {(x,y,z): id, ... }\n road_id_to_edge = dict() # Map with structure {road_id: {lane_id: edge, ... }, ... }\n\n for segment in self._topology:\n\n entry_xyz, exit_xyz = segment['entryxyz'], segment['exitxyz']\n path = segment['path']\n entry_wp, exit_wp = segment['entry'], segment['exit']\n intersection = entry_wp.is_junction\n road_id, section_id, lane_id = entry_wp.road_id, entry_wp.section_id, entry_wp.lane_id\n\n for vertex in entry_xyz, exit_xyz:\n # Adding unique nodes and populating id_map\n if vertex not in id_map:\n new_id = len(id_map)\n id_map[vertex] = new_id\n graph.add_node(new_id, vertex=vertex)\n n1 = id_map[entry_xyz]\n n2 = id_map[exit_xyz]\n if road_id not in road_id_to_edge:\n road_id_to_edge[road_id] = dict()\n if section_id not in road_id_to_edge[road_id]:\n road_id_to_edge[road_id][section_id] = dict()\n road_id_to_edge[road_id][section_id][lane_id] = (n1, n2)\n\n entry_carla_vector = entry_wp.transform.rotation.get_forward_vector()\n exit_carla_vector = exit_wp.transform.rotation.get_forward_vector()\n\n # Adding edge with attributes\n graph.add_edge(\n n1, n2,\n length=len(path) + 1, path=path,\n entry_waypoint=entry_wp, exit_waypoint=exit_wp,\n entry_vector=np.array(\n [entry_carla_vector.x, entry_carla_vector.y, entry_carla_vector.z]),\n exit_vector=np.array(\n [exit_carla_vector.x, exit_carla_vector.y, exit_carla_vector.z]),\n net_vector=vector(entry_wp.transform.location, exit_wp.transform.location),\n intersection=intersection, type=RoadOption.LANEFOLLOW)\n\n return graph, id_map, road_id_to_edge\n\n def _find_loose_ends(self):\n \"\"\"\n This method finds road segments that have an unconnected end, and\n adds them to the internal graph representation\n \"\"\"\n count_loose_ends = 0\n hop_resolution = self._dao.get_resolution()\n for segment in self._topology:\n end_wp = segment['exit']\n exit_xyz = segment['exitxyz']\n road_id, section_id, lane_id = end_wp.road_id, end_wp.section_id, end_wp.lane_id\n if road_id in self._road_id_to_edge and section_id in self._road_id_to_edge[road_id] and lane_id in self._road_id_to_edge[road_id][section_id]:\n pass\n else:\n count_loose_ends += 1\n if road_id not in self._road_id_to_edge:\n self._road_id_to_edge[road_id] = dict()\n if section_id not in self._road_id_to_edge[road_id]:\n self._road_id_to_edge[road_id][section_id] = dict()\n n1 = self._id_map[exit_xyz]\n n2 = -1*count_loose_ends\n self._road_id_to_edge[road_id][section_id][lane_id] = (n1, n2)\n next_wp = end_wp.next(hop_resolution)\n path = []\n while next_wp is not None and next_wp and next_wp[0].road_id == road_id and next_wp[0].section_id == section_id and next_wp[0].lane_id == lane_id:\n path.append(next_wp[0])\n next_wp = next_wp[0].next(hop_resolution)\n if path:\n n2_xyz = (path[-1].transform.location.x,\n path[-1].transform.location.y,\n path[-1].transform.location.z)\n self._graph.add_node(n2, vertex=n2_xyz)\n self._graph.add_edge(\n n1, n2,\n length=len(path) + 1, path=path,\n entry_waypoint=end_wp, exit_waypoint=path[-1],\n entry_vector=None, exit_vector=None, net_vector=None,\n intersection=end_wp.is_junction, type=RoadOption.LANEFOLLOW)\n\n def _localize(self, location):\n \"\"\"\n This function finds the road segment closest to given location\n location : carla.Location to be localized in the graph\n return : pair node ids representing an edge in the graph\n \"\"\"\n waypoint = self._dao.get_waypoint(location)\n edge = None\n try:\n edge = self._road_id_to_edge[waypoint.road_id][waypoint.section_id][waypoint.lane_id]\n except KeyError:\n print(\n \"Failed to localize! : \",\n \"Road id : \", waypoint.road_id,\n \"Section id : \", waypoint.section_id,\n \"Lane id : \", waypoint.lane_id,\n \"Location : \", waypoint.transform.location.x,\n waypoint.transform.location.y)\n return edge\n\n def _lane_change_link(self):\n \"\"\"\n This method places zero cost links in the topology graph\n representing availability of lane changes.\n \"\"\"\n\n for segment in self._topology:\n left_found, right_found = False, False\n\n for waypoint in segment['path']:\n if not segment['entry'].is_junction:\n next_waypoint, next_road_option, next_segment = None, None, None\n\n if waypoint.right_lane_marking.lane_change & carla.LaneChange.Right and not right_found:\n next_waypoint = waypoint.get_right_lane()\n if next_waypoint is not None and next_waypoint.lane_type == carla.LaneType.Driving and waypoint.road_id == next_waypoint.road_id:\n next_road_option = RoadOption.CHANGELANERIGHT\n next_segment = self._localize(next_waypoint.transform.location)\n if next_segment is not None:\n self._graph.add_edge(\n self._id_map[segment['entryxyz']], next_segment[0], entry_waypoint=waypoint,\n exit_waypoint=next_waypoint, intersection=False, exit_vector=None,\n path=[], length=0, type=next_road_option, change_waypoint=next_waypoint)\n right_found = True\n if waypoint.left_lane_marking.lane_change & carla.LaneChange.Left and not left_found:\n next_waypoint = waypoint.get_left_lane()\n if next_waypoint is not None and next_waypoint.lane_type == carla.LaneType.Driving and waypoint.road_id == next_waypoint.road_id:\n next_road_option = RoadOption.CHANGELANELEFT\n next_segment = self._localize(next_waypoint.transform.location)\n if next_segment is not None:\n self._graph.add_edge(\n self._id_map[segment['entryxyz']], next_segment[0], entry_waypoint=waypoint,\n exit_waypoint=next_waypoint, intersection=False, exit_vector=None,\n path=[], length=0, type=next_road_option, change_waypoint=next_waypoint)\n left_found = True\n if left_found and right_found:\n break\n\n def _distance_heuristic(self, n1, n2):\n \"\"\"\n Distance heuristic calculator for path searching\n in self._graph\n \"\"\"\n l1 = np.array(self._graph.nodes[n1]['vertex'])\n l2 = np.array(self._graph.nodes[n2]['vertex'])\n return np.linalg.norm(l1-l2)\n\n def _path_search(self, origin, destination):\n \"\"\"\n This function finds the shortest path connecting origin and destination\n using A* search with distance heuristic.\n origin : carla.Location object of start position\n destination : carla.Location object of of end position\n return : path as list of node ids (as int) of the graph self._graph\n connecting origin and destination\n \"\"\"\n\n start, end = self._localize(origin), self._localize(destination)\n\n route = nx.astar_path(\n self._graph, source=start[0], target=end[0],\n heuristic=self._distance_heuristic, weight='length')\n route.append(end[1])\n return route\n\n def _successive_last_intersection_edge(self, index, route):\n \"\"\"\n This method returns the last successive intersection edge\n from a starting index on the route.\n This helps moving past tiny intersection edges to calculate\n proper turn decisions.\n \"\"\"\n\n last_intersection_edge = None\n last_node = None\n for node1, node2 in [(route[i], route[i+1]) for i in range(index, len(route)-1)]:\n candidate_edge = self._graph.edges[node1, node2]\n if node1 == route[index]:\n last_intersection_edge = candidate_edge\n if candidate_edge['type'] == RoadOption.LANEFOLLOW and candidate_edge['intersection']:\n last_intersection_edge = candidate_edge\n last_node = node2\n else:\n break\n\n return last_node, last_intersection_edge\n\n def _turn_decision(self, index, route, threshold=math.radians(35)):\n \"\"\"\n This method returns the turn decision (RoadOption) for pair of edges\n around current index of route list\n \"\"\"\n\n decision = None\n previous_node = route[index-1]\n current_node = route[index]\n next_node = route[index+1]\n next_edge = self._graph.edges[current_node, next_node]\n if index > 0:\n if self._previous_decision != RoadOption.VOID and self._intersection_end_node > 0 and self._intersection_end_node != previous_node and next_edge['type'] == RoadOption.LANEFOLLOW and next_edge['intersection']:\n decision = self._previous_decision\n else:\n self._intersection_end_node = -1\n current_edge = self._graph.edges[previous_node, current_node]\n calculate_turn = current_edge['type'] == RoadOption.LANEFOLLOW and not current_edge[\n 'intersection'] and next_edge['type'] == RoadOption.LANEFOLLOW and next_edge['intersection']\n if calculate_turn:\n last_node, tail_edge = self._successive_last_intersection_edge(index, route)\n self._intersection_end_node = last_node\n if tail_edge is not None:\n next_edge = tail_edge\n cv, nv = current_edge['exit_vector'], next_edge['exit_vector']\n if cv is None or nv is None:\n return next_edge['type']\n cross_list = []\n for neighbor in self._graph.successors(current_node):\n select_edge = self._graph.edges[current_node, neighbor]\n if select_edge['type'] == RoadOption.LANEFOLLOW:\n if neighbor != route[index+1]:\n sv = select_edge['net_vector']\n cross_list.append(np.cross(cv, sv)[2])\n next_cross = np.cross(cv, nv)[2]\n deviation = math.acos(np.clip(\n np.dot(cv, nv)/(np.linalg.norm(cv)*np.linalg.norm(nv)), -1.0, 1.0))\n if not cross_list:\n cross_list.append(0)\n if deviation < threshold:\n decision = RoadOption.STRAIGHT\n elif cross_list and next_cross < min(cross_list):\n decision = RoadOption.LEFT\n elif cross_list and next_cross > max(cross_list):\n decision = RoadOption.RIGHT\n elif next_cross < 0:\n decision = RoadOption.LEFT\n elif next_cross > 0:\n decision = RoadOption.RIGHT\n else:\n decision = next_edge['type']\n\n else:\n decision = next_edge['type']\n\n self._previous_decision = decision\n return decision\n\n def abstract_route_plan(self, origin, destination):\n \"\"\"\n The following function generates the route plan based on\n origin : carla.Location object of the route's start position\n destination : carla.Location object of the route's end position\n return : list of turn by turn navigation decisions as\n agents.navigation.local_planner.RoadOption elements\n Possible values are STRAIGHT, LEFT, RIGHT, LANEFOLLOW, VOID\n CHANGELANELEFT, CHANGELANERIGHT\n \"\"\"\n\n route = self._path_search(origin, destination)\n plan = []\n\n for i in range(len(route) - 1):\n road_option = self._turn_decision(i, route)\n plan.append(road_option)\n\n return plan\n\n def _find_closest_in_list(self, current_waypoint, waypoint_list):\n min_distance = float('inf')\n closest_index = -1\n for i, waypoint in enumerate(waypoint_list):\n distance = waypoint.transform.location.distance(\n current_waypoint.transform.location)\n if distance < min_distance:\n min_distance = distance\n closest_index = i\n\n return closest_index\n\n def trace_route(self, origin, destination):\n \"\"\"\n This method returns list of (carla.Waypoint, RoadOption)\n from origin to destination\n \"\"\"\n\n route_trace = []\n route = self._path_search(origin, destination)\n current_waypoint = self._dao.get_waypoint(origin)\n destination_waypoint = self._dao.get_waypoint(destination)\n resolution = self._dao.get_resolution()\n\n for i in range(len(route) - 1):\n road_option = self._turn_decision(i, route)\n edge = self._graph.edges[route[i], route[i+1]]\n path = []\n\n if edge['type'] != RoadOption.LANEFOLLOW and edge['type'] != RoadOption.VOID:\n route_trace.append((current_waypoint, road_option))\n exit_wp = edge['exit_waypoint']\n n1, n2 = self._road_id_to_edge[exit_wp.road_id][exit_wp.section_id][exit_wp.lane_id]\n next_edge = self._graph.edges[n1, n2]\n if next_edge['path']:\n closest_index = self._find_closest_in_list(current_waypoint, next_edge['path'])\n closest_index = min(len(next_edge['path'])-1, closest_index+5)\n current_waypoint = next_edge['path'][closest_index]\n else:\n current_waypoint = next_edge['exit_waypoint']\n route_trace.append((current_waypoint, road_option))\n\n else:\n path = path + [edge['entry_waypoint']] + edge['path'] + [edge['exit_waypoint']]\n closest_index = self._find_closest_in_list(current_waypoint, path)\n for waypoint in path[closest_index:]:\n current_waypoint = waypoint\n route_trace.append((current_waypoint, road_option))\n if len(route)-i <= 2 and waypoint.transform.location.distance(destination) < 2*resolution:\n break\n elif len(route)-i <= 2 and current_waypoint.road_id == destination_waypoint.road_id and current_waypoint.section_id == destination_waypoint.section_id and current_waypoint.lane_id == destination_waypoint.lane_id:\n destination_index = self._find_closest_in_list(destination_waypoint, path)\n if closest_index > destination_index:\n break\n\n return route_trace\n" }, { "path": "agents/navigation/global_route_planner_dao.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides implementation for GlobalRoutePlannerDAO\n\"\"\"\n\nimport numpy as np\n\n\nclass GlobalRoutePlannerDAO(object):\n \"\"\"\n This class is the data access layer for fetching data\n from the carla server instance for GlobalRoutePlanner\n \"\"\"\n\n def __init__(self, wmap, sampling_resolution):\n \"\"\"\n Constructor method.\n\n :param wmap: carla.world object\n :param sampling_resolution: sampling distance between waypoints\n \"\"\"\n self._sampling_resolution = sampling_resolution\n self._wmap = wmap\n\n def get_topology(self):\n \"\"\"\n Accessor for topology.\n This function retrieves topology from the server as a list of\n road segments as pairs of waypoint objects, and processes the\n topology into a list of dictionary objects.\n\n :return topology: list of dictionary objects with the following attributes\n entry - waypoint of entry point of road segment\n entryxyz- (x,y,z) of entry point of road segment\n exit - waypoint of exit point of road segment\n exitxyz - (x,y,z) of exit point of road segment\n path - list of waypoints separated by 1m from entry\n to exit\n \"\"\"\n topology = []\n # Retrieving waypoints to construct a detailed topology\n for segment in self._wmap.get_topology():\n wp1, wp2 = segment[0], segment[1]\n l1, l2 = wp1.transform.location, wp2.transform.location\n # Rounding off to avoid floating point imprecision\n x1, y1, z1, x2, y2, z2 = np.round([l1.x, l1.y, l1.z, l2.x, l2.y, l2.z], 0)\n wp1.transform.location, wp2.transform.location = l1, l2\n seg_dict = dict()\n seg_dict['entry'], seg_dict['exit'] = wp1, wp2\n seg_dict['entryxyz'], seg_dict['exitxyz'] = (x1, y1, z1), (x2, y2, z2)\n seg_dict['path'] = []\n endloc = wp2.transform.location\n if wp1.transform.location.distance(endloc) > self._sampling_resolution:\n w = wp1.next(self._sampling_resolution)[0]\n while w.transform.location.distance(endloc) > self._sampling_resolution:\n seg_dict['path'].append(w)\n w = w.next(self._sampling_resolution)[0]\n else:\n seg_dict['path'].append(wp1.next(self._sampling_resolution)[0])\n topology.append(seg_dict)\n return topology\n\n def get_waypoint(self, location):\n \"\"\"\n The method returns waypoint at given location\n\n :param location: vehicle location\n :return waypoint: generated waypoint close to location\n \"\"\"\n waypoint = self._wmap.get_waypoint(location)\n return waypoint\n\n def get_resolution(self):\n \"\"\" Accessor for self._sampling_resolution \"\"\"\n return self._sampling_resolution\n" }, { "path": "agents/navigation/local_planner.py", "content": "# Copyright (c) # Copyright (c) 2018-2020 CVC.\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module contains a local planner to perform low-level waypoint following based on PID controllers. \"\"\"\n\nfrom enum import Enum\nfrom collections import deque\nimport random\n\nimport carla\nfrom agents.navigation.controller import VehiclePIDController\nfrom agents.tools.misc import draw_waypoints\n\n\nclass RoadOption(Enum):\n \"\"\"\n RoadOption represents the possible topological configurations when moving from a segment of lane to other.\n \"\"\"\n VOID = -1\n LEFT = 1\n RIGHT = 2\n STRAIGHT = 3\n LANEFOLLOW = 4\n CHANGELANELEFT = 5\n CHANGELANERIGHT = 6\n\n\nclass LocalPlanner(object):\n \"\"\"\n LocalPlanner implements the basic behavior of following a trajectory of waypoints that is generated on-the-fly.\n The low-level motion of the vehicle is computed by using two PID controllers, one is used for the lateral control\n and the other for the longitudinal control (cruise speed).\n\n When multiple paths are available (intersections) this local planner makes a random choice.\n \"\"\"\n\n # minimum distance to target waypoint as a percentage (e.g. within 90% of\n # total distance)\n MIN_DISTANCE_PERCENTAGE = 0.9\n\n def __init__(self, vehicle, opt_dict=None):\n \"\"\"\n :param vehicle: actor to apply to local planner logic onto\n :param opt_dict: dictionary of arguments with the following semantics:\n dt -- time difference between physics control in seconds. This is typically fixed from server side\n using the arguments -benchmark -fps=F . In this case dt = 1/F\n\n target_speed -- desired cruise speed in Km/h\n\n sampling_radius -- search radius for next waypoints in seconds: e.g. 0.5 seconds ahead\n\n lateral_control_dict -- dictionary of arguments to setup the lateral PID controller\n {'K_P':, 'K_D':, 'K_I':, 'dt'}\n\n longitudinal_control_dict -- dictionary of arguments to setup the longitudinal PID controller\n {'K_P':, 'K_D':, 'K_I':, 'dt'}\n \"\"\"\n self._vehicle = vehicle\n self._map = self._vehicle.get_world().get_map()\n\n self._dt = None\n self._target_speed = None\n self._sampling_radius = None\n self._min_distance = None\n self._current_waypoint = None\n self._target_road_option = None\n self._next_waypoints = None\n self.target_waypoint = None\n self._vehicle_controller = None\n self._global_plan = None\n # queue with tuples of (waypoint, RoadOption)\n self._waypoints_queue = deque(maxlen=20000)\n self._buffer_size = 5\n self._waypoint_buffer = deque(maxlen=self._buffer_size)\n\n # initializing controller\n self._init_controller(opt_dict)\n\n def __del__(self):\n if self._vehicle:\n self._vehicle.destroy()\n print(\"Destroying ego-vehicle!\")\n\n def reset_vehicle(self):\n self._vehicle = None\n print(\"Resetting ego-vehicle!\")\n\n def _init_controller(self, opt_dict):\n \"\"\"\n Controller initialization.\n\n :param opt_dict: dictionary of arguments.\n :return:\n \"\"\"\n # default params\n self._dt = 1.0 / 20.0\n self._target_speed = 20.0 # Km/h\n self._sampling_radius = self._target_speed * 1 / 3.6 # 1 seconds horizon\n self._min_distance = self._sampling_radius * self.MIN_DISTANCE_PERCENTAGE\n self._max_brake = 0.3\n self._max_throt = 0.75\n self._max_steer = 0.8\n args_lateral_dict = {\n 'K_P': 1.95,\n 'K_D': 0.2,\n 'K_I': 0.07,\n 'dt': self._dt}\n args_longitudinal_dict = {\n 'K_P': 1.0,\n 'K_D': 0,\n 'K_I': 0.05,\n 'dt': self._dt}\n\n # parameters overload\n if opt_dict:\n if 'dt' in opt_dict:\n self._dt = opt_dict['dt']\n if 'target_speed' in opt_dict:\n self._target_speed = opt_dict['target_speed']\n if 'sampling_radius' in opt_dict:\n self._sampling_radius = self._target_speed * \\\n opt_dict['sampling_radius'] / 3.6\n if 'lateral_control_dict' in opt_dict:\n args_lateral_dict = opt_dict['lateral_control_dict']\n if 'longitudinal_control_dict' in opt_dict:\n args_longitudinal_dict = opt_dict['longitudinal_control_dict']\n if 'max_throttle' in opt_dict:\n self._max_throt = opt_dict['max_throttle']\n if 'max_brake' in opt_dict:\n self._max_brake = opt_dict['max_brake']\n if 'max_steering' in opt_dict:\n self._max_steer = opt_dict['max_steering']\n\n self._current_waypoint = self._map.get_waypoint(self._vehicle.get_location())\n self._vehicle_controller = VehiclePIDController(self._vehicle,\n args_lateral=args_lateral_dict,\n args_longitudinal=args_longitudinal_dict,\n max_throttle=self._max_throt,\n max_brake=self._max_brake,\n max_steering=self._max_steer,)\n\n self._global_plan = False\n\n # compute initial waypoints\n self._waypoints_queue.append((self._current_waypoint.next(self._sampling_radius)[0], RoadOption.LANEFOLLOW))\n\n self._target_road_option = RoadOption.LANEFOLLOW\n # fill waypoint trajectory queue\n self._compute_next_waypoints(k=200)\n\n def set_speed(self, speed):\n \"\"\"\n Request new target speed.\n\n :param speed: new target speed in Km/h\n :return:\n \"\"\"\n self._target_speed = speed\n\n def _compute_next_waypoints(self, k=1):\n \"\"\"\n Add new waypoints to the trajectory queue.\n\n :param k: how many waypoints to compute\n :return:\n \"\"\"\n # check we do not overflow the queue\n available_entries = self._waypoints_queue.maxlen - len(self._waypoints_queue)\n k = min(available_entries, k)\n\n for _ in range(k):\n last_waypoint = self._waypoints_queue[-1][0]\n next_waypoints = list(last_waypoint.next(self._sampling_radius))\n\n if len(next_waypoints) == 0:\n break\n elif len(next_waypoints) == 1:\n # only one option available ==> lanefollowing\n next_waypoint = next_waypoints[0]\n road_option = RoadOption.LANEFOLLOW\n else:\n # random choice between the possible options\n road_options_list = _retrieve_options(\n next_waypoints, last_waypoint)\n road_option = random.choice(road_options_list)\n next_waypoint = next_waypoints[road_options_list.index(\n road_option)]\n\n self._waypoints_queue.append((next_waypoint, road_option))\n\n def set_global_plan(self, current_plan):\n \"\"\"\n Resets the waypoint queue and buffer to match the new plan. Also\n sets the global_plan flag to avoid creating more waypoints\n\n :param current_plan: list of (carla.Waypoint, RoadOption)\n :return:\n \"\"\"\n\n # Reset the queue\n self._waypoints_queue.clear()\n for elem in current_plan:\n self._waypoints_queue.append(elem)\n self._target_road_option = RoadOption.LANEFOLLOW\n\n # and the buffer\n self._waypoint_buffer.clear()\n for _ in range(self._buffer_size):\n if self._waypoints_queue:\n self._waypoint_buffer.append(\n self._waypoints_queue.popleft())\n else:\n break\n\n self._global_plan = True\n\n def run_step(self, debug=False):\n \"\"\"\n Execute one step of local planning which involves running the longitudinal and lateral PID controllers to\n follow the waypoints trajectory.\n\n :param debug: boolean flag to activate waypoints debugging\n :return: control to be applied\n \"\"\"\n\n # not enough waypoints in the horizon? => add more!\n if not self._global_plan and len(self._waypoints_queue) < int(self._waypoints_queue.maxlen * 0.5):\n self._compute_next_waypoints(k=100)\n\n if len(self._waypoints_queue) == 0 and len(self._waypoint_buffer) == 0:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 1.0\n control.hand_brake = False\n control.manual_gear_shift = False\n\n return control\n\n # Buffering the waypoints\n if not self._waypoint_buffer:\n for _ in range(self._buffer_size):\n if self._waypoints_queue:\n self._waypoint_buffer.append(\n self._waypoints_queue.popleft())\n else:\n break\n\n # current vehicle waypoint\n vehicle_transform = self._vehicle.get_transform()\n self._current_waypoint = self._map.get_waypoint(vehicle_transform.location)\n # target waypoint\n self.target_waypoint, self._target_road_option = self._waypoint_buffer[0]\n # move using PID controllers\n control = self._vehicle_controller.run_step(self._target_speed, self.target_waypoint)\n\n # purge the queue of obsolete waypoints\n max_index = -1\n\n for i, (waypoint, _) in enumerate(self._waypoint_buffer):\n if waypoint.transform.location.distance(vehicle_transform.location) < self._min_distance:\n max_index = i\n if max_index >= 0:\n for i in range(max_index + 1):\n self._waypoint_buffer.popleft()\n\n if debug:\n draw_waypoints(self._vehicle.get_world(), [self.target_waypoint], self._vehicle.get_location().z + 1.0)\n\n return control\n\n def done(self):\n \"\"\"\n Returns whether or not the planner has finished\n\n :return: boolean\n \"\"\"\n return len(self._waypoints_queue) == 0 and len(self._waypoint_buffer) == 0\n\ndef _retrieve_options(list_waypoints, current_waypoint):\n \"\"\"\n Compute the type of connection between the current active waypoint and the multiple waypoints present in\n list_waypoints. The result is encoded as a list of RoadOption enums.\n\n :param list_waypoints: list with the possible target waypoints in case of multiple options\n :param current_waypoint: current active waypoint\n :return: list of RoadOption enums representing the type of connection from the active waypoint to each\n candidate in list_waypoints\n \"\"\"\n options = []\n for next_waypoint in list_waypoints:\n # this is needed because something we are linking to\n # the beggining of an intersection, therefore the\n # variation in angle is small\n next_next_waypoint = next_waypoint.next(3.0)[0]\n link = _compute_connection(current_waypoint, next_next_waypoint)\n options.append(link)\n\n return options\n\n\ndef _compute_connection(current_waypoint, next_waypoint, threshold=35):\n \"\"\"\n Compute the type of topological connection between an active waypoint (current_waypoint) and a target waypoint\n (next_waypoint).\n\n :param current_waypoint: active waypoint\n :param next_waypoint: target waypoint\n :return: the type of topological connection encoded as a RoadOption enum:\n RoadOption.STRAIGHT\n RoadOption.LEFT\n RoadOption.RIGHT\n \"\"\"\n n = next_waypoint.transform.rotation.yaw\n n = n % 360.0\n\n c = current_waypoint.transform.rotation.yaw\n c = c % 360.0\n\n diff_angle = (n - c) % 180.0\n if diff_angle < threshold or diff_angle > (180 - threshold):\n return RoadOption.STRAIGHT\n elif diff_angle > 90.0:\n return RoadOption.LEFT\n else:\n return RoadOption.RIGHT\n" }, { "path": "agents/navigation/local_planner_behavior.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018 Intel Labs.\n# authors: German Ros (german.ros@intel.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module contains a local planner to perform\nlow-level waypoint following based on PID controllers. \"\"\"\n\nfrom collections import deque\nfrom enum import Enum\n\nimport carla\nfrom agents.navigation.controller import VehiclePIDController\nfrom agents.tools.misc import distance_vehicle, draw_waypoints\n\n\nclass RoadOption(Enum):\n \"\"\"\n RoadOption represents the possible topological configurations\n when moving from a segment of lane to other.\n \"\"\"\n VOID = -1\n LEFT = 1\n RIGHT = 2\n STRAIGHT = 3\n LANEFOLLOW = 4\n CHANGELANELEFT = 5\n CHANGELANERIGHT = 6\n\n\nclass LocalPlanner(object):\n \"\"\"\n LocalPlanner implements the basic behavior of following a trajectory\n of waypoints that is generated on-the-fly.\n The low-level motion of the vehicle is computed by using two PID controllers,\n one is used for the lateral control\n and the other for the longitudinal control (cruise speed).\n\n When multiple paths are available (intersections)\n this local planner makes a random choice.\n \"\"\"\n\n # Minimum distance to target waypoint as a percentage\n # (e.g. within 80% of total distance)\n\n # FPS used for dt\n FPS = 20\n\n def __init__(self, agent):\n \"\"\"\n :param agent: agent that regulates the vehicle\n :param vehicle: actor to apply to local planner logic onto\n \"\"\"\n self._vehicle = agent.vehicle\n self._map = agent.vehicle.get_world().get_map()\n\n self._target_speed = None\n self.sampling_radius = None\n self._min_distance = None\n self._current_waypoint = None\n self.target_road_option = None\n self._next_waypoints = None\n self.target_waypoint = None\n self._vehicle_controller = None\n self._global_plan = None\n self._pid_controller = None\n self.waypoints_queue = deque(maxlen=20000) # queue with tuples of (waypoint, RoadOption)\n self._buffer_size = 5\n self._waypoint_buffer = deque(maxlen=self._buffer_size)\n\n self._init_controller() # initializing controller\n\n def reset_vehicle(self):\n \"\"\"Reset the ego-vehicle\"\"\"\n self._vehicle = None\n print(\"Resetting ego-vehicle!\")\n\n def _init_controller(self):\n \"\"\"\n Controller initialization.\n\n dt -- time difference between physics control in seconds.\n This is can be fixed from server side\n using the arguments -benchmark -fps=F, since dt = 1/F\n\n target_speed -- desired cruise speed in km/h\n\n min_distance -- minimum distance to remove waypoint from queue\n\n lateral_dict -- dictionary of arguments to setup the lateral PID controller\n {'K_P':, 'K_D':, 'K_I':, 'dt'}\n\n longitudinal_dict -- dictionary of arguments to setup the longitudinal PID controller\n {'K_P':, 'K_D':, 'K_I':, 'dt'}\n \"\"\"\n # Default parameters\n self.args_lat_hw_dict = {\n 'K_P': 0.75,\n 'K_D': 0.02,\n 'K_I': 0.4,\n 'dt': 1.0 / self.FPS}\n self.args_lat_city_dict = {\n 'K_P': 0.58,\n 'K_D': 0.02,\n 'K_I': 0.5,\n 'dt': 1.0 / self.FPS}\n self.args_long_hw_dict = {\n 'K_P': 0.37,\n 'K_D': 0.024,\n 'K_I': 0.032,\n 'dt': 1.0 / self.FPS}\n self.args_long_city_dict = {\n 'K_P': 0.15,\n 'K_D': 0.05,\n 'K_I': 0.07,\n 'dt': 1.0 / self.FPS}\n\n self._current_waypoint = self._map.get_waypoint(self._vehicle.get_location())\n\n self._global_plan = False\n\n self._target_speed = self._vehicle.get_speed_limit()\n\n self._min_distance = 3\n\n def set_speed(self, speed):\n \"\"\"\n Request new target speed.\n\n :param speed: new target speed in km/h\n \"\"\"\n\n self._target_speed = speed\n\n def set_global_plan(self, current_plan, clean=False):\n \"\"\"\n Sets new global plan.\n\n :param current_plan: list of waypoints in the actual plan\n \"\"\"\n for elem in current_plan:\n self.waypoints_queue.append(elem)\n\n if clean:\n self._waypoint_buffer.clear()\n for _ in range(self._buffer_size):\n if self.waypoints_queue:\n self._waypoint_buffer.append(\n self.waypoints_queue.popleft())\n else:\n break\n\n self._global_plan = True\n\n def get_incoming_waypoint_and_direction(self, steps=3):\n \"\"\"\n Returns direction and waypoint at a distance ahead defined by the user.\n\n :param steps: number of steps to get the incoming waypoint.\n \"\"\"\n if len(self.waypoints_queue) > steps:\n return self.waypoints_queue[steps]\n\n else:\n try:\n wpt, direction = self.waypoints_queue[-1]\n return wpt, direction\n except IndexError as i:\n print(i)\n return None, RoadOption.VOID\n return None, RoadOption.VOID\n\n def run_step(self, target_speed=None, debug=False):\n \"\"\"\n Execute one step of local planning which involves\n running the longitudinal and lateral PID controllers to\n follow the waypoints trajectory.\n\n :param target_speed: desired speed\n :param debug: boolean flag to activate waypoints debugging\n :return: control\n \"\"\"\n\n if target_speed is not None:\n self._target_speed = target_speed\n else:\n self._target_speed = self._vehicle.get_speed_limit()\n\n if len(self.waypoints_queue) == 0:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 1.0\n control.hand_brake = False\n control.manual_gear_shift = False\n return control\n\n # Buffering the waypoints\n if not self._waypoint_buffer:\n for i in range(self._buffer_size):\n if self.waypoints_queue:\n self._waypoint_buffer.append(\n self.waypoints_queue.popleft())\n else:\n break\n\n # Current vehicle waypoint\n self._current_waypoint = self._map.get_waypoint(self._vehicle.get_location())\n\n # Target waypoint\n self.target_waypoint, self.target_road_option = self._waypoint_buffer[0]\n\n if target_speed > 50:\n args_lat = self.args_lat_hw_dict\n args_long = self.args_long_hw_dict\n else:\n args_lat = self.args_lat_city_dict\n args_long = self.args_long_city_dict\n\n self._pid_controller = VehiclePIDController(self._vehicle,\n args_lateral=args_lat,\n args_longitudinal=args_long)\n\n control = self._pid_controller.run_step(self._target_speed, self.target_waypoint)\n\n # Purge the queue of obsolete waypoints\n vehicle_transform = self._vehicle.get_transform()\n max_index = -1\n\n for i, (waypoint, _) in enumerate(self._waypoint_buffer):\n if distance_vehicle(\n waypoint, vehicle_transform) < self._min_distance:\n max_index = i\n if max_index >= 0:\n for i in range(max_index + 1):\n self._waypoint_buffer.popleft()\n\n if debug:\n draw_waypoints(self._vehicle.get_world(),\n [self.target_waypoint], 1.0)\n return control\n" }, { "path": "agents/navigation/roaming_agent.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018 Intel Labs.\n# authors: German Ros (german.ros@intel.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module implements an agent that roams around a track following random waypoints and avoiding other vehicles.\nThe agent also responds to traffic lights. \"\"\"\n\nfrom agents.navigation.agent import Agent, AgentState\nfrom agents.navigation.local_planner import LocalPlanner\n\n\nclass RoamingAgent(Agent):\n \"\"\"\n RoamingAgent implements a basic agent that navigates scenes making random\n choices when facing an intersection.\n\n This agent respects traffic lights and other vehicles.\n \"\"\"\n\n def __init__(self, vehicle):\n \"\"\"\n\n :param vehicle: actor to apply to local planner logic onto\n \"\"\"\n super(RoamingAgent, self).__init__(vehicle)\n self._proximity_threshold = 10.0 # meters\n self._state = AgentState.NAVIGATING\n self._local_planner = LocalPlanner(self._vehicle)\n\n def run_step(self, debug=False):\n \"\"\"\n Execute one step of navigation.\n :return: carla.VehicleControl\n \"\"\"\n\n # is there an obstacle in front of us?\n hazard_detected = False\n\n # retrieve relevant elements for safe navigation, i.e.: traffic lights\n # and other vehicles\n actor_list = self._world.get_actors()\n vehicle_list = actor_list.filter(\"*vehicle*\")\n lights_list = actor_list.filter(\"*traffic_light*\")\n\n # check possible obstacles\n vehicle_state, vehicle = self._is_vehicle_hazard(vehicle_list)\n if vehicle_state:\n if debug:\n print('!!! VEHICLE BLOCKING AHEAD [{}])'.format(vehicle.id))\n\n self._state = AgentState.BLOCKED_BY_VEHICLE\n hazard_detected = True\n\n # check for the state of the traffic lights\n light_state, traffic_light = self._is_light_red(lights_list)\n if light_state:\n if debug:\n print('=== RED LIGHT AHEAD [{}])'.format(traffic_light.id))\n\n self._state = AgentState.BLOCKED_RED_LIGHT\n hazard_detected = True\n\n if hazard_detected:\n control = self.emergency_stop()\n else:\n self._state = AgentState.NAVIGATING\n # standard local planner behavior\n control = self._local_planner.run_step()\n\n return control\n" }, { "path": "agents/navigation/types_behavior.py", "content": "# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" This module contains the different parameters sets for each behavior. \"\"\"\n\n\nclass Cautious(object):\n \"\"\"Class for Cautious agent.\"\"\"\n max_speed = 40\n speed_lim_dist = 6\n speed_decrease = 12\n safety_time = 3\n min_proximity_threshold = 12\n braking_distance = 6\n overtake_counter = -1\n tailgate_counter = 0\n\n\nclass Normal(object):\n \"\"\"Class for Normal agent.\"\"\"\n max_speed = 50\n speed_lim_dist = 3\n speed_decrease = 10\n safety_time = 3\n min_proximity_threshold = 10\n braking_distance = 5\n overtake_counter = 0\n tailgate_counter = 0\n\n\nclass Aggressive(object):\n \"\"\"Class for Aggressive agent.\"\"\"\n max_speed = 70\n speed_lim_dist = 1\n speed_decrease = 8\n safety_time = 3\n min_proximity_threshold = 8\n braking_distance = 4\n overtake_counter = 0\n tailgate_counter = -1\n" }, { "path": "agents/tools/__init__.py", "content": "" }, { "path": "agents/tools/misc.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018 Intel Labs.\n# authors: German Ros (german.ros@intel.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\" Module with auxiliary functions. \"\"\"\n\nimport math\nimport numpy as np\nimport carla\n\ndef draw_waypoints(world, waypoints, z=0.5):\n \"\"\"\n Draw a list of waypoints at a certain height given in z.\n\n :param world: carla.world object\n :param waypoints: list or iterable container with the waypoints to draw\n :param z: height in meters\n \"\"\"\n for wpt in waypoints:\n wpt_t = wpt.transform\n begin = wpt_t.location + carla.Location(z=z)\n angle = math.radians(wpt_t.rotation.yaw)\n end = begin + carla.Location(x=math.cos(angle), y=math.sin(angle))\n world.debug.draw_arrow(begin, end, arrow_size=0.3, life_time=1.0)\n\n\ndef get_speed(vehicle):\n \"\"\"\n Compute speed of a vehicle in Km/h.\n\n :param vehicle: the vehicle for which speed is calculated\n :return: speed as a float in Km/h\n \"\"\"\n vel = vehicle.get_velocity()\n\n return 3.6 * math.sqrt(vel.x ** 2 + vel.y ** 2 + vel.z ** 2)\n\ndef is_within_distance_ahead(target_transform, current_transform, max_distance):\n \"\"\"\n Check if a target object is within a certain distance in front of a reference object.\n\n :param target_transform: location of the target object\n :param current_transform: location of the reference object\n :param orientation: orientation of the reference object\n :param max_distance: maximum allowed distance\n :return: True if target object is within max_distance ahead of the reference object\n \"\"\"\n target_vector = np.array([target_transform.location.x - current_transform.location.x, target_transform.location.y - current_transform.location.y])\n norm_target = np.linalg.norm(target_vector)\n\n # If the vector is too short, we can simply stop here\n if norm_target < 0.001:\n return True\n\n if norm_target > max_distance:\n return False\n\n fwd = current_transform.get_forward_vector()\n forward_vector = np.array([fwd.x, fwd.y])\n d_angle = math.degrees(math.acos(np.clip(np.dot(forward_vector, target_vector) / norm_target, -1., 1.)))\n\n return d_angle < 90.0\n\ndef is_within_distance(target_location, current_location, orientation, max_distance, d_angle_th_up, d_angle_th_low=0):\n \"\"\"\n Check if a target object is within a certain distance from a reference object.\n A vehicle in front would be something around 0 deg, while one behind around 180 deg.\n\n :param target_location: location of the target object\n :param current_location: location of the reference object\n :param orientation: orientation of the reference object\n :param max_distance: maximum allowed distance\n :param d_angle_th_up: upper thereshold for angle\n :param d_angle_th_low: low thereshold for angle (optional, default is 0)\n :return: True if target object is within max_distance ahead of the reference object\n \"\"\"\n target_vector = np.array([target_location.x - current_location.x, target_location.y - current_location.y])\n norm_target = np.linalg.norm(target_vector)\n\n # If the vector is too short, we can simply stop here\n if norm_target < 0.001:\n return True\n\n if norm_target > max_distance:\n return False\n\n forward_vector = np.array(\n [math.cos(math.radians(orientation)), math.sin(math.radians(orientation))])\n d_angle = math.degrees(math.acos(np.clip(np.dot(forward_vector, target_vector) / norm_target, -1., 1.)))\n\n return d_angle_th_low < d_angle < d_angle_th_up\n\n\ndef compute_magnitude_angle(target_location, current_location, orientation):\n \"\"\"\n Compute relative angle and distance between a target_location and a current_location\n\n :param target_location: location of the target object\n :param current_location: location of the reference object\n :param orientation: orientation of the reference object\n :return: a tuple composed by the distance to the object and the angle between both objects\n \"\"\"\n target_vector = np.array([target_location.x - current_location.x, target_location.y - current_location.y])\n norm_target = np.linalg.norm(target_vector)\n\n forward_vector = np.array([math.cos(math.radians(orientation)), math.sin(math.radians(orientation))])\n d_angle = math.degrees(math.acos(np.clip(np.dot(forward_vector, target_vector) / norm_target, -1., 1.)))\n\n return (norm_target, d_angle)\n\n\ndef distance_vehicle(waypoint, vehicle_transform):\n \"\"\"\n Returns the 2D distance from a waypoint to a vehicle\n\n :param waypoint: actual waypoint\n :param vehicle_transform: transform of the target vehicle\n \"\"\"\n loc = vehicle_transform.location\n x = waypoint.transform.location.x - loc.x\n y = waypoint.transform.location.y - loc.y\n\n return math.sqrt(x * x + y * y)\n\n\ndef vector(location_1, location_2):\n \"\"\"\n Returns the unit vector from location_1 to location_2\n\n :param location_1, location_2: carla.Location objects\n \"\"\"\n x = location_2.x - location_1.x\n y = location_2.y - location_1.y\n z = location_2.z - location_1.z\n norm = np.linalg.norm([x, y, z]) + np.finfo(float).eps\n\n return [x / norm, y / norm, z / norm]\n\n\ndef compute_distance(location_1, location_2):\n \"\"\"\n Euclidean distance between 3D points\n\n :param location_1, location_2: 3D points\n \"\"\"\n x = location_2.x - location_1.x\n y = location_2.y - location_1.y\n z = location_2.z - location_1.z\n norm = np.linalg.norm([x, y, z]) + np.finfo(float).eps\n return norm\n\n\ndef positive(num):\n \"\"\"\n Return the given number if positive, else 0\n\n :param num: value to check\n \"\"\"\n return num if num > 0.0 else 0.0\n" }, { "path": "camera_calibration/README.md", "content": "# Camera Calibration\n\nUnder the current setting, all four cameras have **fov 150**. If you want to change their fov, you could use the following code to estimate the corresponding distortion parameters. More discussions here: https://github.com/carla-simulator/carla/issues/3412.\n\n1. Build Carla from source code with commit 0c97e9a5de5b35b759cc5b0955801244ed76791f:\n - git clone the latest commit\n - git checkout -b branch_id commit_id\n - following the guidance in https://carla.readthedocs.io/en/0.9.10/build_linux/\n - make launch (so that the Carla is opened with an UE4 editor)\n \n Note that the Carla built from source and its cooresponding environment (Python, environment variables, etc) are only used for estimating the distortion parameters. In all other experiments, we use the official pre-build version 0.9.10.1 and the environment under our setting. If there is any problem during building the Carla, please refer to https://carla.readthedocs.io/en/0.9.10/build_linux/.\n\n2. Conduct Checkboard Calibration\n - Download the checkboard asset for Carla Town3\n https://github.com/AbanobSoliman/IBISCape and put it in the suggested folder of Carla from source\n - In the Carla UE4 editor, click make launch -> Compile -> Build -> Play\n - **python collect_data_for_calibration.py** (sensor setting) to collect image for calibration (For more information about the data collecting process, please refer to tutorials about checkboard camera calibration: https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html and the instruction in the original code https://github.com/AbanobSoliman/IBISCape)\n - **python calculate_distortion_parameters.py** (line 26-35 to calculate intrinsic matrix, line 32 to select image index for calibration) to output distortion matrix\n - Put an image collected by your Carla into this folded and rename it as \"test.png\".\n - **python undistort.py** (line 4-10 to set intrinsics matrix, line 14 to input the distortion matrix from the last step) to verify the effectiveness of the undistortion. Note that the Tangential Distortion effects need extra attention when collect images.\n\n\n## Images without Distortion\nIf your application does not involve official benchmarks such town05Long, Longest6 or Leaderboard, you could eliminate all distortions. In [leaderboard/leaderboard/autoagents/agent_wrapper.py](../leaderboard/leaderboard/autoagents/agent_wrapper.py), set all *lens_circle_multiplier* to 0.0, all *lens_circle_falloff* to 5.0, and all *chromatic_aberration_intensity* to 0.0. \n\nReference: https://carla.readthedocs.io/en/latest/ref_sensors/\n\n\n## Acknowledgements\n\nThe calibration code is based on [IBISCape](https://github.com/AbanobSoliman/IBISCape) and please check out their awsome code.\n" }, { "path": "camera_calibration/calculate_distortion_parameters.py", "content": "#!/usr/bin/env python\n\nimport cv2\nimport numpy as np\nimport os\nimport glob\n\n# Defining the dimensions of checkerboard\nCHECKERBOARD = (7, 7)\ncriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)\n\n# Creating vector to store vectors of 3D points for each checkerboard image\nobjpoints = []\n# Creating vector to store vectors of 2D points for each checkerboard image\nimgpoints = [] \n\n\n# Defining the world coordinates for 3D points\nobjp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)\nobjp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)\nprev_img_shape = None\n\n# Extracting path of individual image stored in a given directory\nimg_path = None\nprint(\"Image Path:\", img_path, \"(Please Change it to your path of Carla **built from source**!!!)\")\n\nimages = glob.glob(img_path+'/*.png')\n\nImageSizeX = 1600\nImageSizeY = 900\nCameraFOV = 150\nf = ImageSizeX /(2 * np.tan(CameraFOV * np.pi / 360))\nCx = ImageSizeX / 2\nCy = ImageSizeY / 2\n# intrinsics = np.array([[214.35935394, 0, 800, ],\n# [ 0, 214.35935394, 450, ], \n# [ 0, 0, 1, ]])\nintrinsics = np.array([[f, 0, Cx],\n [0, f, Cy],\n [0, 0, 1 ]])\nprint(intrinsics)\n\nfor fname in images:\n index = int()\n if fname[:-4].split(\"/\")[-1][-4:] < \"8028\" or fname[:-4].split(\"/\")[-1][-4:] > \"8068\":\n continue\n img = cv2.imread(fname)\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n # Find the chess board corners\n # If desired number of corners are found in the image then ret = true\n ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_NORMALIZE_IMAGE + cv2.CALIB_CB_FAST_CHECK)\n \n \"\"\"\n If desired number of corner are detected,\n we refine the pixel coordinates and display \n them on the images of checker board\n \"\"\"\n print(fname, ret)\n if ret == True:\n objpoints.append(objp)\n # refining pixel coordinates for given 2d points.\n corners2 = cv2.cornerSubPix(gray, corners, (11,11),(-1,-1), criteria)\n \n imgpoints.append(corners2)\n\n # Draw and display the corners\n img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)\n # cv2.imshow('img',img)\n # cv2.waitKey(0)\n\ncv2.destroyAllWindows()\n\nh,w = img.shape[:2]\n\n\"\"\"\nPerforming camera calibration by \npassing the value of known 3D points (objpoints)\nand corresponding pixel coordinates of the \ndetected corners (imgpoints)\n\"\"\"\nret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], intrinsics, None, flags=cv2.CALIB_FIX_INTRINSIC+cv2.CALIB_FIX_PRINCIPAL_POINT)\n\nprint(\"Camera matrix : \\n\")\nprint(mtx)\nprint(\"Distortion Matrix : \\n\")\nprint(dist)\nprint(\"rvecs : \\n\")\nprint(rvecs)\nprint(\"tvecs : \\n\")\nprint(tvecs)" }, { "path": "camera_calibration/collect_data_for_calibration.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2021 IBISC Laborartory, Pelvoux, France\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n##From https://github.com/AbanobSoliman/IBISCape\n\nimport random\nimport glob\nimport os\nimport sys\n\nimport csv\n\n\n####Please Change it to your path of Carla **built from source**\negg_path = None\nprint(\"Egg Path:\", egg_path, \"(Please Change it to your path of Carla **built from source**!!!)\")\n\nsys.path.append(glob.glob(egg_path)[0])\n\nimport cv2\nimport carla\nfrom carla import ColorConverter as cc\n\nimport argparse\nimport collections\nimport datetime\nimport logging\nimport math\nimport random\nimport re\nimport weakref\n\ntry:\n import pygame\n from pygame.locals import KMOD_CTRL\n from pygame.locals import KMOD_SHIFT\n from pygame.locals import K_0\n from pygame.locals import K_9\n from pygame.locals import K_BACKQUOTE\n from pygame.locals import K_BACKSPACE\n from pygame.locals import K_COMMA\n from pygame.locals import K_DOWN\n from pygame.locals import K_ESCAPE\n from pygame.locals import K_F1\n from pygame.locals import K_LEFT\n from pygame.locals import K_PERIOD\n from pygame.locals import K_RIGHT\n from pygame.locals import K_SLASH\n from pygame.locals import K_SPACE\n from pygame.locals import K_TAB\n from pygame.locals import K_UP\n from pygame.locals import K_a\n from pygame.locals import K_b\n from pygame.locals import K_c\n from pygame.locals import K_d\n from pygame.locals import K_g\n from pygame.locals import K_h\n from pygame.locals import K_i\n from pygame.locals import K_l\n from pygame.locals import K_m\n from pygame.locals import K_n\n from pygame.locals import K_p\n from pygame.locals import K_k\n from pygame.locals import K_j\n from pygame.locals import K_q\n from pygame.locals import K_r\n from pygame.locals import K_s\n from pygame.locals import K_v\n from pygame.locals import K_w\n from pygame.locals import K_x\n from pygame.locals import K_z\n from pygame.locals import K_MINUS\n from pygame.locals import K_EQUALS\nexcept ImportError:\n raise RuntimeError(\n 'cannot import pygame, make sure pygame package is installed')\n\ntry:\n import numpy as np\nexcept ImportError:\n raise RuntimeError(\n 'cannot import numpy, make sure numpy package is installed')\n\ntry:\n import queue\nexcept ImportError:\n import Queue as queue\n\n\nimport time\nimport subprocess\n\n\nclient = carla.Client('localhost', 2000)\nclient.set_timeout(20.0)\n\n\n# ==============================================================================\n# -- Creating the World --------------------------------------------------------\n# ==============================================================================\n\nglobal world\nglobal m\nworld = client.get_world()\nm = world.get_map()\nself = world\nself.recording = False\n\n# ==============================================================================\n# -- Output files Handling------------------------------------------------------\n# ==============================================================================\ntry:\n parent_dir = os.getcwd()\n path1 = os.path.join(parent_dir, \"RGBD_Calib_i_%s\" % (m.name))\n os.mkdir(path1)\n path4 = os.path.join(path1, \"rgb\")\n os.mkdir(path4)\n path8 = os.path.join(path4, \"frames\")\n os.mkdir(path8)\n path12 = os.path.join(path1, \"depth\")\n os.mkdir(path12)\n path15 = os.path.join(path12, \"frames\")\n os.mkdir(path15)\n path10 = os.path.join(path1, \"other_sensors\")\n os.mkdir(path10)\n path11 = os.path.join(path1, \"vehicle_gt\")\n os.mkdir(path11)\nexcept OSError:\n print(\"Creation of the directory %s failed\" % path1)\n print(\"Creation of the directory %s failed\" % path4)\n print(\"Creation of the directory %s failed\" % path8)\n print(\"Creation of the directory %s failed\" % path10)\n print(\"Creation of the directory %s failed\" % path11)\n print(\"Creation of the directory %s failed\" % path12)\n print(\"Creation of the directory %s failed\" % path15)\nelse:\n print(\"Successfully created the directory %s \" % path1)\n print(\"Successfully created the directory %s \" % path4)\n print(\"Successfully created the directory %s \" % path8)\n print(\"Successfully created the directory %s \" % path10)\n print(\"Successfully created the directory %s \" % path11)\n print(\"Successfully created the directory %s \" % path12)\n print(\"Successfully created the directory %s \" % path15)\n\nfile_rgb = open(os.path.join(path4, 'timestamps.csv'), \"a\")\nfile_depth = open(os.path.join(path12, 'timestamps.csv'), \"a\")\nfile_groundtruth_sync = open(os.path.join(path11, 'groundtruth_sync.csv'), \"a\")\nfile_GNSS = open(os.path.join(path10, 'gnss.csv'), \"a\")\n\nwriter0 = csv.writer(file_rgb, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\nwriter0.writerow(['#timestamp [ns]', 'filename'])\nwriter8 = csv.writer(file_depth, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\nwriter8.writerow(['#timestamp [ns]', 'filename'])\nwriter4 = csv.writer(file_groundtruth_sync, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\nwriter4.writerow(['#timestamp [ns]', 'p_RS_R_x_m_', 'p_RS_R_y_m_', 'p_RS_R_z_m_', 'v_RS_R_x_mS__1_', 'v_RS_R_y_mS__1_', 'v_RS_R_z_mS__1_', 'a_RS_S_x_mS__2_', 'a_RS_S_y_mS__2_', 'a_RS_S_z_mS__2_', 'q_RS_w__', 'q_RS_x__', 'q_RS_y__', 'q_RS_z__', 'w_RS_S_x_radS__1_', 'w_RS_S_y_radS__1_', 'w_RS_S_z_radS__1_'])\nwriter5 = csv.writer(file_GNSS, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\nwriter5.writerow(['#timestamp [ns]', 'latitude', 'longitude', 'altitude'])\n\n# ==============================================================================\n# -- GnssSensor ----------------------------------------------------------------\n# ==============================================================================\nclass gnssSensor(object):\n def __init__(self, parent_actor):\n self.sensor = None\n self._parent = parent_actor\n self.lat = 0.0\n self.lon = 0.0\n world = self._parent.get_world()\n bp = world.get_blueprint_library().find('sensor.other.gnss')\n bp.set_attribute('noise_alt_bias', '0.0')\n bp.set_attribute('noise_lat_bias', '0.0')\n bp.set_attribute('noise_lon_bias', '0.0')\n bp.set_attribute('noise_alt_stddev', '0.0')\n bp.set_attribute('noise_lat_stddev', '0.0')\n bp.set_attribute('noise_lon_stddev', '0.0')\n bp.set_attribute('noise_seed', '0')\n bp.set_attribute('sensor_tick', '0.05')\n self.sensor = world.spawn_actor(bp, carla.Transform(carla.Location(\n x=-0.2, y=0, z=2.8), carla.Rotation(pitch=0, roll=0, yaw=0)), attach_to=self._parent, attachment_type=carla.AttachmentType.Rigid)\n # We need to pass the lambda a weak reference to self to avoid circular\n # reference.\n weak_self = weakref.ref(self)\n self.sensor.listen(\n lambda event: gnssSensor._on_gnss_event(weak_self, event))\n\n @staticmethod\n def _on_gnss_event(weak_self, event):\n self = weak_self()\n if not self:\n return\n self.lat = event.latitude\n self.lon = event.longitude\n self.alt = event.altitude\n self.tmp = event.timestamp\n\n def destroy(self):\n self.sensor.stop()\n self.sensor.destroy()\n\n\n# ==============================================================================\n# -- Useful Functions-----------------------------------------------------------\n# ==============================================================================\ndef resize_img(img):\n scale_percent = 50 # percent of original size\n width = int(img.shape[1] * scale_percent / 100)\n height = int(img.shape[0] * scale_percent / 100)\n dim = (width, height)\n # resize image\n resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n return resized\n\ndef euler_to_quaternion(yaw, pitch, roll):\n qx = np.sin(roll/2) * np.cos(pitch/2) * np.cos(yaw/2) - np.cos(roll/2) * np.sin(pitch/2) * np.sin(yaw/2)\n qy = np.cos(roll/2) * np.sin(pitch/2) * np.cos(yaw/2) + np.sin(roll/2) * np.cos(pitch/2) * np.sin(yaw/2)\n qz = np.cos(roll/2) * np.cos(pitch/2) * np.sin(yaw/2) - np.sin(roll/2) * np.sin(pitch/2) * np.cos(yaw/2)\n qw = np.cos(roll/2) * np.cos(pitch/2) * np.cos(yaw/2) + np.sin(roll/2) * np.sin(pitch/2) * np.sin(yaw/2)\n quat = [qw, qx, qy, qz]\n return quat/np.linalg.norm(quat)\n\n\ndef draw_rgb_image(surface, image_rgb):\n array = np.frombuffer(image_rgb.raw_data, dtype=np.dtype(\"uint8\"))\n array = np.reshape(array, (image_rgb.height, image_rgb.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n image_surface = pygame.surfarray.make_surface(resize_img(array.swapaxes(0, 1)))\n # image_surface.set_alpha(100)\n surface.blit(image_surface, (0, 0))\n\ndef draw_depth_image(surface, image_depth):\n array = np.frombuffer(image_depth.raw_data, dtype=np.dtype(\"uint8\"))\n array = np.reshape(array, (image_depth.height, image_depth.width, 4))\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n image_surface = pygame.surfarray.make_surface(resize_img(array.swapaxes(0, 1)))\n # image_surface.set_alpha(100)\n surface.blit(image_surface, (1000, 0))\n \ndef get_font():\n fonts = [x for x in pygame.font.get_fonts()]\n default_font = 'ubuntumono'\n font = default_font if default_font in fonts else fonts[0]\n font = pygame.font.match_font(font)\n return pygame.font.Font(font, 14)\n\n\ndef create_main_actors(start_pose, waypoint):\n blueprint_library = world.get_blueprint_library()\n\n vehicle = world.spawn_actor(\n blueprint_library.filter('vehicle')[1], start_pose)\n vehicle.set_simulate_physics(True)\n vehicle.set_transform(waypoint.transform)\n\n bp_rgb = blueprint_library.find('sensor.camera.rgb')\n fov = \"150.0\"\n print(\"Current Fov:\", fov, \"Please Change it to your deisred fov!\")\n # Modify the basic attributes of the blueprint\n bp_rgb.set_attribute('image_size_x', '1600')\n bp_rgb.set_attribute('image_size_y', '900')\n bp_rgb.set_attribute('sensor_tick', '0.05')\n bp_rgb.set_attribute('bloom_intensity', '0.675')\n bp_rgb.set_attribute('fov', fov)\n bp_rgb.set_attribute('fstop', '1.4')\n bp_rgb.set_attribute('iso', '100.0')\n bp_rgb.set_attribute('gamma', '2.2')\n bp_rgb.set_attribute('lens_flare_intensity', '0.1')\n bp_rgb.set_attribute('shutter_speed', '200.0')\n # Modify the lens distortion attributes\n bp_rgb.set_attribute('chromatic_aberration_intensity', '0.5')\n bp_rgb.set_attribute('chromatic_aberration_offset', '0.0')\n bp_rgb.set_attribute('lens_circle_falloff', '3.0')\n bp_rgb.set_attribute('lens_circle_multiplier', '3.0')\n bp_rgb.set_attribute('lens_k', '-1.0')\n bp_rgb.set_attribute('lens_kcube', '0.0')\n bp_rgb.set_attribute('lens_x_size', '0.08')\n bp_rgb.set_attribute('lens_y_size', '0.08')\n camera_rgb = world.spawn_actor(\n bp_rgb,\n carla.Transform(carla.Location(x=1.3, y=0.0, z=2.5),\n carla.Rotation(pitch=0.0, roll=0.0, yaw=0.0)),\n attach_to=vehicle, attachment_type=carla.AttachmentType.Rigid)\n \n bp_depth = blueprint_library.find('sensor.camera.depth')\n # Modify the basic attributes of the blueprint\n bp_depth.set_attribute('image_size_x', '160')\n bp_depth.set_attribute('image_size_y', '90')\n bp_depth.set_attribute('fov', fov)\n bp_depth.set_attribute('sensor_tick', '0.05')\n # Modify the lens distortion attributes\n bp_depth.set_attribute('lens_circle_falloff', '5.0')\n bp_depth.set_attribute('lens_circle_multiplier', '0.0')\n bp_depth.set_attribute('lens_k', '-1.0')\n bp_depth.set_attribute('lens_kcube', '0.0')\n bp_depth.set_attribute('lens_x_size', '0.08')\n bp_depth.set_attribute('lens_y_size', '0.08')\n camera_depth = world.spawn_actor(\n bp_depth,\n carla.Transform(carla.Location(x=0.0, y=0.0, z=2.8),\n carla.Rotation(pitch=0.0, roll=0.0, yaw=0.0)),\n attach_to=vehicle, attachment_type=carla.AttachmentType.Rigid)\n\n Gnss_sensor = gnssSensor(vehicle)\n\n return vehicle, camera_rgb, camera_depth, Gnss_sensor\n\n\ndef get_actor_display_name(actor, truncate=250):\n name = ' '.join(actor.type_id.replace('_', '.').title().split('.')[1:])\n return (name[:truncate - 1] + u'\\u2026') if len(name) > truncate else name\n\n \ndef _RGB_sensor_callback(sensor_data, ts, display):\n draw_rgb_image(display, sensor_data)\n if self.recording:\n sensor_data.save_to_disk('RGBD_Calib_i_%s/rgb/frames/%d.png' % (m.name, ts))\n file_rgb.write(\"%d,%d.png \\n\" % (ts, ts))\n \ndef _DEPTH_sensor_callback(sensor_data, ts, display):\n sensor_data.convert(cc.LogarithmicDepth)\n draw_depth_image(display, sensor_data)\n if self.recording:\n sensor_data.save_to_disk('RGBD_Calib_i_%s/depth/frames/%d.png' % (m.name, ts))\n file_depth.write(\"%d,%d.png \\n\" % (ts, ts))\n\n \n# ==============================================================================\n# -- Sensor Synchronization-----------------------------------------------------\n# ==============================================================================\n\nclass CarlaSyncMode(object):\n \"\"\"\n Context manager to synchronize output from different sensors. Synchronous\n mode is enabled as long as we are inside this context\n\n with CarlaSyncMode(world, sensors) as sync_mode:\n while True:\n data = sync_mode.tick(timeout=1.0)\n\n \"\"\"\n def __init__(self, world, *sensors, **kwargs):\n self.world = world\n self.sensors = sensors\n self.frame = None\n self.delta_seconds = 1.0 / kwargs.get('fps', 20)\n self._queues = []\n self._settings = None\n\n def __enter__(self):\n self._settings = self.world.get_settings()\n self.frame = self.world.apply_settings(carla.WorldSettings(\n no_rendering_mode=False,\n synchronous_mode=True,\n fixed_delta_seconds=self.delta_seconds))\n\n def make_queue(register_event):\n q = queue.Queue()\n register_event(q.put)\n self._queues.append(q)\n\n make_queue(self.world.on_tick)\n for sensor in self.sensors:\n make_queue(sensor.listen)\n return self\n\n def tick(self, timeout):\n self.frame = self.world.tick()\n data = [self._retrieve_data(q, timeout) for q in self._queues]\n assert all(x.frame == self.frame for x in data)\n return data\n\n def __exit__(self, *args, **kwargs):\n self.world.apply_settings(self._settings)\n\n def _retrieve_data(self, sensor_queue, timeout):\n while True:\n data = sensor_queue.get(timeout=timeout)\n if data.frame == self.frame:\n return data\n \n# ==============================================================================\n# -- Main Function--------------------------------------------------------------\n# ==============================================================================\n\n\ndef main():\n actor_list = []\n pygame.init()\n pygame.display.set_caption(\n 'IBISCape: Multi-modal Data Acquisition Framework')\n\n display = pygame.display.set_mode(\n (1200, 450),\n pygame.HWSURFACE | pygame.DOUBLEBUF)\n font = get_font()\n clock = pygame.time.Clock()\n\n iterator = 1\n rvrs = False\n hand_brake = False \n\n try:\n start_Pose = m.get_spawn_points()\n start_pose = start_Pose[32]\n waypoint = m.get_waypoint(start_pose.location)\n\n vehicle, camera_rgb, camera_depth, Gnss_sensor = create_main_actors(start_pose, waypoint)\n file_veh_sim = open(os.path.join(path11, '%s_simulation.txt' %\n get_actor_display_name(vehicle, truncate=200)), \"a\")\n file_associate = open(os.path.join(path11, 'association.txt'),\"a\") \n\n actor_list.append(vehicle)\n actor_list.append(camera_rgb)\n actor_list.append(camera_depth)\n actor_list.append(Gnss_sensor)\n \n # Create a synchronous mode context.\n with CarlaSyncMode(world, camera_rgb, camera_depth, fps=20) as sync_mode:\n\n while True:\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n elif event.type == pygame.KEYDOWN:\n if event.key == K_r:\n self.recording = not self.recording\n elif event.key == K_q:\n rvrs = not rvrs\n elif event.key == K_SPACE:\n vehicle.disable_constant_velocity()\n vehicle.apply_control(\n carla.VehicleControl(hand_brake=not hand_brake))\n elif event.key == K_w:\n if rvrs:\n vehicle.apply_control(\n carla.VehicleControl(reverse=True, brake=0.0))\n vehicle.enable_constant_velocity(\n carla.Vector3D(-3, 0, 0))\n else:\n vehicle.apply_control(\n carla.VehicleControl(throttle=1.0, brake=0.0))\n vehicle.enable_constant_velocity(\n carla.Vector3D(3, 0, 0))\n elif event.key == K_s:\n vehicle.disable_constant_velocity()\n vehicle.apply_control(\n carla.VehicleControl(throttle=0.0, brake=1.0))\n elif event.key == K_a:\n vehicle.apply_control(\n carla.VehicleControl(steer=-1.0))\n elif event.key == K_d:\n vehicle.apply_control(\n carla.VehicleControl(steer=1.0))\n elif event.key == pygame.K_ESCAPE:\n return\n elif event.type == pygame.KEYUP:\n if event.key == K_a:\n vehicle.apply_control(\n carla.VehicleControl(steer=0.0))\n elif event.key == K_d:\n vehicle.apply_control(\n carla.VehicleControl(steer=0.0))\n\n clock.tick()\n\n # Advance the simulation and wait for the data.\n snapshot, image_rgb, image_depth = sync_mode.tick(\n timeout=2.0)\n Pose = vehicle.get_transform()\n Accl = vehicle.get_acceleration()\n velo = vehicle.get_velocity()\n AngV = vehicle.get_angular_velocity()\n Ctrl = vehicle.get_control()\n t_world = snapshot.timestamp.elapsed_seconds*(10**9)\n\n fps = round(1.0 / snapshot.timestamp.delta_seconds)\n\n # Draw the display.\n _RGB_sensor_callback(image_rgb, t_world, display)\n _DEPTH_sensor_callback(image_depth, t_world, display) \n \n if self.recording:\n file_GNSS.write(\"%f,%21.21f,%21.21f,%21.21f \\n\" % (t_world, Gnss_sensor.lat, Gnss_sensor.lon, Gnss_sensor.alt))\n quat = euler_to_quaternion(math.radians(Pose.rotation.yaw), math.radians(Pose.rotation.pitch), math.radians(Pose.rotation.roll))\n file_groundtruth_sync.write(\"%d,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f,%21.21f \\n\" % (t_world, Pose.location.x, Pose.location.y, Pose.location.z, velo.x, velo.y, velo.z, Accl.x, Accl.y, Accl.z, quat[0], quat[1], quat[2], quat[3], math.radians(AngV.x), math.radians(AngV.y), math.radians(AngV.z)))\n file_veh_sim.write(\"%d,%f,%f,%f \\n\" % (\n t_world, Ctrl.throttle, Ctrl.steer, Ctrl.brake))\n file_associate.write(\"%d rgb/%d.png %d depth/%d.png \\n\" %(t_world,t_world,t_world,t_world))\n display.blit(font.render('Calibration Frame Number: %d' % (\n iterator), True, (250, 0, 0)), (50, 208))\n iterator += 1\n\n display.blit(\n font.render('% 5d FPS (real)' %\n clock.get_fps(), True, (250, 0, 0)),\n (8, 10))\n display.blit(\n font.render('% 5d FPS (simulated)' %\n fps, True, (250, 0, 0)),\n (8, 28))\n display.blit(font.render('Recording %s' % (\n 'On' if self.recording else 'Off - Press R Key to Record'), True, (250, 0, 0)), (50, 46))\n display.blit(font.render('GNSS:% 24s' % ('(% 3.8f, % 3.8f, % 3.8f)' % (\n Gnss_sensor.lat, Gnss_sensor.lon, Gnss_sensor.alt)), True, (250, 0, 0)), (50, 100))\n display.blit(font.render('x=%5.8f,y=%5.8f,z=%5.8f (m)' % (\n Pose.location.x, Pose.location.y, Pose.location.z), True, (250, 0, 0)), (50, 118))\n display.blit(font.render('vx=%5.8f,vy=%5.8f,vz=%5.8f (m/sec)' % (\n velo.x, velo.y, velo.z), True, (250, 0, 0)), (50, 136))\n display.blit(font.render('ax=%5.8f,ay=%5.8f,az=%5.8f (m/sec^2)' % (\n Accl.x, Accl.y, Accl.z), True, (250, 0, 0)), (50, 154))\n display.blit(font.render('roll=%5.8f,pitch=%5.8f,yaw=%5.8f (rad)' % (\n math.radians(Pose.rotation.roll), math.radians(Pose.rotation.pitch), math.radians(Pose.rotation.yaw)), True, (250, 0, 0)), (50, 172))\n display.blit(font.render('r_d=%5.8f,p_d=%5.8f,y_d=%5.8f (rad/sec)' % (\n math.radians(AngV.x), math.radians(AngV.y), math.radians(AngV.z)), True, (250, 0, 0)), (50, 190))\n\n pygame.display.flip()\n\n finally:\n\n print('destroying actors.')\n for actor in actor_list:\n actor.destroy()\n\n file_rgb.close()\n file_depth.close()\n file_groundtruth_sync.close()\n file_GNSS.close()\n file_veh_sim.close()\n file_associate.close()\n\n pygame.quit()\n print('done.')\n\n\nif __name__ == '__main__':\n\n try:\n\n main()\n\n except KeyboardInterrupt:\n print('\\nCancelled by user. Bye!')\n" }, { "path": "camera_calibration/undistort.py", "content": "import cv2 as cv\nimport numpy as np\nimport torch\nImageSizeX = 1600\nImageSizeY = 900\nCameraFOV = 150\nf = ImageSizeX /(2 * np.tan(CameraFOV * np.pi / 360))\nCx = ImageSizeX / 2\nCy = ImageSizeY / 2\nintrinsics = np.array([[f, 0, Cx],\n [0, f, Cy],\n [0, 0, 1 ]])\n\ndistortion_matrix = np.array([[ 0.00888296, -0.00130899, 0.00012061, -0.00338673, 0.00028834]])\nnewcameramtx, roi = cv.getOptimalNewCameraMatrix(intrinsics, distortion_matrix, (1600, 900), 1, (1600, 900))\n# newcameramtx = np.array([[304.14395142, 0, 788.25758876,],\n# [ 0, 221.49429321, 449.78972161,],\n# [ 0, 0, 1, ],])\n#print(roi, newcameramtx)\n\n\noriginal_img = cv.imread(\"test\" + '.png')\nmapx, mapy = cv.initUndistortRectifyMap(intrinsics, distortion_matrix, None, newcameramtx, (ImageSizeX, ImageSizeY), 5)\nundistorted_img = cv.remap(original_img, mapx, mapy, cv.INTER_LINEAR)\ncv.imwrite(\"undistort.png\", undistorted_img)" }, { "path": "dataset/tools/generate_metadata.py", "content": "import json\nimport pickle\nimport os, sys\n\ntowns = [\"01\", \"02\", \"03\", \"04\",\"05\", \"06\", \"07\", \"10\"]\nindex_per_down = [\"val\"] + [str(_).zfill(2) for _ in range(0, 11)]\ndata_folder = \"../\"\n\njson_path = \"../collect_data_json\"\n\n\n## Store the length of each route since we truncate those ones with infraction happening \nlength_dict = {}\nfor town_name in towns:\n for data_index in index_per_down:\n length_dict[\"town\"+town_name+\"_\"+data_index] = {}\n try:\n with open(os.path.join(json_path, \"data_collect_town\"+town_name+\"_\"+data_index+\"_results.json\", ), \"r\") as f:\n records = json.load(f)[\"_checkpoint\"][\"records\"]\n except:\n print(\"No json for\", os.path.join(json_path, \"data_collect_town\"+town_name+\"_\"+data_index+\"_results.json\", ), flush=True)\n continue\n for index, record in enumerate(records):\n record = records[index]\n route_data_folder = os.path.join(data_folder, \"town\"+town_name+\"_\"+data_index, record[\"meta\"][\"folder_name\"])\n\n measurements_files = os.listdir(os.path.join(route_data_folder, \"measurements\"))\n total_length = len(measurements_files)\n if record[\"scores\"][\"score_composed\"] >= 100:\n length_dict[\"town\"+town_name+\"_\"+data_index][route_data_folder] = total_length\n continue\n\n # timeout or blocked, remove the last ones where the vehicle stops\n if len(record[\"infractions\"][\"route_timeout\"]) > 0 or \\\n len(record[\"infractions\"][\"vehicle_blocked\"]) > 0:\n stop_index = 0\n for i in range(total_length-1, 0, -1):\n fname = os.path.join(route_data_folder, \"measurements\", str(i).zfill(4)) + \".json\"\n with open(fname, 'r') as mf:\n speed = json.load(mf)[\"speed\"]\n if speed > 0.1:\n stop_index = i\n break\n length_dict[\"town\"+town_name+\"_\"+data_index][route_data_folder] = min(total_length, stop_index + 5)\n # collision or red-light\n elif len(record[\"infractions\"][\"red_light\"]) > 0 or \\\n len(record[\"infractions\"][\"collisions_pedestrian\"]) > 0 or \\\n len(record[\"infractions\"][\"collisions_vehicle\"]) > 0 or \\\n len(record[\"infractions\"][\"collisions_layout\"]) > 0:\n length_dict[\"town\"+town_name+\"_\"+data_index][route_data_folder] = max(0, total_length-10)\n print(os.path.join(json_path, \"data_collect_town\"+town_name+\"_\"+data_index+\"_results.json\", ), \"Routes with Jsons Finished\", flush=True)\n\n\n#### Data without any json file\nall_town_folder = os.listdir(data_folder)\nfor town_name in towns:\n for data_index in index_per_down:\n if \"town\"+town_name+\"_\"+data_index in all_town_folder:\n all_route_folder = os.listdir(os.path.join(data_folder, \"town\"+town_name+\"_\"+data_index))\n for route_folder in all_route_folder:\n route_data_folder = os.path.join(data_folder, \"town\"+town_name+\"_\"+data_index, route_folder)\n if route_data_folder in length_dict[\"town\"+town_name+\"_\"+data_index]:\n continue\n\n measurements_files = os.listdir(os.path.join(route_data_folder, \"measurements\"))\n total_length = len(measurements_files)\n if total_length > 10:\n total_length = total_length - 10\n stop_index = 0\n for i in range(total_length-1, 0, -1):\n fname = os.path.join(route_data_folder, \"measurements\", str(i).zfill(4)) + \".json\"\n with open(fname, 'r') as mf:\n speed = json.load(mf)[\"speed\"]\n if speed > 0.1:\n stop_index = i\n break\n length_dict[\"town\"+town_name+\"_\"+data_index][route_data_folder] = min(total_length, stop_index + 5)\n print(os.path.join(json_path, \"data_collect_town\"+town_name+\"_\"+data_index+\"_results.json\", ), \"Routes without Jsons Finished\", flush=True)\n\nprint(\"Town-Name #Route-Per-Town #Frames -------------------\")\ntotal_cnt = 0\nfor town_name in length_dict:\n cnt = 0\n for number_of_frames_per_route in length_dict[town_name].values():\n cnt += number_of_frames_per_route\n print(town_name, length_dict[town_name], cnt)\n total_cnt += cnt\nprint(\"Total Frames:\", total_cnt)\n\n\nwith open(\"../dataset_metadata.pkl\", \"wb\") as f:\n pickle.dump(length_dict, f)\n\nprint(\"Dataset metadata pickle saved\")\n\n\n" }, { "path": "dataset/tools/generate_random_routes.py", "content": "import glob\nimport os\nimport sys\nsys.path.append('../../')\n\nimport lxml.etree as ET\nimport argparse\nimport random\nimport time\n\nimport carla\n\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\n\n\nWEATHER_LIST = {'ClearNoon':{'cloudiness': 15.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 0.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'CloudyNoon':{'cloudiness': 80.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 45.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetNoon':{'cloudiness': 20.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 45.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetCloudyNoon':{'cloudiness': 90.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 180.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'SoftRainNoon':{'cloudiness': 90.0,\n 'precipitation': 15.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 315.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'MidRainyNoon':{'cloudiness': 80.0,\n 'precipitation': 30.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.40,\n 'sun_azimuth_angle': 0.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'HardRainNoon':{'cloudiness': 90.0,\n 'precipitation': 60.0,\n 'precipitation_deposits': 100.0,\n 'wind_intensity':1.0,\n 'sun_azimuth_angle': 90.0,\n 'sun_altitude_angle': 75.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'ClearSunset':{'cloudiness': 15.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 45.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'CloudySunset':{'cloudiness': 80.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 270.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetSunset':{'cloudiness': 20.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 270.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetCloudySunset':{'cloudiness': 90.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 0.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'MidRainSunset':{'cloudiness': 80.0,\n 'precipitation': 30.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.40,\n 'sun_azimuth_angle': 270.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'HardRainSunset':{'cloudiness': 80.0,\n 'precipitation': 60.0,\n 'precipitation_deposits': 100.0,\n 'wind_intensity':1.0,\n 'sun_azimuth_angle': 0.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'SoftRainSunset':{'cloudiness': 90.0,\n 'precipitation': 15.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.35,\n 'sun_azimuth_angle': 270.0,\n 'sun_altitude_angle': 15.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'ClearNight':{'cloudiness': 15.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity': 0.35,\n 'sun_azimuth_angle': 0.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'CloudyNight':{'cloudiness': 80.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 0.0,\n 'wind_intensity': 0.35,\n 'sun_azimuth_angle': 45.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetNight':{'cloudiness': 20.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity': 0.35,\n 'sun_azimuth_angle': 225.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'WetCloudyNight':{'cloudiness': 90.0,\n 'precipitation': 0.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity': 0.35,\n 'sun_azimuth_angle': 225.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'SoftRainNight':{'cloudiness': 90.0,\n 'precipitation': 15.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity': 0.35,\n 'sun_azimuth_angle': 270.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'MidRainyNight':{'cloudiness': 80.0,\n 'precipitation': 30.0,\n 'precipitation_deposits': 50.0,\n 'wind_intensity':0.4,\n 'sun_azimuth_angle': 225.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n 'HardRainNight':{'cloudiness': 90.0,\n 'precipitation': 60.0,\n 'precipitation_deposits': 100.0,\n 'wind_intensity':1,\n 'sun_azimuth_angle': 225.0,\n 'sun_altitude_angle': -80.0,\n 'fog_density': 0.0, \n 'fog_distance': 0.0,\n 'fog_falloff': 0.0,\n 'wetness': 0.0,\n },\n }\n\n\n\n\n\ndef interpolate_trajectory(world_map, waypoints_trajectory, hop_resolution=1.0):\n \"\"\"\n Given some raw keypoints interpolate a full dense trajectory to be used by the user.\n Args:\n world: an reference to the CARLA world so we can use the planner\n waypoints_trajectory: the current coarse trajectory\n hop_resolution: is the resolution, how dense is the provided trajectory going to be made\n Return: \n route: full interpolated route both in GPS coordinates and also in its original form.\n \"\"\"\n\n dao = GlobalRoutePlannerDAO(world_map, hop_resolution)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n # Obtain route plan\n route = []\n is_junction = False\n distance = 0\n\n try:\n for i in range(len(waypoints_trajectory) - 1): # Goes until the one before the last.\n\n waypoint = waypoints_trajectory[i]\n waypoint_next = waypoints_trajectory[i + 1]\n interpolated_trace = grp.trace_route(waypoint, waypoint_next)\n for i, wp_tuple in enumerate(interpolated_trace):\n route.append(wp_tuple[0].transform)\n if i > 0:\n distance += wp_tuple[0].transform.location.distance(interpolated_trace[i-1][0].transform.location)\n if not is_junction:\n is_junction = wp_tuple[0].is_junction\n # print (wp_tuple[0].transform.location, wp_tuple[1])\n except:\n return None, distance, is_junction\n\n return route, distance, is_junction\n\nimport subprocess\n\ndef kill_carla():\n kill_process = subprocess.Popen('killall -9 -r CarlaUE4-Linux', shell=True)\n kill_process.wait()\n time.sleep(5)\n\ndef main():\n carla_path = os.environ[\"CARLA_ROOT\"]\n cmd = 'DISPLAY= bash ' + os.path.join(carla_path, 'CarlaUE4.sh')+f' -opengl -carla-rpc-port={args.port} -nosound'\n server_process = subprocess.Popen(cmd, shell=True, preexec_fn=os.setsid)\n time.sleep(10) ## Wait for carla\n client = carla.Client('localhost', int(args.port))\n client.set_timeout(200.0)\n print(\"find carla!\", args, flush=True)\n\n world = client.load_world(args.town)\n\n print ('loaded world')\n\n waypoint_list = world.get_map().generate_waypoints(2.0)\n print ('got %d possible waypoint'%len(waypoint_list))\n\n actors = world.get_actors()\n traffic_lights_list = actors.filter('*traffic_light')\n print ('got %d traffic lights'%len(traffic_lights_list))\n\n max_dist = 300\n min_dist = 50\n\n valid_route_list = []\n number = 0\n\n distance_list = []\n is_junction_list = []\n\n world_map = world.get_map()\n\n root = ET.Element('routes')\n while number < args.route_num:\n start_wp = random.choice(waypoint_list)\n end_wp = random.choice(waypoint_list)\n start_wp = world_map.get_waypoint(start_wp.transform.location,project_to_road=True, lane_type=carla.LaneType.Driving)\n end_wp = world_map.get_waypoint(end_wp.transform.location,project_to_road=True, lane_type=carla.LaneType.Driving)\n\n if start_wp.transform.location.distance(end_wp.transform.location) < min_dist or start_wp.transform.location.distance(end_wp.transform.location) > max_dist:\n continue\n\n wp_list, distance, is_junction = interpolate_trajectory(world_map, [start_wp.transform.location, end_wp.transform.location])\n if wp_list is None:\n continue\n if distance <= min_dist or distance >= max_dist:\n continue\n distance_list.append(distance)\n is_junction_list.append(is_junction)\n\n route = ET.SubElement(root, 'route', id='%d'%number, town=args.town)\n\n # choose a random weather\n weather = random.choice(list(WEATHER_LIST.keys()))\n route = ET.SubElement(route, 'weather', id=weather, cloudiness='%f'%WEATHER_LIST[weather]['cloudiness'], precipitation='%f'%WEATHER_LIST[weather]['precipitation'], precipitation_deposits='%f'%WEATHER_LIST[weather]['precipitation_deposits'], wind_intensity='%f'%WEATHER_LIST[weather]['wind_intensity'], sun_azimuth_angle='%f'%WEATHER_LIST[weather]['sun_azimuth_angle'], sun_altitude_angle='%f'%WEATHER_LIST[weather]['sun_altitude_angle'], fog_density='%f'%WEATHER_LIST[weather]['fog_density'], fog_distance='%f'%WEATHER_LIST[weather]['fog_distance'], fog_falloff='%f'%WEATHER_LIST[weather]['fog_falloff'],wetness='%f'%WEATHER_LIST[weather]['wetness'])\n\n x, y, yaw = start_wp.transform.location.x, start_wp.transform.location.y, start_wp.transform.rotation.yaw\n ET.SubElement(route, 'waypoint', x='%f'%x, y='%f'%y, z='0.0', \n pitch='0.0', roll='0.0', yaw='%f'%yaw)\n x, y, yaw = end_wp.transform.location.x, end_wp.transform.location.y, end_wp.transform.rotation.yaw\n ET.SubElement(route, 'waypoint', x='%f'%x, y='%f'%y, z='0.0', \n pitch='0.0', roll='0.0', yaw='%f'%yaw)\n \n number += 1\n print(number)\n\n print('Avg distance:', sum(distance_list)/number)\n print('Portion of going through junction:', sum(is_junction_list)/number)\n\n tree = ET.ElementTree(root)\n\n if args.save_file is not None:\n tree.write(args.save_file, xml_declaration=True, encoding='utf-8', pretty_print=True)\n\nif __name__ == '__main__':\n global args\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--save_file', type=str, required=False, default=\"./\", help='xml file path to save the route waypoints')\n parser.add_argument('--town', type=str, default='Town02', help='town for generating routes')\n parser.add_argument('--route_num', type=int, default=300, help='number of routes')\n parser.add_argument('--port', type=int, default=20023, help='number of routes')\n args = parser.parse_args()\n main()\n " }, { "path": "docs/DATA_PREP.md", "content": "# Prepare Dataset\n\n## Script to Collect for One Xml File\nDue to the huge size of the dataset (189K frames - 8TB, 2M frames - 85TB), we provide the instructations to generate the dataset. If the size is still too large for your device, techniques to reduce the size of files could be explored. We look forward to your contributions:). \nUpdate: If your disk space is limited, you could consider trying the techniques mentioned in the compression section of this awesome repo [carla_garge](https://github.com/autonomousvision/carla_garage/blob/main/docs/engineering.md).\n\nThe command to collect data from one .xml file is:\n\n```shell\n## In the ThinkTwice/ directory\nport_for_carla=22023 ## Change the port for each running script to avoid cofliction\nport_for_traffic_manager=22033 ## Change the port for each running script to avoid cofliction\nroute_file=town05_00\nis_resume=False ## If there is the corresponding json file in the folder collect_data_json, you could set it as True to continue after the last finished route.\nis_local=True\nagent_name=roach_ap_agent_data_collection\nscenario_file=all_towns_traffic_scenarios_no256\ncuda_device=0\nCUDA_VISIBLE_DEVICES=$cuda_device nohup bash ./leaderboard/scripts/collect_data.sh $port_for_carla $port_for_traffic_manager $route_file $is_resume $is_local $agent_name $scenario_file 2>&1 > $route_file.log &\n```\n\nor simply:\n\n```shell\nCUDA_VISIBLE_DEVICES=0 nohup bash ./leaderboard/scripts/collect_data.sh 22023 22033 town05_00 False True roach_ap_agent_data_collection all_towns_traffic_scenarios_no256 2>&1 > town05_00.log &\n```\n\nNote that one Carla simulator will be opened by the script and there is no need to open Carla manually. **You should manually kill that Carla to avoid *error: address is used* after the collection is done or reports errors.**\n\n## Collect Whole Dataset\n\nFor 189K training data + the rest of open loop valdiation data = 400k frames in TCP, please collect data with .xmls in [leaderboard/data/routes_for_open_loop_training](../leaderboard/data/routes_for_open_loop_training) with suffix *00, 01, 02, val*. During open-looped running, we train on town 01,03, 04, 06 and validate on town 02, 05 as in LAV, Transfuser, TCP.\n\nFor 2M frames data, please collect data with all .xmls in [leaderboard/data/routes_for_open_loop_training](../leaderboard/data/routes_for_open_loop_training). During open-looped running, we train on all routes except those with suffix *val*.\n\n\n## Generate More Routes\nIf you want to collect more data with random routes, you could use [dataset/tools/generate_random_routes.py](../dataset/tools/generate_random_routes.py)\n\n```shell\nCUDA_VISIBLE_DEVICES=0 python generate_random_routes.py --save_file tmp.xml --town Town02 --route_num 5 --port 22023\n```\n\n## Generate Metadata for Open-Loop Training\nAfter collecting data by the expert, we filter out those frames where expert made mistakes and then generate the meta information for all routes together:\n```shell\n#In ThinkTwice/dataset/tools directory\npython generate_metadata.py #It could take a day for the 2M frames dataset.\n```\n\n## Additional Notes\n\n- We strongly recommnd you **first collecting a small number of data** (for example, one route with ~20 frames) to make sure that the functions of open-loop training and closed-loop evaluation work well (by thorough visualizations and test runs). Otherwise, it could be time-consuming and frustrating to find out that there are some bugs or missing information (for example, we do not use semantic lidar sensor in our code).\n\n- Since we do not use any bounding boxs in our code, **if you would like to add some functions about bounding box, please first make sure that the collected information is correct and enough for your desired usage.** Test runs and visualizations as mentioned in the previous note is strongly recommended. \n\n- There are several coordinate system within the code: Unreal, Roach, Lidar, Camera, GNSS, Control, etc. If you want to modify or add any new component, visualizations to check whether they align with existing ones are strongly recommended\n\n- You could modify [leaderboard/team_code/roach_ap_agent_data_collection.py](../leaderboard/team_code/roach_ap_agent_data_collection.py) to save the information you need for training. \n\n- For the size of images, we collect images at 900x1600 and we downsample them into 448x896 to save GPU memory. Thus, you may change the collected images' resolution (as well as depth and segmentation labels) to save disk space.\n\n- For traffic light segmentation in images, we wrote rules regarding color to generate GT, which might not be accurate. You could consider use the build-in API of Carla to generate the GT.\n \n- To reduce the data collection cost, you could first only collect data from Town01, Town03, Town04, and Town06 for training as in Appendix B Table 1 of [TCP](https://arxiv.org/pdf/2206.08129.pdf) and do not conduct open-loop validation, which could approximately half the needed data. When the model achieves *current_throttle_brake_offset < 0.1* and *longitudinal_offset < 0.2* in training set, it is considered fitting well.\n" }, { "path": "docs/EVAL.md", "content": "# Clsoed-Loop Evaluation\n\nTo evaluate in the town05long:\n```shell\n## In the ThinkTwice/ directory\nport_for_carla=22023 ## Change the port for each running script to avoid cofliction\nport_for_traffic_manager=22033 ## Change the port for each running script to avoid cofliction\nteam_agent=thinktwice_agent\nis_resume=False ## If there is the corresponding json file in the folder closed_loop_eval_log, you could set it as True to continue after the last finished route.\nis_local=True\nckpt_and_config_path=open_loop_training/ckpt/thinktwice.pth+open_loop_training/configs/thinktwice.py\nscenario_file=all_towns_traffic_scenarios_no256\ncuda_device=0\nsetting_name=thinktwice_town05long\nCUDA_VISIBLE_DEVICES=$cuda_device nohup bash ./leaderboard/scripts/evaluation_town05long.sh $port_for_carla $port_for_traffic_manager $team_agent $is_resume $is_local $ckpt_and_config_path $scenario_file $setting_name 2>&1 > $setting_name.log &\n```\n\nor simply:\n```shell\n## In the ThinkTwice/ directory\nCUDA_VISIBLE_DEVICES=0 nohup bash ./leaderboard/scripts/evaluation_town05long.sh 22023 22033 thinktwice_agent False True open_loop_training/ckpt/thinktwice.pth+open_loop_training/configs/thinktwice.py all_towns_traffic_scenarios_no256 thinktwice_town05long 2>&1 > thinktwice_town05long.log &\n```\n\nTo evaluate in the longest6, you can simply use:\n```shell\n## In the ThinkTwice/ directory\nCUDA_VISIBLE_DEVICES=0 nohup bash ./leaderboard/scripts/evaluation_longest6.sh 23023 23033 thinktwice_agent False True open_loop_training/ckpt/thinktwice.pth+open_loop_training/configs/thinktwice.py longest6_eval_scenarios thinktwice_longest6 2>&1 > thinktwice_longest6.log &\n```\n\nNote that the evaluation result is in the directory **closed_loop_eval_log/results_$setting_name.json** and the visualizations and recordings for debug (top-down view, front view, and canbus) are in the directory **closed_loop_eval_log/eval_log/$setting_name**.\n\nWarning: The visualizations and recordings could take lots of disk space. Please monitor those folders in the [closed_loop_eval_log/eval_log/](../closed_loop_eval_log/eval_log/) and delete those useless ones in time. You could also modify the **save** function of [leaderboard/team_code/thinktwice_agent.py](../leaderboard/team_code/thinktwice_agent.py) to change the saved information during evaluation.\n\nUpdate: As kindly reminded by the authors of [carla_garge](https://github.com/autonomousvision/carla_garage) (One awsome repo for e2e ad! Check it out for more detials), we update the implementation of longest6 to align with the one used by [Transfuser](https://github.com/autonomousvision/transfuser/tree/2022/leaderboard). Specifically, they ignore the penalty score of running stop sign and increase the number of agents in the scene." }, { "path": "docs/INSTALL.md", "content": "# Installation\nModified from mmdetection3d, BEVFormer, UniAD\n\n**a. Env: Create a conda virtual environment and activate it.**\n```shell\nconda create -n thinktwice python=3.7 -y ## Must be py3.7 required by Carla 9.10\nconda activate thinktwice\n```\n\n**b. Torch: Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).**\n```shell\nconda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y\n```\n\n**c. GCC: Make sure gcc>=5 in conda env.**\n```shell\nconda install -c omgarcia gcc-6\nconda install libgcc -y\nconda install -c conda-forge libcxxabi -y\n```\n\n**d. CUDA: Before installing MMCV family, you need to set up the environment variables in ~/.bashrc (for compiling some operators on the gpu).**\n```shell\nexport CUDA_HOME=YOUR_CUDA_PATH/\n# Eg: export CUDA_HOME=/mnt/cuda-11.3/\nexport PATH=$PATH:YOUR_CUDA_PATH//bin\n# Eg: export CUDA_HOME=/mnt/cuda-11.3/bin\nexport LD_LIBRARY_PATH=YOUR_CUDA_PATH/lib64:YOUR_CONDA_PATH/envs/thinktwice/lib\n# Eg: export LD_LIBRARY_PATH=/mnt/cuda-11.3/lib64:/mnt/miniconda3/envs/thinktwice/lib\nexport LD_PRELOAD=YOUR_CONDA_PATH/envs/thinktwice/lib/libstdc++.so.6.0.29\n# Eg: export LD_PRELOAD=/mnt/miniconda3/envs/thinktwice/lib/libstdc++.so.6.0.29\n```\n\n\n**e. Install mmcv-full, mmdet, and mmseg**\n```shell\npip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.12/index.html\npip install mmdet==2.28.2\npip install mmsegmentation==0.30.0\n```\n\n\n**f. Install mmdet3d from source code.**\n```shell\ngit clone https://github.com/open-mmlab/mmdetection3d.git -b 1.0 ## Note that 1.1 is incompatible\ncd mmdetection3d\npip install cumm-cu113\npip install spconv-cu113 ## For fast lidar model\nMMCV_WITH_OPS=1 FORCE_CUDA=1 pip install -v -e . ## Must have a GPU\n```\n\n\n**g. Other Requirements.**\n```shell\nconda install -c anaconda protobuf -y\nconda install matplotlib -y\nconda install requests -y\nconda install tabulate -y\nconda install protobuf -y\nconda install -c anaconda setuptools -y\nconda install -c conda-forge libjpeg-turbo -y\nconda install -c anaconda decorator -y\nconda install shapely -y\nconda install -c anaconda ephem -y\nconda install -c conda-forge omegaconf -y\nconda install -c conda-forge hydra -y\nconda install -c anaconda scikit-image -y\nconda install -c conda-forge opt_einsum -y\nconda install -c conda-forge mpi4py -y\nconda install h5py -y\nconda install -c conda-forge importlib-metadata -y\nconda install -c conda-forge zipp -y\npip install gym==0.17.2\npip install imgaug\npip install opencv-python\npip install opencv-contrib-python\npip install pygame\npip install py_trees==0.8.1\npip install dictor\npip install gym==0.17.2\npip install stable-baselines3==0.8.0\npip install numpy --upgrade\n\npip install shapely==1.6.4.post2 --force-reinstall ### Important! Higher version of shapely may make the CARLA crash mysteriously!\n```\n\n**h. Install Carla. (From Roach)**\n```shell\nmkdir carla\ncd carla\nwget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.10.1.tar.gz\ntar -xvzf CARLA_0.9.10.1.tar.gz\necho \"export CARLA_ROOT=/mnt/carla\" >> /mnt/.bashrc ## Set to your own Carla path and ~/.bashrc in your system\ncd Import && wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/AdditionalMaps_0.9.10.1.tar.gz\ncd .. && bash ImportAssets.sh\nrm CARLA_0.9.11.tar.gz Import/AdditionalMaps_0.9.10.1.tar.gz\nrm Import/AdditionalMaps_0.9.10.1.tar.gz Import/AdditionalMaps_0.9.11.tar.gz\ncd && source .bashrc\n\necho \"$CARLA_ROOT/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg\" >> YOUR_CONDA_PATH/envs/thinktwice/lib/python3.7/site-packages/carla.pth\n```\n\n**i. Clone ThinkTwice.**\n```shell\ngit clone git@github.com:OpenDriveLab/ThinkTwice.git\ncd ThinkTwice/open_loop_training\npython setup.py develop ## Compile CUDA function for LSS from BEVDepth\n```\n" }, { "path": "docs/TRAIN.md", "content": "# Train Your Model\n\n## Script For Training\n\nOur training pipeline is based on [mmcv](https://github.com/open-mmlab/mmcv) and [mmdet3d](https://github.com/open-mmlab/mmdetection3d). To train a ThinkTwice model, you could use:\n```shell\n#In ThinkTwice/open_loop_training/ directory\n#We train on 16 A100 for 4 days\nCUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7, python -m torch.distributed.launch --nproc_per_node=8 --master_port=22023 train.py configs/thinktwice.py --work-dir=work_dirs/thinktwice --launcher=\"pytorch\"\n```\n\nFor single GPU debug, you could simply use:\n```shell\n#In ThinkTwice/open_loop_training/ directory\nCUDA_VISIBLE_DEVICES=0 python train.py configs/thinktwice.py --work-dir=work_dirs/debug\n```\n\n## Code Structure\nWe give the structure of the training code. Note that We only introduce those folders/files are commonly used and modified.\n\n ThinkTwice/open_loop_training\n ├── ckpt # Checkpoints\n ├── configs # Hyper-Parameter\n ├── work_dirs # Training Log\n ├── code # Preprocessing, DataLoader, Model\n │ ├── apis # Training pipeline for mmdet3D\n │ ├── core # The hooks for mmdet3D\n │ ├── datasets # Preprocessing and DataLoader\n | | ├── pipelines # Functions of Preprocessing and DataLoader\n │ | ├── samplers # For DDP\n │ | └── carla_dataset.py # Framework of Preprocessing and DataLoading\n │ ├── model_code # Neural Network\n | | ├── backbones # Module of Encoder\n | | └── dense_heads # Module of Decoder and Loss Functions\n │ └── encoder_decoder_framework.py # Entrance of Neural Network\n └── train.py # Entrance of Training\n\n\n\n## Tips\n- Change **is_dev** in [open_loop_training/configs/thinktwice.py](../open_loop_training/configs/thinktwice.py) to True when you develop your model and to False during training\n- Set **is_full** in [open_loop_training/configs/thinktwice.py](../open_loop_training/configs/thinktwice.py) to False would only use the same number of data as TCP while to True would use all possible data recorded in **dataset/dataset_metadata.pkl**.\n- Your could start with [open_loop_training/code/encoder_decoder_framework.py](../open_loop_training/configs/thinktwice.py) when you want to learn about the neural network and [open_loop_training/code/datasets/carla_dataset.py](../open_loop_training/configs/thinktwice.py) when you want to learn about data." }, { "path": "leaderboard/LICENSE", "content": "MIT License\n\nCopyright (c) 2019 CARLA\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "leaderboard/README.md", "content": "## Note by ThinkTwice\nWe have changed/added some log related functions of the leaderboard for easier debug. For example, each simulated route would have a index in its folder name. For all infractions, the location and timestep would be recorded in the json file.\n\n## Original README.md\n\nThe main goal of the CARLA Autonomous Driving Leaderboard is to evaluate the driving proficiency of autonomous agents in realistic traffic situations. The leaderboard serves as an open platform for the community to perform fair and reproducible evaluations, simplifying the comparison between different approaches.\n\nAutonomous agents have to drive through a set of predefined routes. For each route, agents are initialized at a starting point and have to drive to a destination point. The agents will be provided with a description of the route. Routes will happen in a variety of areas, including freeways, urban scenes, and residential districts.\n\nAgents will face multiple traffic situations based in the NHTSA typology, such as:\n\n* Lane merging\n* Lane changing\n* Negotiations at traffic intersections\n* Negotiations at roundabouts\n* Handling traffic lights and traffic signs\n* Coping with pedestrians, cyclists and other elements\n\nThe user can change the weather of the simulation, allowing the evaluation of the agent in a variety of weather conditions, including daylight scenes, sunset, rain, fog, and night, among others.\n\nMore information can be found [here](https://leaderboard.carla.org/)" }, { "path": "leaderboard/data/routes_for_evaluation/routes_longest6.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_evaluation/routes_town05_long.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town01_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town02_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town03_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town04_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town05_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town06_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town07_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_00.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n " }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_01.xml", "content": "\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_02.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_03.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_04.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_05.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_06.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_07.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_08.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_09.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_10.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_11.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/routes_for_open_loop_training/routes_town10_val.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "leaderboard/data/scenarios/all_towns_traffic_scenarios_no256.json", "content": "{\r\n \"available_scenarios\": [\r\n {\r\n \"Town01\": [\r\n {\r\n \"available_event_configurations\": [\r\n {\r\n \"transform\": {\r\n \"pitch\": \"0\",\r\n \"x\": \"319.64\",\r\n \"y\": \"-2.20\",\r\n \"yaw\": \"180\",\r\n \"z\": \"1.0\"\r\n }\r\n },\r\n {\r\n \"transform\": {\r\n \"pitch\": \"0\",\r\n \"x\": \"107.98\",\r\n \"y\": 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\"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.68631362915039\",\n \"y\": \"250.11334228515625\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-6.65\",\n \"y\": \"208.21\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n },\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.15\",\n \"y\": \"208.25\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.7227668762207\",\n \"y\": \"239.6134033203125\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-6.69\",\n \"y\": \"208.32\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n },\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.19\",\n \"y\": \"208.37\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": -35.7,\n \"y\": 246.7,\n \"yaw\": 359.0,\n \"z\": 1.0\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-6.69\",\n \"y\": \"208.32\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n },\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.19\",\n \"y\": \"208.37\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.688148498535156\",\n \"y\": \"250.11334228515625\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-6.69\",\n \"y\": \"208.32\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n },\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.19\",\n \"y\": \"208.37\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.71245193481445\",\n \"y\": \"243.11338806152344\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-6.69\",\n \"y\": \"208.32\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n },\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.19\",\n \"y\": \"208.37\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.72460174560547\",\n \"y\": \"239.6134033203125\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.30\",\n \"y\": \"208.77\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": -35.7,\n \"y\": 250.2,\n \"yaw\": 359.0,\n \"z\": 1.0\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.30\",\n \"y\": \"208.77\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.71244812011719\",\n \"y\": \"246.6134033203125\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.30\",\n \"y\": \"208.77\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.72460174560547\",\n \"y\": \"243.11343383789062\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.30\",\n \"y\": \"208.77\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.736751556396484\",\n \"y\": \"239.6134490966797\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.24\",\n \"y\": \"208.23\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": -35.7,\n \"y\": 250.5,\n \"yaw\": 359.0,\n \"z\": 1.0\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.24\",\n \"y\": \"208.23\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.71349334716797\",\n \"y\": \"246.6134033203125\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.24\",\n \"y\": \"208.23\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.72564697265625\",\n \"y\": \"243.11343383789062\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"-10.24\",\n \"y\": \"208.23\",\n \"yaw\": \"89.27478\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0.0\",\n \"x\": \"-35.737796783447266\",\n \"y\": \"239.6134490966797\",\n \"yaw\": \"-0.19892168045043945\",\n \"z\": \"0.0\"\n }\n }\n ],\n \"scenario_type\": \"Scenario9\"\n },\n {\n \"available_event_configurations\": [],\n \"scenario_type\": \"Scenario10\"\n }\n ]\n }\n ]\n}" }, { "path": "leaderboard/leaderboard/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/autoagents/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/autoagents/agent_wrapper.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nWrapper for autonomous agents required for tracking and checking of used sensors\n\"\"\"\n\nfrom __future__ import print_function\nimport math\nimport os\nimport time\n\nimport carla\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\nfrom leaderboard.envs.sensor_interface import CallBack, OpenDriveMapReader, SpeedometerReader, SensorConfigurationInvalid\nfrom leaderboard.autoagents.autonomous_agent import Track\n\nMAX_ALLOWED_RADIUS_SENSOR = 3.0\nMAX_ALLOWED_RADIUS_SENSOR = 1000.0 # increased for topdown map generation\n\nSENSORS_LIMITS = {\n 'sensor.camera.rgb': 10,\n 'sensor.camera.semantic_segmentation': 10,\n 'sensor.camera.depth': 10,\n 'sensor.lidar.ray_cast': 1,\n 'sensor.lidar.ray_cast_semantic': 1,\n 'sensor.other.radar': 2,\n 'sensor.other.gnss': 1,\n 'sensor.other.imu': 1,\n 'sensor.opendrive_map': 1,\n 'sensor.speedometer': 1\n}\n\n\nclass AgentError(Exception):\n \"\"\"\n Exceptions thrown when the agent returns an error during the simulation\n \"\"\"\n\n def __init__(self, message):\n super(AgentError, self).__init__(message)\n\n\nclass AgentWrapper(object):\n\n \"\"\"\n Wrapper for autonomous agents required for tracking and checking of used sensors\n \"\"\"\n\n allowed_sensors = [\n 'sensor.opendrive_map',\n 'sensor.speedometer',\n 'sensor.camera.rgb',\n 'sensor.camera.semantic_segmentation',\n 'sensor.camera.depth',\n 'sensor.camera',\n 'sensor.lidar.ray_cast',\n 'sensor.lidar.ray_cast_semantic',\n 'sensor.other.radar',\n 'sensor.other.gnss',\n 'sensor.other.imu'\n ]\n\n _agent = None\n _sensors_list = []\n\n def __init__(self, agent):\n \"\"\"\n Set the autonomous agent\n \"\"\"\n self._agent = agent\n\n def __call__(self):\n \"\"\"\n Pass the call directly to the agent\n \"\"\"\n return self._agent()\n\n def setup_sensors(self, vehicle, debug_mode=False):\n \"\"\"\n Create the sensors defined by the user and attach them to the ego-vehicle\n :param vehicle: ego vehicle\n :return:\n \"\"\"\n bp_library = CarlaDataProvider.get_world().get_blueprint_library()\n for sensor_spec in self._agent.sensors():\n # These are the pseudosensors (not spawned)\n if sensor_spec['type'].startswith('sensor.opendrive_map'):\n # The HDMap pseudo sensor is created directly here\n sensor = OpenDriveMapReader(vehicle, sensor_spec['reading_frequency'])\n elif sensor_spec['type'].startswith('sensor.speedometer'):\n delta_time = CarlaDataProvider.get_world().get_settings().fixed_delta_seconds\n frame_rate = 1 / delta_time\n sensor = SpeedometerReader(vehicle, frame_rate)\n # These are the sensors spawned on the carla world\n else:\n bp = bp_library.find(str(sensor_spec['type']))\n if sensor_spec['type'].startswith('sensor.camera.semantic_segmentation'):\n bp.set_attribute('image_size_x', str(sensor_spec['width']))\n bp.set_attribute('image_size_y', str(sensor_spec['height']))\n bp.set_attribute('fov', str(sensor_spec['fov']))\n bp.set_attribute('lens_circle_multiplier', str(3.0))\n bp.set_attribute('lens_circle_falloff', str(3.0))\n\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.camera.depth'):\n bp.set_attribute('image_size_x', str(sensor_spec['width']))\n bp.set_attribute('image_size_y', str(sensor_spec['height']))\n bp.set_attribute('fov', str(sensor_spec['fov']))\n bp.set_attribute('lens_circle_multiplier', str(3.0))\n bp.set_attribute('lens_circle_falloff', str(3.0))\n\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.camera'):\n bp.set_attribute('image_size_x', str(sensor_spec['width']))\n bp.set_attribute('image_size_y', str(sensor_spec['height']))\n bp.set_attribute('fov', str(sensor_spec['fov']))\n bp.set_attribute('lens_circle_multiplier', str(3.0))\n bp.set_attribute('lens_circle_falloff', str(3.0))\n bp.set_attribute('chromatic_aberration_intensity', str(0.5))\n bp.set_attribute('chromatic_aberration_offset', str(0))\n\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.lidar.ray_cast_semantic'):\n bp.set_attribute('range', str(85))\n bp.set_attribute('rotation_frequency', str(10)) # default: 10, change to 20 for old lidar models\n bp.set_attribute('channels', str(64))\n bp.set_attribute('upper_fov', str(10))\n bp.set_attribute('lower_fov', str(-30))\n bp.set_attribute('points_per_second', str(600000))\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.lidar'):\n bp.set_attribute('range', str(85))\n bp.set_attribute('rotation_frequency', str(10)) # default: 10, change to 20 to generate 360 degree LiDAR point cloud\n bp.set_attribute('channels', str(64))\n bp.set_attribute('upper_fov', str(10))\n bp.set_attribute('lower_fov', str(-30))\n bp.set_attribute('points_per_second', str(600000))\n bp.set_attribute('atmosphere_attenuation_rate', str(0.004))\n bp.set_attribute('dropoff_general_rate', str(0.45))\n bp.set_attribute('dropoff_intensity_limit', str(0.8))\n bp.set_attribute('dropoff_zero_intensity', str(0.4))\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.other.radar'):\n bp.set_attribute('horizontal_fov', str(sensor_spec['fov'])) # degrees\n bp.set_attribute('vertical_fov', str(sensor_spec['fov'])) # degrees\n bp.set_attribute('points_per_second', '1500')\n bp.set_attribute('range', '100') # meters\n\n sensor_location = carla.Location(x=sensor_spec['x'],\n y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n\n elif sensor_spec['type'].startswith('sensor.other.gnss'):\n # bp.set_attribute('noise_alt_stddev', str(0.000005))\n # bp.set_attribute('noise_lat_stddev', str(0.000005))\n # bp.set_attribute('noise_lon_stddev', str(0.000005))\n bp.set_attribute('noise_alt_bias', str(0.0))\n bp.set_attribute('noise_lat_bias', str(0.0))\n bp.set_attribute('noise_lon_bias', str(0.0))\n\n sensor_location = carla.Location(x=sensor_spec['x'],\n y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation()\n\n elif sensor_spec['type'].startswith('sensor.other.imu'):\n bp.set_attribute('noise_accel_stddev_x', str(0.001))\n bp.set_attribute('noise_accel_stddev_y', str(0.001))\n bp.set_attribute('noise_accel_stddev_z', str(0.015))\n bp.set_attribute('noise_gyro_stddev_x', str(0.001))\n bp.set_attribute('noise_gyro_stddev_y', str(0.001))\n bp.set_attribute('noise_gyro_stddev_z', str(0.001))\n\n sensor_location = carla.Location(x=sensor_spec['x'],\n y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n # create sensor\n sensor_transform = carla.Transform(sensor_location, sensor_rotation)\n sensor = CarlaDataProvider.get_world().spawn_actor(bp, sensor_transform, vehicle)\n # setup callback\n sensor.listen(CallBack(sensor_spec['id'], sensor_spec['type'], sensor, self._agent.sensor_interface))\n self._sensors_list.append(sensor)\n\n # Tick once to spawn the sensors\n CarlaDataProvider.get_world().tick()\n\n\n @staticmethod\n def validate_sensor_configuration(sensors, agent_track, selected_track):\n \"\"\"\n Ensure that the sensor configuration is valid, in case the challenge mode is used\n Returns true on valid configuration, false otherwise\n \"\"\"\n if Track(selected_track) != agent_track:\n raise SensorConfigurationInvalid(\"You are submitting to the wrong track [{}]!\".format(Track(selected_track)))\n\n sensor_count = {}\n sensor_ids = []\n\n for sensor in sensors:\n\n # Check if the is has been already used\n sensor_id = sensor['id']\n if sensor_id in sensor_ids:\n raise SensorConfigurationInvalid(\"Duplicated sensor tag [{}]\".format(sensor_id))\n else:\n sensor_ids.append(sensor_id)\n\n # Check if the sensor is valid\n if agent_track == Track.SENSORS:\n if sensor['type'].startswith('sensor.opendrive_map'):\n raise SensorConfigurationInvalid(\"Illegal sensor used for Track [{}]!\".format(agent_track))\n\n # Check the sensors validity\n if sensor['type'] not in AgentWrapper.allowed_sensors:\n raise SensorConfigurationInvalid(\"Illegal sensor used. {} are not allowed!\".format(sensor['type']))\n\n # Check the extrinsics of the sensor\n if 'x' in sensor and 'y' in sensor and 'z' in sensor:\n if math.sqrt(sensor['x']**2 + sensor['y']**2 + sensor['z']**2) > MAX_ALLOWED_RADIUS_SENSOR:\n raise SensorConfigurationInvalid(\n \"Illegal sensor extrinsics used for Track [{}]!\".format(agent_track))\n\n # Check the amount of sensors\n if sensor['type'] in sensor_count:\n sensor_count[sensor['type']] += 1\n else:\n sensor_count[sensor['type']] = 1\n\n\n for sensor_type, max_instances_allowed in SENSORS_LIMITS.items():\n if sensor_type in sensor_count and sensor_count[sensor_type] > max_instances_allowed:\n raise SensorConfigurationInvalid(\n \"Too many {} used! \"\n \"Maximum number allowed is {}, but {} were requested.\".format(sensor_type,\n max_instances_allowed,\n sensor_count[sensor_type]))\n\n def cleanup(self):\n \"\"\"\n Remove and destroy all sensors\n \"\"\"\n for i, _ in enumerate(self._sensors_list):\n if self._sensors_list[i] is not None:\n self._sensors_list[i].stop()\n self._sensors_list[i].destroy()\n self._sensors_list[i] = None\n self._sensors_list = []\n" }, { "path": "leaderboard/leaderboard/autoagents/autonomous_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the base class for all autonomous agents\n\"\"\"\n\nfrom __future__ import print_function\n\nfrom enum import Enum\n\nimport carla\nfrom srunner.scenariomanager.timer import GameTime\n\nfrom leaderboard.utils.route_manipulation import downsample_route\nfrom leaderboard.envs.sensor_interface import SensorInterface\n\n\nclass Track(Enum):\n\n \"\"\"\n This enum represents the different tracks of the CARLA AD leaderboard.\n \"\"\"\n SENSORS = 'SENSORS'\n MAP = 'MAP'\n\nclass AutonomousAgent(object):\n\n \"\"\"\n Autonomous agent base class. All user agents have to be derived from this class\n \"\"\"\n\n def __init__(self, path_to_conf_file):\n self.track = Track.SENSORS\n # current global plans to reach a destination\n self._global_plan = None\n self._global_plan_world_coord = None\n\n # this data structure will contain all sensor data\n self.sensor_interface = SensorInterface()\n\n # agent's initialization\n self.setup(path_to_conf_file)\n\n self.wallclock_t0 = None\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Initialize everything needed by your agent and set the track attribute to the right type:\n Track.SENSORS : CAMERAS, LIDAR, RADAR, GPS and IMU sensors are allowed\n Track.MAP : OpenDRIVE map is also allowed\n \"\"\"\n pass\n\n def sensors(self): # pylint: disable=no-self-use\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n\n \"\"\"\n sensors = []\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n :return: control\n \"\"\"\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n return control\n\n def destroy(self):\n \"\"\"\n Destroy (clean-up) the agent\n :return:\n \"\"\"\n pass\n\n def __call__(self):\n \"\"\"\n Execute the agent call, e.g. agent()\n Returns the next vehicle controls\n \"\"\"\n input_data = self.sensor_interface.get_data()\n\n timestamp = GameTime.get_time()\n\n if not self.wallclock_t0:\n self.wallclock_t0 = GameTime.get_wallclocktime()\n wallclock = GameTime.get_wallclocktime()\n wallclock_diff = (wallclock - self.wallclock_t0).total_seconds()\n\n # print('======[Agent] Wallclock_time = {} / {} / Sim_time = {} / {}x'.format(wallclock, wallclock_diff, timestamp, timestamp/(wallclock_diff+0.001)))\n\n control = self.run_step(input_data, timestamp)\n control.manual_gear_shift = False\n\n return control\n\n def set_global_plan(self, global_plan_gps, global_plan_world_coord, wp=None):\n \"\"\"\n Set the plan (route) for the agent\n \"\"\"\n ds_ids = downsample_route(global_plan_world_coord, 50)\n self._global_plan_world_coord = [(global_plan_world_coord[x][0], global_plan_world_coord[x][1]) for x in ds_ids]\n self._global_plan = [global_plan_gps[x] for x in ds_ids]\n" }, { "path": "leaderboard/leaderboard/autoagents/dummy_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a dummy agent to control the ego vehicle\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\n\nfrom leaderboard.autoagents.autonomous_agent import AutonomousAgent, Track\n\ndef get_entry_point():\n return 'DummyAgent'\n\nclass DummyAgent(AutonomousAgent):\n\n \"\"\"\n Dummy autonomous agent to control the ego vehicle\n \"\"\"\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n self.track = Track.MAP\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n \"\"\"\n\n sensors = [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Center'},\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': -45.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Left'},\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 45.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Right'},\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'id': 'LIDAR'},\n {'type': 'sensor.other.radar', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'fov': 30, 'id': 'RADAR'},\n {'type': 'sensor.other.gnss', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'id': 'GPS'},\n {'type': 'sensor.other.imu', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'id': 'IMU'},\n {'type': 'sensor.opendrive_map', 'reading_frequency': 1, 'id': 'OpenDRIVE'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n print(\"=====================>\")\n for key, val in input_data.items():\n if hasattr(val[1], 'shape'):\n shape = val[1].shape\n print(\"[{} -- {:06d}] with shape {}\".format(key, val[0], shape))\n else:\n print(\"[{} -- {:06d}] \".format(key, val[0]))\n print(\"<=====================\")\n\n # DO SOMETHING SMART\n\n # RETURN CONTROL\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n return control\n" }, { "path": "leaderboard/leaderboard/autoagents/human_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a human agent to control the ego vehicle via keyboard\n\"\"\"\n\nimport time\nimport json\nfrom threading import Thread\nimport cv2\nimport numpy as np\n\ntry:\n import pygame\n from pygame.locals import K_DOWN\n from pygame.locals import K_LEFT\n from pygame.locals import K_RIGHT\n from pygame.locals import K_SPACE\n from pygame.locals import K_UP\n from pygame.locals import K_a\n from pygame.locals import K_d\n from pygame.locals import K_s\n from pygame.locals import K_w\n from pygame.locals import K_q\nexcept ImportError:\n raise RuntimeError('cannot import pygame, make sure pygame package is installed')\n\nimport carla\n\nfrom leaderboard.autoagents.autonomous_agent import AutonomousAgent, Track\n\n\ndef get_entry_point():\n return 'HumanAgent'\n\nclass HumanInterface(object):\n\n \"\"\"\n Class to control a vehicle manually for debugging purposes\n \"\"\"\n\n def __init__(self):\n self._width = 800\n self._height = 600\n self._surface = None\n\n pygame.init()\n pygame.font.init()\n self._clock = pygame.time.Clock()\n self._display = pygame.display.set_mode((self._width, self._height), pygame.HWSURFACE | pygame.DOUBLEBUF)\n pygame.display.set_caption(\"Human Agent\")\n\n def run_interface(self, input_data):\n \"\"\"\n Run the GUI\n \"\"\"\n\n # process sensor data\n image_center = input_data['Center'][1][:, :, -2::-1]\n\n # display image\n self._surface = pygame.surfarray.make_surface(image_center.swapaxes(0, 1))\n if self._surface is not None:\n self._display.blit(self._surface, (0, 0))\n pygame.display.flip()\n\n def _quit(self):\n pygame.quit()\n\n\nclass HumanAgent(AutonomousAgent):\n\n \"\"\"\n Human agent to control the ego vehicle via keyboard\n \"\"\"\n\n current_control = None\n agent_engaged = False\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n self.track = Track.SENSORS\n\n self.agent_engaged = False\n self._hic = HumanInterface()\n self._controller = KeyboardControl(path_to_conf_file)\n self._prev_timestamp = 0\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n \"\"\"\n\n sensors = [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Center'},\n {'type': 'sensor.speedometer', 'reading_frequency': 20, 'id': 'speed'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n self.agent_engaged = True\n self._hic.run_interface(input_data)\n\n control = self._controller.parse_events(timestamp - self._prev_timestamp)\n self._prev_timestamp = timestamp\n\n return control\n\n def destroy(self):\n \"\"\"\n Cleanup\n \"\"\"\n self._hic._quit = True\n\n\nclass KeyboardControl(object):\n\n \"\"\"\n Keyboard control for the human agent\n \"\"\"\n\n def __init__(self, path_to_conf_file):\n \"\"\"\n Init\n \"\"\"\n self._control = carla.VehicleControl()\n self._steer_cache = 0.0\n self._clock = pygame.time.Clock()\n\n # Get the mode\n if path_to_conf_file:\n\n with (open(path_to_conf_file, \"r\")) as f:\n lines = f.read().split(\"\\n\")\n self._mode = lines[0].split(\" \")[1]\n self._endpoint = lines[1].split(\" \")[1]\n\n # Get the needed vars\n if self._mode == \"log\":\n self._log_data = {'records': []}\n\n elif self._mode == \"playback\":\n self._index = 0\n self._control_list = []\n\n with open(self._endpoint) as fd:\n try:\n self._records = json.load(fd)\n self._json_to_control()\n except json.JSONDecodeError:\n pass\n else:\n self._mode = \"normal\"\n self._endpoint = None\n\n def _json_to_control(self):\n\n # transform strs into VehicleControl commands\n for entry in self._records['records']:\n control = carla.VehicleControl(throttle=entry['control']['throttle'],\n steer=entry['control']['steer'],\n brake=entry['control']['brake'],\n hand_brake=entry['control']['hand_brake'],\n reverse=entry['control']['reverse'],\n manual_gear_shift=entry['control']['manual_gear_shift'],\n gear=entry['control']['gear'])\n self._control_list.append(control)\n\n def parse_events(self, timestamp):\n \"\"\"\n Parse the keyboard events and set the vehicle controls accordingly\n \"\"\"\n # Move the vehicle\n if self._mode == \"playback\":\n self._parse_json_control()\n else:\n self._parse_vehicle_keys(pygame.key.get_pressed(), timestamp*1000)\n\n # Record the control\n if self._mode == \"log\":\n self._record_control()\n\n return self._control\n\n def _parse_vehicle_keys(self, keys, milliseconds):\n \"\"\"\n Calculate new vehicle controls based on input keys\n \"\"\"\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return \n elif event.type == pygame.KEYUP:\n if event.key == K_q:\n self._control.gear = 1 if self._control.reverse else -1\n self._control.reverse = self._control.gear < 0\n\n if keys[K_UP] or keys[K_w]:\n self._control.throttle = 0.6\n else:\n self._control.throttle = 0.0\n\n steer_increment = 3e-4 * milliseconds\n if keys[K_LEFT] or keys[K_a]:\n self._steer_cache -= steer_increment\n elif keys[K_RIGHT] or keys[K_d]:\n self._steer_cache += steer_increment\n else:\n self._steer_cache = 0.0\n\n steer_cache = min(0.95, max(-0.95, self._steer_cache))\n self._control.steer = round(self._steer_cache, 1)\n self._control.brake = 1.0 if keys[K_DOWN] or keys[K_s] else 0.0\n self._control.hand_brake = keys[K_SPACE]\n\n def _parse_json_control(self):\n\n if self._index < len(self._control_list):\n self._control = self._control_list[self._index]\n self._index += 1\n else:\n print(\"JSON file has no more entries\")\n\n def _record_control(self):\n new_record = {\n 'control': {\n 'throttle': self._control.throttle,\n 'steer': self._control.steer,\n 'brake': self._control.brake,\n 'hand_brake': self._control.hand_brake,\n 'reverse': self._control.reverse,\n 'manual_gear_shift': self._control.manual_gear_shift,\n 'gear': self._control.gear\n }\n }\n\n self._log_data['records'].append(new_record)\n\n def __del__(self):\n # Get ready to log user commands\n if self._mode == \"log\" and self._log_data:\n with open(self._endpoint, 'w') as fd:\n json.dump(self._log_data, fd, indent=4, sort_keys=True)\n" }, { "path": "leaderboard/leaderboard/autoagents/human_agent_config.txt", "content": "mode: log # Either 'log' or 'playback'\nfile: test.json # path to the file with the controls\n\n\nThis is the configuration file of the human agent. This agent incorporates two modes.\nThe log mode parses the controls given to the vehicle into a dictionary and records them into a .json file.\nThis file can be read by the playback mode to control the vehicle in the same way, resulting in a deterministic agent.\nThe file name can be chosen, and is the second argument of this config file.\n\n" }, { "path": "leaderboard/leaderboard/autoagents/npc_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an NPC agent to control the ego vehicle\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\nfrom agents.navigation.basic_agent import BasicAgent\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\nfrom leaderboard.autoagents.autonomous_agent import AutonomousAgent, Track\n\ndef get_entry_point():\n return 'NpcAgent'\n\nclass NpcAgent(AutonomousAgent):\n\n \"\"\"\n NPC autonomous agent to control the ego vehicle\n \"\"\"\n\n _agent = None\n _route_assigned = False\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n self.track = Track.SENSORS\n\n self._route_assigned = False\n self._agent = None\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n \"\"\"\n\n sensors = [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n if not self._agent:\n hero_actor = None\n for actor in CarlaDataProvider.get_world().get_actors():\n if 'role_name' in actor.attributes and actor.attributes['role_name'] == 'hero':\n hero_actor = actor\n break\n if hero_actor:\n self._agent = BasicAgent(hero_actor)\n\n return control\n\n if not self._route_assigned:\n if self._global_plan:\n plan = []\n\n prev = None\n for transform, _ in self._global_plan_world_coord:\n wp = CarlaDataProvider.get_map().get_waypoint(transform.location)\n if prev:\n route_segment = self._agent._trace_route(prev, wp)\n plan.extend(route_segment)\n\n prev = wp\n\n #loc = plan[-1][0].transform.location\n #self._agent.set_destination([loc.x, loc.y, loc.z])\n self._agent._local_planner.set_global_plan(plan) # pylint: disable=protected-access\n self._route_assigned = True\n\n else:\n control = self._agent.run_step()\n\n return control\n" }, { "path": "leaderboard/leaderboard/autoagents/ros_agent.py", "content": "#!/usr/bin/env python\n#\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n#\n\n\"\"\"\nThis module provides a ROS autonomous agent interface to control the ego vehicle via a ROS stack\n\"\"\"\n\nimport math\nimport os\nimport subprocess\nimport signal\nimport threading\nimport time\n\nimport numpy\n\nimport carla\n\nimport rospy\nfrom cv_bridge import CvBridge\nfrom geometry_msgs.msg import PoseStamped\nfrom nav_msgs.msg import Odometry, Path\nfrom rosgraph_msgs.msg import Clock\nfrom sensor_msgs.msg import Image, PointCloud2, NavSatFix, NavSatStatus, CameraInfo\nfrom sensor_msgs.point_cloud2 import create_cloud_xyz32\nfrom std_msgs.msg import Header, String\nimport tf\n# pylint: disable=line-too-long\nfrom carla_msgs.msg import CarlaEgoVehicleStatus, CarlaEgoVehicleInfo, CarlaEgoVehicleInfoWheel, CarlaEgoVehicleControl, CarlaWorldInfo\n# pylint: enable=line-too-long\n\nfrom leaderboard.autoagents.autonomous_agent import AutonomousAgent, Track\n\n\nclass RosAgent(AutonomousAgent):\n\n \"\"\"\n Base class for ROS-based stacks.\n\n Derive from it and implement the sensors() method.\n\n Please define TEAM_CODE_ROOT in your environment.\n The stack is started by executing $TEAM_CODE_ROOT/start.sh\n\n The sensor data is published on similar topics as with the carla-ros-bridge. You can find details about\n the utilized datatypes there.\n\n This agent expects a roscore to be running.\n \"\"\"\n\n speed = None\n current_control = None\n stack_process = None\n timestamp = None\n current_map_name = None\n step_mode_possible = None\n vehicle_info_publisher = None\n global_plan_published = None\n\n def setup(self, path_to_conf_file):\n \"\"\"\n setup agent\n \"\"\"\n self.track = Track.MAP\n self.stack_thread = None\n\n # get start_script from environment\n team_code_path = os.environ['TEAM_CODE_ROOT']\n if not team_code_path or not os.path.exists(team_code_path):\n raise IOError(\"Path '{}' defined by TEAM_CODE_ROOT invalid\".format(team_code_path))\n start_script = \"{}/start.sh\".format(team_code_path)\n if not os.path.exists(start_script):\n raise IOError(\"File '{}' defined by TEAM_CODE_ROOT invalid\".format(start_script))\n\n # set use_sim_time via commandline before init-node\n process = subprocess.Popen(\n \"rosparam set use_sim_time true\", shell=True, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\n process.wait()\n if process.returncode:\n raise RuntimeError(\"Could not set use_sim_time\")\n\n # initialize ros node\n rospy.init_node('ros_agent', anonymous=True)\n\n # publish first clock value '0'\n self.clock_publisher = rospy.Publisher('clock', Clock, queue_size=10, latch=True)\n self.clock_publisher.publish(Clock(rospy.Time.from_sec(0)))\n\n # execute script that starts the ad stack (remains running)\n rospy.loginfo(\"Executing stack...\")\n self.stack_process = subprocess.Popen(start_script, shell=True, preexec_fn=os.setpgrp)\n\n self.vehicle_control_event = threading.Event()\n self.timestamp = None\n self.speed = 0\n self.global_plan_published = False\n\n self.vehicle_info_publisher = None\n self.vehicle_status_publisher = None\n self.odometry_publisher = None\n self.world_info_publisher = None\n self.map_file_publisher = None\n self.current_map_name = None\n self.tf_broadcaster = None\n self.step_mode_possible = False\n\n self.vehicle_control_subscriber = rospy.Subscriber(\n '/carla/ego_vehicle/vehicle_control_cmd', CarlaEgoVehicleControl, self.on_vehicle_control)\n\n self.current_control = carla.VehicleControl()\n\n self.waypoint_publisher = rospy.Publisher(\n '/carla/ego_vehicle/waypoints', Path, queue_size=1, latch=True)\n\n self.publisher_map = {}\n self.id_to_sensor_type_map = {}\n self.id_to_camera_info_map = {}\n self.cv_bridge = CvBridge()\n\n # setup ros publishers for sensors\n # pylint: disable=line-too-long\n for sensor in self.sensors():\n self.id_to_sensor_type_map[sensor['id']] = sensor['type']\n if sensor['type'] == 'sensor.camera.rgb':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/camera/rgb/' + sensor['id'] + \"/image_color\", Image, queue_size=1, latch=True)\n self.id_to_camera_info_map[sensor['id']] = self.build_camera_info(sensor)\n self.publisher_map[sensor['id'] + '_info'] = rospy.Publisher(\n '/carla/ego_vehicle/camera/rgb/' + sensor['id'] + \"/camera_info\", CameraInfo, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.lidar.ray_cast':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/lidar/' + sensor['id'] + \"/point_cloud\", PointCloud2, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.other.gnss':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/gnss/' + sensor['id'] + \"/fix\", NavSatFix, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.can_bus':\n if not self.vehicle_info_publisher:\n self.vehicle_info_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_info', CarlaEgoVehicleInfo, queue_size=1, latch=True)\n if not self.vehicle_status_publisher:\n self.vehicle_status_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_status', CarlaEgoVehicleStatus, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.hd_map':\n if not self.odometry_publisher:\n self.odometry_publisher = rospy.Publisher(\n '/carla/ego_vehicle/odometry', Odometry, queue_size=1, latch=True)\n if not self.world_info_publisher:\n self.world_info_publisher = rospy.Publisher(\n '/carla/world_info', CarlaWorldInfo, queue_size=1, latch=True)\n if not self.map_file_publisher:\n self.map_file_publisher = rospy.Publisher('/carla/map_file', String, queue_size=1, latch=True)\n if not self.tf_broadcaster:\n self.tf_broadcaster = tf.TransformBroadcaster()\n else:\n raise TypeError(\"Invalid sensor type: {}\".format(sensor['type']))\n # pylint: enable=line-too-long\n\n def destroy(self):\n \"\"\"\n Cleanup of all ROS publishers\n \"\"\"\n if self.stack_process and self.stack_process.poll() is None:\n rospy.loginfo(\"Sending SIGTERM to stack...\")\n os.killpg(os.getpgid(self.stack_process.pid), signal.SIGTERM)\n rospy.loginfo(\"Waiting for termination of stack...\")\n self.stack_process.wait()\n time.sleep(5)\n rospy.loginfo(\"Terminated stack.\")\n\n rospy.loginfo(\"Stack is no longer running\")\n self.world_info_publisher.unregister()\n self.map_file_publisher.unregister()\n self.vehicle_status_publisher.unregister()\n self.vehicle_info_publisher.unregister()\n self.waypoint_publisher.unregister()\n self.stack_process = None\n rospy.loginfo(\"Cleanup finished\")\n\n def on_vehicle_control(self, data):\n \"\"\"\n callback if a new vehicle control command is received\n \"\"\"\n cmd = carla.VehicleControl()\n cmd.throttle = data.throttle\n cmd.steer = data.steer\n cmd.brake = data.brake\n cmd.hand_brake = data.hand_brake\n cmd.reverse = data.reverse\n cmd.gear = data.gear\n cmd.manual_gear_shift = data.manual_gear_shift\n self.current_control = cmd\n if not self.vehicle_control_event.is_set():\n self.vehicle_control_event.set()\n # After the first vehicle control is sent out, it is possible to use the stepping mode\n self.step_mode_possible = True\n\n def build_camera_info(self, attributes): # pylint: disable=no-self-use\n \"\"\"\n Private function to compute camera info\n\n camera info doesn't change over time\n \"\"\"\n camera_info = CameraInfo()\n # store info without header\n camera_info.header = None\n camera_info.width = int(attributes['width'])\n camera_info.height = int(attributes['height'])\n camera_info.distortion_model = 'plumb_bob'\n cx = camera_info.width / 2.0\n cy = camera_info.height / 2.0\n fx = camera_info.width / (\n 2.0 * math.tan(float(attributes['fov']) * math.pi / 360.0))\n fy = fx\n camera_info.K = [fx, 0, cx, 0, fy, cy, 0, 0, 1]\n camera_info.D = [0, 0, 0, 0, 0]\n camera_info.R = [1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]\n camera_info.P = [fx, 0, cx, 0, 0, fy, cy, 0, 0, 0, 1.0, 0]\n return camera_info\n\n def publish_plan(self):\n \"\"\"\n publish the global plan\n \"\"\"\n msg = Path()\n msg.header.frame_id = \"/map\"\n msg.header.stamp = rospy.Time.now()\n for wp in self._global_plan_world_coord:\n pose = PoseStamped()\n pose.pose.position.x = wp[0].location.x\n pose.pose.position.y = -wp[0].location.y\n pose.pose.position.z = wp[0].location.z\n quaternion = tf.transformations.quaternion_from_euler(\n 0, 0, -math.radians(wp[0].rotation.yaw))\n pose.pose.orientation.x = quaternion[0]\n pose.pose.orientation.y = quaternion[1]\n pose.pose.orientation.z = quaternion[2]\n pose.pose.orientation.w = quaternion[3]\n msg.poses.append(pose)\n\n rospy.loginfo(\"Publishing Plan...\")\n self.waypoint_publisher.publish(msg)\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors\n \"\"\"\n raise NotImplementedError(\n \"This function has to be implemented by the derived classes\")\n\n def get_header(self):\n \"\"\"\n Returns ROS message header\n \"\"\"\n header = Header()\n header.stamp = rospy.Time.from_sec(self.timestamp)\n return header\n\n def publish_lidar(self, sensor_id, data):\n \"\"\"\n Function to publish lidar data\n \"\"\"\n header = self.get_header()\n header.frame_id = 'ego_vehicle/lidar/{}'.format(sensor_id)\n\n lidar_data = numpy.frombuffer(\n data, dtype=numpy.float32)\n lidar_data = numpy.reshape(\n lidar_data, (int(lidar_data.shape[0] / 3), 3))\n # we take the oposite of y axis\n # (as lidar point are express in left handed coordinate system, and ros need right handed)\n # we need a copy here, because the data are read only in carla numpy\n # array\n lidar_data = -1.0 * lidar_data\n # we also need to permute x and y\n lidar_data = lidar_data[..., [1, 0, 2]]\n msg = create_cloud_xyz32(header, lidar_data)\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_gnss(self, sensor_id, data):\n \"\"\"\n Function to publish gnss data\n \"\"\"\n msg = NavSatFix()\n msg.header = self.get_header()\n msg.header.frame_id = 'gps'\n msg.latitude = data[0]\n msg.longitude = data[1]\n msg.altitude = data[2]\n msg.status.status = NavSatStatus.STATUS_SBAS_FIX\n # pylint: disable=line-too-long\n msg.status.service = NavSatStatus.SERVICE_GPS | NavSatStatus.SERVICE_GLONASS | NavSatStatus.SERVICE_COMPASS | NavSatStatus.SERVICE_GALILEO\n # pylint: enable=line-too-long\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_camera(self, sensor_id, data):\n \"\"\"\n Function to publish camera data\n \"\"\"\n msg = self.cv_bridge.cv2_to_imgmsg(data, encoding='bgra8')\n # the camera data is in respect to the camera's own frame\n msg.header = self.get_header()\n msg.header.frame_id = 'ego_vehicle/camera/rgb/{}'.format(sensor_id)\n\n cam_info = self.id_to_camera_info_map[sensor_id]\n cam_info.header = msg.header\n self.publisher_map[sensor_id + '_info'].publish(cam_info)\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_can(self, sensor_id, data):\n \"\"\"\n publish can data\n \"\"\"\n if not self.vehicle_info_publisher:\n self.vehicle_info_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_info', CarlaEgoVehicleInfo, queue_size=1, latch=True)\n info_msg = CarlaEgoVehicleInfo()\n for wheel in data['wheels']:\n wheel_info = CarlaEgoVehicleInfoWheel()\n wheel_info.tire_friction = wheel['tire_friction']\n wheel_info.damping_rate = wheel['damping_rate']\n wheel_info.steer_angle = wheel['steer_angle']\n wheel_info.disable_steering = wheel['disable_steering']\n info_msg.wheels.append(wheel_info)\n info_msg.max_rpm = data['max_rpm']\n info_msg.moi = data['moi']\n info_msg.damping_rate_full_throttle = data['damping_rate_full_throttle']\n info_msg.damping_rate_zero_throttle_clutch_disengaged = data['damping_rate_zero_throttle_clutch_disengaged']\n info_msg.use_gear_autobox = data['use_gear_autobox']\n info_msg.clutch_strength = data['clutch_strength']\n info_msg.mass = data['mass']\n info_msg.drag_coefficient = data['drag_coefficient']\n info_msg.center_of_mass.x = data['center_of_mass']['x']\n info_msg.center_of_mass.y = data['center_of_mass']['y']\n info_msg.center_of_mass.z = data['center_of_mass']['z']\n self.vehicle_info_publisher.publish(info_msg)\n msg = CarlaEgoVehicleStatus()\n msg.header = self.get_header()\n msg.velocity = data['speed']\n self.speed = data['speed']\n # msg.acceleration\n msg.control.throttle = self.current_control.throttle\n msg.control.steer = self.current_control.steer\n msg.control.brake = self.current_control.brake\n msg.control.hand_brake = self.current_control.hand_brake\n msg.control.reverse = self.current_control.reverse\n msg.control.gear = self.current_control.gear\n msg.control.manual_gear_shift = self.current_control.manual_gear_shift\n\n self.vehicle_status_publisher.publish(msg)\n\n def publish_hd_map(self, sensor_id, data):\n \"\"\"\n publish hd map data\n \"\"\"\n roll = -math.radians(data['transform']['roll'])\n pitch = -math.radians(data['transform']['pitch'])\n yaw = -math.radians(data['transform']['yaw'])\n quat = tf.transformations.quaternion_from_euler(roll, pitch, yaw)\n x = data['transform']['x']\n y = -data['transform']['y']\n z = data['transform']['z']\n\n if self.odometry_publisher:\n\n odometry = Odometry()\n odometry.header.frame_id = 'map'\n odometry.header.stamp = rospy.Time.from_sec(self.timestamp)\n odometry.child_frame_id = 'base_link'\n odometry.pose.pose.position.x = x\n odometry.pose.pose.position.y = y\n odometry.pose.pose.position.z = z\n\n odometry.pose.pose.orientation.x = quat[0]\n odometry.pose.pose.orientation.y = quat[1]\n odometry.pose.pose.orientation.z = quat[2]\n odometry.pose.pose.orientation.w = quat[3]\n\n odometry.twist.twist.linear.x = self.speed\n odometry.twist.twist.linear.y = 0\n odometry.twist.twist.linear.z = 0\n\n self.odometry_publisher.publish(odometry)\n\n if self.world_info_publisher:\n # extract map name\n map_name = os.path.basename(data['map_file'])[:-4]\n if self.current_map_name != map_name:\n self.current_map_name = map_name\n world_info = CarlaWorldInfo()\n world_info.map_name = self.current_map_name\n world_info.opendrive = data['opendrive']\n self.world_info_publisher.publish(world_info)\n if self.map_file_publisher:\n self.map_file_publisher.publish(data['map_file'])\n\n def use_stepping_mode(self): # pylint: disable=no-self-use\n \"\"\"\n Overload this function to use stepping mode!\n \"\"\"\n return False\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n self.vehicle_control_event.clear()\n self.timestamp = timestamp\n self.clock_publisher.publish(Clock(rospy.Time.from_sec(timestamp)))\n\n # check if stack is still running\n if self.stack_process and self.stack_process.poll() is not None:\n raise RuntimeError(\"Stack exited with: {} {}\".format(\n self.stack_process.returncode, self.stack_process.communicate()[0]))\n\n # publish global plan to ROS once\n if self._global_plan_world_coord and not self.global_plan_published:\n self.global_plan_published = True\n self.publish_plan()\n\n new_data_available = False\n\n # publish data of all sensors\n for key, val in input_data.items():\n new_data_available = True\n sensor_type = self.id_to_sensor_type_map[key]\n if sensor_type == 'sensor.camera.rgb':\n self.publish_camera(key, val[1])\n elif sensor_type == 'sensor.lidar.ray_cast':\n self.publish_lidar(key, val[1])\n elif sensor_type == 'sensor.other.gnss':\n self.publish_gnss(key, val[1])\n elif sensor_type == 'sensor.can_bus':\n self.publish_can(key, val[1])\n elif sensor_type == 'sensor.hd_map':\n self.publish_hd_map(key, val[1])\n else:\n raise TypeError(\"Invalid sensor type: {}\".format(sensor_type))\n\n if self.use_stepping_mode():\n if self.step_mode_possible and new_data_available:\n self.vehicle_control_event.wait()\n # if the stepping mode is not used or active, there is no need to wait here\n\n return self.current_control\n" }, { "path": "leaderboard/leaderboard/envs/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/envs/sensor_interface.py", "content": "import copy\nimport logging\nimport numpy as np\nimport os\nimport time\nfrom threading import Thread\n\nfrom queue import Queue\nfrom queue import Empty\n\nimport carla\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\n\n\ndef threaded(fn):\n def wrapper(*args, **kwargs):\n thread = Thread(target=fn, args=args, kwargs=kwargs)\n thread.setDaemon(True)\n thread.start()\n\n return thread\n return wrapper\n\n\nclass SensorConfigurationInvalid(Exception):\n \"\"\"\n Exceptions thrown when the sensors used by the agent are not allowed for that specific submissions\n \"\"\"\n\n def __init__(self, message):\n super(SensorConfigurationInvalid, self).__init__(message)\n\n\nclass SensorReceivedNoData(Exception):\n \"\"\"\n Exceptions thrown when the sensors used by the agent take too long to receive data\n \"\"\"\n\n def __init__(self, message):\n super(SensorReceivedNoData, self).__init__(message)\n\n\nclass GenericMeasurement(object):\n def __init__(self, data, frame):\n self.data = data\n self.frame = frame\n\n\nclass BaseReader(object):\n def __init__(self, vehicle, reading_frequency=1.0):\n self._vehicle = vehicle\n self._reading_frequency = reading_frequency\n self._callback = None\n self._run_ps = True\n self.run()\n\n def __call__(self):\n pass\n\n @threaded\n def run(self):\n first_time = True\n latest_time = GameTime.get_time()\n while self._run_ps:\n if self._callback is not None:\n current_time = GameTime.get_time()\n\n # Second part forces the sensors to send data at the first tick, regardless of frequency\n if current_time - latest_time > (1 / self._reading_frequency) \\\n or (first_time and GameTime.get_frame() != 0):\n self._callback(GenericMeasurement(self.__call__(), GameTime.get_frame()))\n latest_time = GameTime.get_time()\n first_time = False\n\n else:\n time.sleep(0.001)\n\n def listen(self, callback):\n # Tell that this function receives what the producer does.\n self._callback = callback\n\n def stop(self):\n self._run_ps = False\n\n def destroy(self):\n self._run_ps = False\n\n\nclass SpeedometerReader(BaseReader):\n \"\"\"\n Sensor to measure the speed of the vehicle.\n \"\"\"\n MAX_CONNECTION_ATTEMPTS = 10\n\n def _get_forward_speed(self, transform=None, velocity=None):\n \"\"\" Convert the vehicle transform directly to forward speed \"\"\"\n if not velocity:\n velocity = self._vehicle.get_velocity()\n if not transform:\n transform = self._vehicle.get_transform()\n\n vel_np = np.array([velocity.x, velocity.y, velocity.z])\n pitch = np.deg2rad(transform.rotation.pitch)\n yaw = np.deg2rad(transform.rotation.yaw)\n orientation = np.array([np.cos(pitch) * np.cos(yaw), np.cos(pitch) * np.sin(yaw), np.sin(pitch)])\n speed = np.dot(vel_np, orientation)\n return speed\n\n def __call__(self):\n \"\"\" We convert the vehicle physics information into a convenient dictionary \"\"\"\n\n # protect this access against timeout\n attempts = 0\n while attempts < self.MAX_CONNECTION_ATTEMPTS:\n try:\n velocity = self._vehicle.get_velocity()\n transform = self._vehicle.get_transform()\n break\n except Exception:\n attempts += 1\n time.sleep(0.2)\n continue\n\n return {'speed': self._get_forward_speed(transform=transform, velocity=velocity)}\n\n\nclass OpenDriveMapReader(BaseReader):\n def __call__(self):\n return {'opendrive': CarlaDataProvider.get_map().to_opendrive()}\n\n\nclass CallBack(object):\n def __init__(self, tag, sensor_type, sensor, data_provider):\n self._tag = tag\n self._data_provider = data_provider\n\n self._data_provider.register_sensor(tag, sensor_type, sensor)\n\n def __call__(self, data):\n if isinstance(data, carla.libcarla.Image):\n self._parse_image_cb(data, self._tag)\n elif isinstance(data, carla.libcarla.LidarMeasurement):\n self._parse_lidar_cb(data, self._tag)\n elif isinstance(data, carla.libcarla.SemanticLidarMeasurement):\n self._parse_semantic_lidar_cb(data, self._tag)\n elif isinstance(data, carla.libcarla.RadarMeasurement):\n self._parse_radar_cb(data, self._tag)\n elif isinstance(data, carla.libcarla.GnssMeasurement):\n self._parse_gnss_cb(data, self._tag)\n elif isinstance(data, carla.libcarla.IMUMeasurement):\n self._parse_imu_cb(data, self._tag)\n elif isinstance(data, GenericMeasurement):\n self._parse_pseudosensor(data, self._tag)\n else:\n logging.error('No callback method for this sensor.')\n\n # Parsing CARLA physical Sensors\n def _parse_image_cb(self, image, tag):\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = copy.deepcopy(array)\n array = np.reshape(array, (image.height, image.width, 4))\n self._data_provider.update_sensor(tag, array, image.frame)\n\n def _parse_lidar_cb(self, lidar_data, tag):\n points = np.frombuffer(lidar_data.raw_data, dtype=np.dtype('f4'))\n points = copy.deepcopy(points)\n points = np.reshape(points, (int(points.shape[0] / 4), 4))\n self._data_provider.update_sensor(tag, points, lidar_data.frame)\n\n def _parse_semantic_lidar_cb(self, semantic_lidar_data, tag):\n points = np.frombuffer(semantic_lidar_data.raw_data, dtype=np.dtype('f4'))\n points = copy.deepcopy(points)\n points = np.reshape(points, (int(points.shape[0] / 6), 6))\n self._data_provider.update_sensor(tag, points, semantic_lidar_data.frame)\n\n def _parse_radar_cb(self, radar_data, tag):\n # [depth, azimuth, altitute, velocity]\n points = np.frombuffer(radar_data.raw_data, dtype=np.dtype('f4'))\n points = copy.deepcopy(points)\n points = np.reshape(points, (int(points.shape[0] / 4), 4))\n points = np.flip(points, 1)\n self._data_provider.update_sensor(tag, points, radar_data.frame)\n\n def _parse_gnss_cb(self, gnss_data, tag):\n array = np.array([gnss_data.latitude,\n gnss_data.longitude,\n gnss_data.altitude], dtype=np.float64)\n self._data_provider.update_sensor(tag, array, gnss_data.frame)\n\n def _parse_imu_cb(self, imu_data, tag):\n array = np.array([imu_data.accelerometer.x,\n imu_data.accelerometer.y,\n imu_data.accelerometer.z,\n imu_data.gyroscope.x,\n imu_data.gyroscope.y,\n imu_data.gyroscope.z,\n imu_data.compass,\n ], dtype=np.float64)\n self._data_provider.update_sensor(tag, array, imu_data.frame)\n\n def _parse_pseudosensor(self, package, tag):\n self._data_provider.update_sensor(tag, package.data, package.frame)\n\n\nclass SensorInterface(object):\n def __init__(self):\n self._sensors_objects = {}\n self._data_buffers = {}\n self._new_data_buffers = Queue()\n self._queue_timeout = 10 # default: 10\n\n # Only sensor that doesn't get the data on tick, needs special treatment\n self._opendrive_tag = None\n\n\n def register_sensor(self, tag, sensor_type, sensor):\n if tag in self._sensors_objects:\n raise SensorConfigurationInvalid(\"Duplicated sensor tag [{}]\".format(tag))\n\n self._sensors_objects[tag] = sensor\n\n if sensor_type == 'sensor.opendrive_map': \n self._opendrive_tag = tag\n\n def update_sensor(self, tag, data, timestamp):\n # print(\"Updating {} - {}\".format(tag, timestamp))\n if tag not in self._sensors_objects:\n raise SensorConfigurationInvalid(\"The sensor with tag [{}] has not been created!\".format(tag))\n\n self._new_data_buffers.put((tag, timestamp, data))\n\n def get_data(self):\n try: \n data_dict = {}\n while len(data_dict.keys()) < len(self._sensors_objects.keys()):\n\n # Don't wait for the opendrive sensor\n if self._opendrive_tag and self._opendrive_tag not in data_dict.keys() \\\n and len(self._sensors_objects.keys()) == len(data_dict.keys()) + 1:\n # print(\"Ignoring opendrive sensor\")\n break\n\n sensor_data = self._new_data_buffers.get(True, self._queue_timeout)\n data_dict[sensor_data[0]] = ((sensor_data[1], sensor_data[2]))\n\n except Empty:\n raise SensorReceivedNoData(\"A sensor took too long to send their data\")\n\n return data_dict\n" }, { "path": "leaderboard/leaderboard/leaderboard_evaluator.py", "content": "#!/usr/bin/env python\n# Copyright (c) 2018-2019 Intel Corporation.\n# authors: German Ros (german.ros@intel.com), Felipe Codevilla (felipe.alcm@gmail.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nCARLA Challenge Evaluator Routes\n\nProvisional code to evaluate Autonomous Agents for the CARLA Autonomous Driving challenge\n\"\"\"\nfrom __future__ import print_function\nfrom multiprocessing.connection import wait\n\nimport traceback\nimport argparse\nfrom argparse import RawTextHelpFormatter\nfrom datetime import datetime\nfrom distutils.version import LooseVersion\nimport importlib\nimport os\nimport sys\nimport gc\nimport pkg_resources\nimport sys\nimport carla\nimport copy\nimport signal\nimport time\nimport subprocess\nfrom srunner.scenariomanager.carla_data_provider import *\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenariomanager.watchdog import Watchdog\nimport random\nfrom leaderboard.scenarios.scenario_manager import ScenarioManager\nfrom leaderboard.scenarios.route_scenario import RouteScenario\nfrom leaderboard.envs.sensor_interface import SensorInterface, SensorConfigurationInvalid\nfrom leaderboard.autoagents.agent_wrapper import AgentWrapper, AgentError\nfrom leaderboard.utils.statistics_manager import StatisticsManager\nfrom leaderboard.utils.route_indexer import RouteIndexer\n\ndef str2bool(v):\n if isinstance(v, bool):\n return v\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\n return True\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n return False\n else:\n raise argparse.ArgumentTypeError('Boolean value expected.')\n\nsensors_to_icons = {\n 'sensor.camera.rgb': 'carla_camera',\n 'sensor.camera.semantic_segmentation': 'carla_camera',\n 'sensor.camera.depth': 'carla_camera',\n 'sensor.lidar.ray_cast': 'carla_lidar',\n 'sensor.lidar.ray_cast_semantic': 'carla_lidar',\n 'sensor.other.radar': 'carla_radar',\n 'sensor.other.gnss': 'carla_gnss',\n 'sensor.other.imu': 'carla_imu',\n 'sensor.opendrive_map': 'carla_opendrive_map',\n 'sensor.speedometer': 'carla_speedometer'\n}\n\n\nWEATHERS = {\n 'ClearNoon': carla.WeatherParameters.ClearNoon,\n 'ClearSunset': carla.WeatherParameters.ClearSunset,\n\n 'CloudyNoon': carla.WeatherParameters.CloudyNoon,\n 'CloudySunset': carla.WeatherParameters.CloudySunset,\n\n 'WetNoon': carla.WeatherParameters.WetNoon,\n 'WetSunset': carla.WeatherParameters.WetSunset,\n\n 'MidRainyNoon': carla.WeatherParameters.MidRainyNoon,\n 'MidRainSunset': carla.WeatherParameters.MidRainSunset,\n\n 'WetCloudyNoon': carla.WeatherParameters.WetCloudyNoon,\n 'WetCloudySunset': carla.WeatherParameters.WetCloudySunset,\n\n 'HardRainNoon': carla.WeatherParameters.HardRainNoon,\n 'HardRainSunset': carla.WeatherParameters.HardRainSunset,\n\n 'SoftRainNoon': carla.WeatherParameters.SoftRainNoon,\n 'SoftRainSunset': carla.WeatherParameters.SoftRainSunset,\n}\n\ndef kill_carla():\n kill_process = subprocess.Popen('killall -9 -r CarlaUE4-Linux', shell=True)\n kill_process.wait()\n time.sleep(5)\n\nclass LeaderboardEvaluator(object):\n\n \"\"\"\n document me!\n \"\"\"\n\n ego_vehicles = []\n\n # Tunable parameters\n client_timeout = 10.0 # in seconds\n wait_for_world = 20.0 # in seconds\n frame_rate = 20.0 # in Hz\n\n def __init__(self, args, statistics_manager):\n \"\"\"\n Setup CARLA client and world\n Setup ScenarioManager\n \"\"\"\n self.statistics_manager = statistics_manager\n self.sensors = None\n self.sensor_icons = []\n self._vehicle_lights = carla.VehicleLightState.Position | carla.VehicleLightState.LowBeam\n self.carla_path = os.environ[\"CARLA_ROOT\"]\n # First of all, we need to create the client that will send the requests\n # to the simulator. Here we'll assume the simulator is accepting\n # requests in the localhost at port 2000.\n\n cmd = 'DISPLAY= bash ' + os.path.join(self.carla_path, 'CarlaUE4.sh')+f' -opengl -carla-rpc-port={args.port} -nosound'\n print(cmd)\n wait_time = 10\n server_process = subprocess.Popen(cmd, shell=True, preexec_fn=os.setsid)\n print(\"Process ID:\", server_process.pid)\n time.sleep(wait_time)\n self.client = carla.Client(args.host, int(args.port))\n if args.timeout:\n self.client_timeout = float(args.timeout)\n self.client.set_timeout(self.client_timeout)\n\n try:\n self.traffic_manager = self.client.get_trafficmanager(int(args.trafficManagerPort))\n # self.traffic_manager = self.client.get_trafficmanager(8000)\n except Exception as e:\n print(\"traffic_manager fail to init\", flush=True)\n print(e, flush=True)\n dist = pkg_resources.get_distribution(\"carla\")\n\n # load agent\n module_name = os.path.basename(args.agent).split('.')[0]\n sys.path.insert(0, os.path.dirname(args.agent))\n self.module_agent = importlib.import_module(module_name)\n\n # Create the ScenarioManager\n self.manager = ScenarioManager(args.timeout, args.debug > 1)\n\n # Time control for summary purposes\n self._start_time = GameTime.get_time()\n self._end_time = None\n\n # Create the agent timer\n self._agent_watchdog = Watchdog(int(float(args.timeout)))\n signal.signal(signal.SIGINT, self._signal_handler)\n\n def _signal_handler(self, signum, frame):\n \"\"\"\n Terminate scenario ticking when receiving a signal interrupt\n \"\"\"\n if self._agent_watchdog and not self._agent_watchdog.get_status():\n raise RuntimeError(\"Timeout: Agent took too long to setup\")\n elif self.manager:\n self.manager.signal_handler(signum, frame)\n\n def __del__(self):\n \"\"\"\n Cleanup and delete actors, ScenarioManager and CARLA world\n \"\"\"\n\n self._cleanup()\n if hasattr(self, 'manager') and self.manager:\n del self.manager\n if hasattr(self, 'world') and self.world:\n del self.world\n\n def _cleanup(self):\n \"\"\"\n Remove and destroy all actors\n \"\"\"\n\n # Simulation still running and in synchronous mode?\n if self.manager and self.manager.get_running_status() \\\n and hasattr(self, 'world') and self.world:\n # Reset to asynchronous mode\n settings = self.world.get_settings()\n settings.synchronous_mode = False\n settings.fixed_delta_seconds = None\n self.world.apply_settings(settings)\n self.traffic_manager.set_synchronous_mode(False)\n\n if self.manager:\n self.manager.cleanup()\n\n CarlaDataProvider.cleanup()\n\n for i, _ in enumerate(self.ego_vehicles):\n if self.ego_vehicles[i]:\n self.ego_vehicles[i].destroy()\n self.ego_vehicles[i] = None\n self.ego_vehicles = []\n\n if self._agent_watchdog:\n self._agent_watchdog.stop()\n\n if hasattr(self, 'agent_instance') and self.agent_instance:\n self.agent_instance.destroy()\n self.agent_instance = None\n\n if hasattr(self, 'statistics_manager') and self.statistics_manager:\n self.statistics_manager.scenario = None\n\n def _prepare_ego_vehicles(self, ego_vehicles, wait_for_ego_vehicles=False):\n \"\"\"\n Spawn or update the ego vehicles\n \"\"\"\n\n if not wait_for_ego_vehicles:\n for vehicle in ego_vehicles:\n self.ego_vehicles.append(CarlaDataProvider.request_new_actor(vehicle.model,\n vehicle.transform,\n vehicle.rolename,\n color=vehicle.color,\n vehicle_category=vehicle.category))\n\n else:\n ego_vehicle_missing = True\n while ego_vehicle_missing:\n self.ego_vehicles = []\n ego_vehicle_missing = False\n for ego_vehicle in ego_vehicles:\n ego_vehicle_found = False\n carla_vehicles = CarlaDataProvider.get_world().get_actors().filter('vehicle.*')\n for carla_vehicle in carla_vehicles:\n if carla_vehicle.attributes['role_name'] == ego_vehicle.rolename:\n ego_vehicle_found = True\n self.ego_vehicles.append(carla_vehicle)\n break\n if not ego_vehicle_found:\n ego_vehicle_missing = True\n break\n\n for i, _ in enumerate(self.ego_vehicles):\n self.ego_vehicles[i].set_transform(ego_vehicles[i].transform)\n\n # sync state\n CarlaDataProvider.get_world().tick()\n\n def _load_and_wait_for_world(self, args, town, ego_vehicles=None):\n \"\"\"\n Load a new CARLA world and provide data to CarlaDataProvider\n \"\"\"\n self.traffic_manager.set_synchronous_mode(False)\n # if hasattr(self, 'world'):\n # settings = self.world.get_settings()\n # settings.synchronous_mode = False\n # self.world.apply_settings(settings)\n print(town)\n try: \n self.world = self.client.load_world(town)\n except Exception as e:\n print(111, e)\n settings = self.world.get_settings()\n settings.fixed_delta_seconds = 1.0 / self.frame_rate\n settings.synchronous_mode = True\n self.world.apply_settings(settings)\n\n self.world.reset_all_traffic_lights()\n\n CarlaDataProvider.set_client(self.client)\n CarlaDataProvider.set_world(self.world)\n CarlaDataProvider.set_traffic_manager_port(int(args.trafficManagerPort))\n\n if args.weather != \"none\":\n assert args.weather in WEATHERS\n CarlaDataProvider.set_weather(WEATHERS[args.weather])\n\n self.traffic_manager.set_synchronous_mode(True)\n if not os.environ['IS_EVAL']:\n args.trafficManagerSeed = random.randint(0, 100000)\n args.init_trafficManagerSeed = random.randint(0, 100000)\n print(\"Seed\", args.trafficManagerSeed)\n self.traffic_manager.set_random_device_seed(int(args.trafficManagerSeed))\n\n # Wait for the world to be ready\n if CarlaDataProvider.is_sync_mode():\n self.world.tick()\n else:\n self.world.wait_for_tick()\n\n if CarlaDataProvider.get_map().name != town:\n raise Exception(\"The CARLA server uses the wrong map!\"\n \"This scenario requires to use map {}\".format(town))\n\n def _register_statistics(self, config, checkpoint, entry_status, crash_message=\"\"):\n \"\"\"\n Computes and saved the simulation statistics\n \"\"\"\n # register statistics\n current_stats_record = self.statistics_manager.compute_route_statistics(\n config,\n self.manager.scenario_duration_system,\n self.manager.scenario_duration_game,\n crash_message\n )\n\n print(\"\\033[1m> Registering the route statistics\\033[0m\")\n self.statistics_manager.save_record(current_stats_record, config.index, checkpoint)\n self.statistics_manager.save_entry_status(entry_status, False, checkpoint)\n\n def _load_and_run_scenario(self, args, config):\n \"\"\"\n Load and run the scenario given by config.\n\n Depending on what code fails, the simulation will either stop the route and\n continue from the next one, or report a crash and stop.\n \"\"\"\n crash_message = \"\"\n entry_status = \"Started\"\n\n print(\"\\n\\033[1m========= Preparing {} (repetition {}) =========\".format(config.name, config.repetition_index))\n print(\"> Setting up the agent\\033[0m\")\n\n # Prepare the statistics of the route\n self.statistics_manager.set_route(config.name, config.index)\n\n # Set up the user's agent, and the timer to avoid freezing the simulation\n try:\n self._agent_watchdog.start()\n agent_class_name = getattr(self.module_agent, 'get_entry_point')()\n if \"roach\" not in args.agent_config:\n self.agent_instance = getattr(self.module_agent, agent_class_name)(args.agent_config + \"+\" + str(config.index) + \"+\" + str(config.repetition_index))\n else:\n self.agent_instance = getattr(self.module_agent, agent_class_name)(args.agent_config)\n config.agent = self.agent_instance\n # Check and store the sensors\n if not self.sensors:\n self.sensors = self.agent_instance.sensors()\n track = self.agent_instance.track\n\n AgentWrapper.validate_sensor_configuration(self.sensors, track, args.track)\n\n self.sensor_icons = [sensors_to_icons[sensor['type']] for sensor in self.sensors]\n self.statistics_manager.save_sensors(self.sensor_icons, args.checkpoint)\n config.folder_name = self.agent_instance.folder_name\n self._agent_watchdog.stop()\n\n except SensorConfigurationInvalid as e:\n # The sensors are invalid -> set the ejecution to rejected and stop\n print(\"\\n\\033[91mThe sensor's configuration used is invalid:\")\n print(\"> {}\\033[0m\\n\".format(e))\n traceback.print_exc()\n\n crash_message = \"Agent's sensors were invalid\"\n entry_status = \"Rejected\"\n\n self._register_statistics(config, args.checkpoint, entry_status, crash_message)\n self._cleanup()\n sys.exit(-1)\n\n except Exception as e:\n # The agent setup has failed -> start the next route\n print(\"\\n\\033[91mCould not set up the required agent:\")\n print(\"> {}\\033[0m\\n\".format(e))\n traceback.print_exc()\n\n crash_message = \"Agent couldn't be set up\"\n\n self._register_statistics(config, args.checkpoint, entry_status, crash_message)\n self._cleanup()\n return\n\n print(\"\\033[1m> Loading the world\\033[0m\")\n\n # Load the world and the scenario\n try:\n args.trafficManagerSeed = int(args.init_trafficManagerSeed) + int(config.index)\n self._load_and_wait_for_world(args, config.town, config.ego_vehicles)\n self._prepare_ego_vehicles(config.ego_vehicles, False)\n scenario = RouteScenario(world=self.world, config=config, debug_mode=args.debug)\n self.statistics_manager.set_scenario(scenario.scenario)\n\n # self.agent_instance._init()\n # self.agent_instance.sensor_interface = SensorInterface()\n\n #Night mode\n if config.weather.sun_altitude_angle < 0.0:\n for vehicle in scenario.ego_vehicles:\n vehicle.set_light_state(carla.VehicleLightState(self._vehicle_lights))\n\n # Load scenario and run it\n if args.record:\n self.client.start_recorder(\"{}/{}_rep{}.log\".format(args.record, config.name, config.repetition_index))\n self.manager.load_scenario(scenario, self.agent_instance, config.repetition_index)\n\n except Exception as e:\n # The scenario is wrong -> set the ejecution to crashed and stop\n print(\"\\n\\033[91mThe scenario could not be loaded:\")\n print(\"> {}\\033[0m\\n\".format(e))\n traceback.print_exc()\n\n crash_message = \"Simulation crashed\"\n entry_status = \"Crashed\"\n\n self._register_statistics(config, args.checkpoint, entry_status, crash_message)\n\n if args.record:\n self.client.stop_recorder()\n\n self._cleanup()\n sys.exit(-1)\n\n print(\"\\033[1m> Running the route\\033[0m\")\n\n # Run the scenario\n self.manager.run_scenario()\n try:\n print(\"\\033[1m> Stopping the route\\033[0m\")\n self.manager.stop_scenario()\n self._register_statistics(config, args.checkpoint, entry_status, crash_message)\n\n if args.record:\n self.client.stop_recorder()\n\n # Remove all actors\n scenario.remove_all_actors()\n\n self._cleanup()\n\n except Exception as e:\n print(\"\\n\\033[91mFailed to stop the scenario, the statistics might be empty:\")\n print(\"> {}\\033[0m\\n\".format(e))\n traceback.print_exc()\n\n crash_message = \"Simulation crashed\"\n\n if crash_message == \"Simulation crashed\":\n sys.exit(-1)\n\n def run(self, args):\n \"\"\"\n Run the challenge mode\n \"\"\"\n # agent_class_name = getattr(self.module_agent, 'get_entry_point')()\n # self.agent_instance = getattr(self.module_agent, agent_class_name)(args.agent_config)\n\n route_indexer = RouteIndexer(args.routes, args.scenarios, args.repetitions)\n print(\"resume:\", args.resume, flush=True)\n if args.resume:\n route_indexer.resume(args.checkpoint)\n self.statistics_manager.resume(args.checkpoint)\n else:\n self.statistics_manager.clear_record(args.checkpoint)\n route_indexer.save_state(args.checkpoint)\n\n while route_indexer.peek():\n # setup\n config = route_indexer.next()\n\n\n # run\n self._load_and_run_scenario(args, config)\n\n for obj in gc.get_objects():\n try:\n if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):\n print(type(obj), obj.size())\n except:\n pass\n\n route_indexer.save_state(args.checkpoint)\n\n # save global statistics\n print(\"\\033[1m> Registering the global statistics\\033[0m\")\n global_stats_record = self.statistics_manager.compute_global_statistics(route_indexer.total)\n StatisticsManager.save_global_record(global_stats_record, self.sensor_icons, route_indexer.total, args.checkpoint)\n\n\ndef main():\n description = \"CARLA AD Leaderboard Evaluation: evaluate your Agent in CARLA scenarios\\n\"\n\n # general parameters\n parser = argparse.ArgumentParser(description=description, formatter_class=RawTextHelpFormatter)\n parser.add_argument('--host', default='localhost',\n help='IP of the host server (default: localhost)')\n parser.add_argument('--port', default='2000', help='TCP port to listen to (default: 2000)')\n parser.add_argument('--trafficManagerPort', default='8000',\n help='Port to use for the TrafficManager (default: 8000)')\n parser.add_argument('--trafficManagerSeed', default='2023',\n help='Seed used by the TrafficManager (default: 0)')\n parser.add_argument('--init_trafficManagerSeed', default=2023, type=int)\n parser.add_argument('--debug', type=int, help='Run with debug output', default=0)\n parser.add_argument('--record', type=str, default='',\n help='Use CARLA recording feature to create a recording of the scenario')\n parser.add_argument('--timeout', default=\"6000.0\",\n help='Set the CARLA client timeout value in seconds')\n\n # simulation setup\n parser.add_argument('--routes',\n help='Name of the route to be executed. Point to the route_xml_file to be executed.',\n required=True)\n parser.add_argument('--weather',\n type=str, default='none',\n help='Name of the weahter to be executed',\n )\n parser.add_argument('--scenarios',\n help='Name of the scenario annotation file to be mixed with the route.',\n required=True)\n parser.add_argument('--repetitions',\n type=int,\n default=1,\n help='Number of repetitions per route.')\n\n # agent-related options\n parser.add_argument(\"-a\", \"--agent\", type=str, help=\"Path to Agent's py file to evaluate\", required=True)\n parser.add_argument(\"--agent-config\", type=str, help=\"Path to Agent's configuration file\", default=\"\")\n\n parser.add_argument(\"--track\", type=str, default='SENSORS', help=\"Participation track: SENSORS, MAP\")\n parser.add_argument('--resume', type=str2bool, default=False, help='Resume execution from last checkpoint?')\n parser.add_argument('--is_local', type=str2bool, default=False, help='whether in local machine')\n parser.add_argument('--is_eval', type=str2bool, default=False, help='whether in eval mode otherwise data collection mode')\n\n parser.add_argument(\"--checkpoint\", type=str,\n default='./simulation_results.json',\n help=\"Path to checkpoint used for saving statistics and resuming\")\n\n arguments = parser.parse_args()\n print(\"init statistics_manager\")\n statistics_manager = StatisticsManager()\n if arguments.is_eval:\n os.environ['IS_EVAL'] = \"True\"\n else:\n os.environ['IS_EVAL'] = \"False\"\n \n if arguments.is_local:\n os.environ['IS_LOCAL'] = \"True\"\n else:\n os.environ['IS_LOCAL'] = \"False\"\n try:\n print(\"begin\")\n leaderboard_evaluator = LeaderboardEvaluator(arguments, statistics_manager)\n leaderboard_evaluator.run(arguments)\n\n except Exception as e:\n traceback.print_exc()\n finally:\n print(\"Collect Done\", flush=True)\n del leaderboard_evaluator\n\nif __name__ == '__main__':\n main()\n" }, { "path": "leaderboard/leaderboard/scenarios/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/scenarios/background_activity.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nScenario spawning elements to make the town dynamic and interesting\n\"\"\"\n\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenarios.basic_scenario import BasicScenario\nimport os\n\nBACKGROUND_ACTIVITY_SCENARIOS = [\"BackgroundActivity\"]\n\n\nclass BackgroundActivity(BasicScenario):\n\n \"\"\"\n Implementation of a scenario to spawn a set of background actors,\n and to remove traffic jams in background traffic\n\n This is a single ego vehicle scenario\n \"\"\"\n\n category = \"BackgroundActivity\"\n\n town_amount = {\n # 'Town01': 120,\n 'Town01': 120,\n 'Town02': 100,\n 'Town03': 120,\n 'Town04': 200,\n 'Town05': 120,\n 'Town06': 150,\n 'Town07': 110,\n 'Town08': 180,\n 'Town09': 300,\n 'Town10': 120,\n }\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, timeout=35 * 60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self.config = config\n self.debug = debug_mode\n\n self.timeout = timeout # Timeout of scenario in seconds\n\n super(BackgroundActivity, self).__init__(\"BackgroundActivity\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n terminate_on_failure=True,\n criteria_enable=True)\n\n def _initialize_actors(self, config):\n\n town_name = config.town\n if town_name in self.town_amount:\n amount = self.town_amount[town_name]\n else:\n amount = 0\n if os.environ[\"BENCHMARK\"] == \"town05long\":\n amount = 120\n print(\"----------------Eval with Town05 Long, amount=120\", flush=True)\n if os.environ[\"BENCHMARK\"] == \"longest6\":\n amount = 500\n print(\"----------------Eval with Longest6, amount=500\", flush=True)\n new_actors = CarlaDataProvider.request_new_batch_actors('vehicle.*',\n amount,\n carla.Transform(),\n autopilot=True,\n random_location=True,\n rolename='background')\n\n if new_actors is None:\n raise Exception(\"Error: Unable to add the background activity, all spawn points were occupied\")\n\n for _actor in new_actors:\n self.other_actors.append(_actor)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n pass\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n pass\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "leaderboard/leaderboard/scenarios/master_scenario.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nBasic CARLA Autonomous Driving training scenario\n\"\"\"\n\nimport py_trees\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteConfiguration\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import Idle\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import (CollisionTest,\n InRouteTest,\n RouteCompletionTest,\n RunningRedLightTest,\n RunningStopTest,\n OutsideRouteLanesTest)\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\nfrom leaderboard.scenarios.scenarioatomics.atomic_criteria import ActorSpeedAboveThresholdTest\n\n\nMASTER_SCENARIO = [\"MasterScenario\"]\n\n\nclass MasterScenario(BasicScenario):\n\n \"\"\"\n Implementation of a Master scenario that controls the route.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n category = \"Master\"\n radius = 10.0 # meters\n\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=300):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self.config = config\n self.route = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n if hasattr(self.config, 'route'):\n self.route = self.config.route\n else:\n raise ValueError(\"Master scenario must have a route\")\n\n super(MasterScenario, self).__init__(\"MasterScenario\", ego_vehicles=ego_vehicles, config=config,\n world=world, debug_mode=debug_mode,\n terminate_on_failure=True, criteria_enable=criteria_enable)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"MasterScenario\")\n idle_behavior = Idle()\n sequence.add_child(idle_behavior)\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n\n if isinstance(self.route, RouteConfiguration):\n route = self.route.data\n else:\n route = self.route\n\n collision_criterion = CollisionTest(self.ego_vehicles[0], terminate_on_failure=False)\n\n route_criterion = InRouteTest(self.ego_vehicles[0],\n route=route,\n offroad_max=30,\n terminate_on_failure=True)\n\n completion_criterion = RouteCompletionTest(self.ego_vehicles[0], route=route)\n\n outsidelane_criterion = OutsideRouteLanesTest(self.ego_vehicles[0],route=route)\n\n red_light_criterion = RunningRedLightTest(self.ego_vehicles[0])\n\n stop_criterion = RunningStopTest(self.ego_vehicles[0])\n\n blocked_criterion = ActorSpeedAboveThresholdTest(self.ego_vehicles[0],\n speed_threshold=0.1,\n below_threshold_max_time=90.0,\n terminate_on_failure=True)\n\n parallel_criteria = py_trees.composites.Parallel(\"group_criteria\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n parallel_criteria.add_child(completion_criterion)\n parallel_criteria.add_child(collision_criterion)\n parallel_criteria.add_child(route_criterion)\n parallel_criteria.add_child(outsidelane_criterion)\n parallel_criteria.add_child(red_light_criterion)\n parallel_criteria.add_child(stop_criterion)\n parallel_criteria.add_child(blocked_criterion)\n\n\n return parallel_criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "leaderboard/leaderboard/scenarios/route_scenario.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides Challenge routes as standalone scenarios\n\"\"\"\n\nfrom __future__ import print_function\n\nimport math\nimport xml.etree.ElementTree as ET\nimport numpy.random as random\n\nimport py_trees\n\nimport carla\n\nfrom agents.navigation.local_planner import RoadOption\n\n# pylint: disable=line-too-long\nfrom srunner.scenarioconfigs.scenario_configuration import ScenarioConfiguration, ActorConfigurationData\n# pylint: enable=line-too-long\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import Idle, ScenarioTriggerer\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.scenarios.control_loss import ControlLoss\nfrom srunner.scenarios.follow_leading_vehicle import FollowLeadingVehicle\nfrom srunner.scenarios.object_crash_vehicle import DynamicObjectCrossing\nfrom srunner.scenarios.object_crash_intersection import VehicleTurningRoute\nfrom srunner.scenarios.other_leading_vehicle import OtherLeadingVehicle\nfrom srunner.scenarios.maneuver_opposite_direction import ManeuverOppositeDirection\nfrom srunner.scenarios.junction_crossing_route import SignalJunctionCrossingRoute, NoSignalJunctionCrossingRoute\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import (CollisionTest,\n InRouteTest,\n RouteCompletionTest,\n OutsideRouteLanesTest,\n RunningRedLightTest,\n RunningStopTest,\n ActorSpeedAboveThresholdTest)\n\nfrom leaderboard.utils.route_parser import RouteParser, TRIGGER_THRESHOLD, TRIGGER_ANGLE_THRESHOLD\nfrom leaderboard.utils.route_manipulation import interpolate_trajectory\nimport os\n\nROUTESCENARIO = [\"RouteScenario\"]\n\nSECONDS_GIVEN_PER_METERS = 0.8 # for timeout\n# SECONDS_GIVEN_PER_METERS = 2 # for timeout\nINITIAL_SECONDS_DELAY = 5.0\n\nNUMBER_CLASS_TRANSLATION = {\n \"Scenario1\": ControlLoss,\n \"Scenario2\": FollowLeadingVehicle,\n \"Scenario3\": DynamicObjectCrossing,\n \"Scenario4\": VehicleTurningRoute,\n \"Scenario5\": OtherLeadingVehicle,\n \"Scenario6\": ManeuverOppositeDirection,\n \"Scenario7\": SignalJunctionCrossingRoute,\n \"Scenario8\": SignalJunctionCrossingRoute,\n \"Scenario9\": SignalJunctionCrossingRoute,\n \"Scenario10\": NoSignalJunctionCrossingRoute\n}\n\n\ndef oneshot_behavior(name, variable_name, behaviour):\n \"\"\"\n This is taken from py_trees.idiom.oneshot.\n \"\"\"\n # Initialize the variables\n blackboard = py_trees.blackboard.Blackboard()\n _ = blackboard.set(variable_name, False)\n\n # Wait until the scenario has ended\n subtree_root = py_trees.composites.Selector(name=name)\n check_flag = py_trees.blackboard.CheckBlackboardVariable(\n name=variable_name + \" Done?\",\n variable_name=variable_name,\n expected_value=True,\n clearing_policy=py_trees.common.ClearingPolicy.ON_INITIALISE\n )\n set_flag = py_trees.blackboard.SetBlackboardVariable(\n name=\"Mark Done\",\n variable_name=variable_name,\n variable_value=True\n )\n # If it's a sequence, don't double-nest it in a redundant manner\n if isinstance(behaviour, py_trees.composites.Sequence):\n behaviour.add_child(set_flag)\n sequence = behaviour\n else:\n sequence = py_trees.composites.Sequence(name=\"OneShot\")\n sequence.add_children([behaviour, set_flag])\n\n subtree_root.add_children([check_flag, sequence])\n return subtree_root\n\n\ndef convert_json_to_transform(actor_dict):\n \"\"\"\n Convert a JSON string to a CARLA transform\n \"\"\"\n return carla.Transform(location=carla.Location(x=float(actor_dict['x']), y=float(actor_dict['y']),\n z=float(actor_dict['z'])),\n rotation=carla.Rotation(roll=0.0, pitch=0.0, yaw=float(actor_dict['yaw'])))\n\n\ndef convert_json_to_actor(actor_dict):\n \"\"\"\n Convert a JSON string to an ActorConfigurationData dictionary\n \"\"\"\n node = ET.Element('waypoint')\n node.set('x', actor_dict['x'])\n node.set('y', actor_dict['y'])\n node.set('z', actor_dict['z'])\n node.set('yaw', actor_dict['yaw'])\n\n return ActorConfigurationData.parse_from_node(node, 'simulation')\n\n\ndef convert_transform_to_location(transform_vec):\n \"\"\"\n Convert a vector of transforms to a vector of locations\n \"\"\"\n location_vec = []\n for transform_tuple in transform_vec:\n location_vec.append((transform_tuple[0].location, transform_tuple[1]))\n\n return location_vec\n\n\ndef compare_scenarios(scenario_choice, existent_scenario):\n \"\"\"\n Compare function for scenarios based on distance of the scenario start position\n \"\"\"\n def transform_to_pos_vec(scenario):\n \"\"\"\n Convert left/right/front to a meaningful CARLA position\n \"\"\"\n position_vec = [scenario['trigger_position']]\n if scenario['other_actors'] is not None:\n if 'left' in scenario['other_actors']:\n position_vec += scenario['other_actors']['left']\n if 'front' in scenario['other_actors']:\n position_vec += scenario['other_actors']['front']\n if 'right' in scenario['other_actors']:\n position_vec += scenario['other_actors']['right']\n\n return position_vec\n\n # put the positions of the scenario choice into a vec of positions to be able to compare\n\n choice_vec = transform_to_pos_vec(scenario_choice)\n existent_vec = transform_to_pos_vec(existent_scenario)\n for pos_choice in choice_vec:\n for pos_existent in existent_vec:\n\n dx = float(pos_choice['x']) - float(pos_existent['x'])\n dy = float(pos_choice['y']) - float(pos_existent['y'])\n dz = float(pos_choice['z']) - float(pos_existent['z'])\n dist_position = math.sqrt(dx * dx + dy * dy + dz * dz)\n dyaw = float(pos_choice['yaw']) - float(pos_choice['yaw'])\n dist_angle = math.sqrt(dyaw * dyaw)\n if dist_position < TRIGGER_THRESHOLD and dist_angle < TRIGGER_ANGLE_THRESHOLD:\n return True\n\n return False\n\n\nclass RouteScenario(BasicScenario):\n\n \"\"\"\n Implementation of a RouteScenario, i.e. a scenario that consists of driving along a pre-defined route,\n along which several smaller scenarios are triggered\n \"\"\"\n\n category = \"RouteScenario\"\n\n def __init__(self, world, config, debug_mode=0, criteria_enable=True):\n \"\"\"\n Setup all relevant parameters and create scenarios along route\n \"\"\"\n self.config = config\n self.route = None\n self.sampled_scenarios_definitions = None\n\n self._update_route(world, config, debug_mode>0)\n\n ego_vehicle = self._update_ego_vehicle()\n\n self.list_scenarios = self._build_scenario_instances(world,\n ego_vehicle,\n self.sampled_scenarios_definitions,\n scenarios_per_tick=10,\n timeout=self.timeout,\n debug_mode=debug_mode>1)\n\n self.is_eval = (os.environ['IS_EVAL'] == \"True\")\n print(\"IS_EVAL\", self.is_eval)\n super(RouteScenario, self).__init__(name=config.name,\n ego_vehicles=[ego_vehicle],\n config=config,\n world=world,\n debug_mode=debug_mode>1,\n terminate_on_failure=False,\n criteria_enable=criteria_enable)\n\n def _update_route(self, world, config, debug_mode):\n \"\"\"\n Update the input route, i.e. refine waypoint list, and extract possible scenario locations\n\n Parameters:\n - world: CARLA world\n - config: Scenario configuration (RouteConfiguration)\n \"\"\"\n\n # Transform the scenario file into a dictionary\n world_annotations = RouteParser.parse_annotations_file(config.scenario_file)\n\n # prepare route's trajectory (interpolate and add the GPS route)\n # gps_route, route = interpolate_trajectory(world, config.trajectory)\n gps_route, route, wp_route = interpolate_trajectory(world, config.trajectory)\n\n potential_scenarios_definitions, _ = RouteParser.scan_route_for_scenarios(\n config.town, route, world_annotations)\n\n self.route = route\n CarlaDataProvider.set_ego_vehicle_route(convert_transform_to_location(self.route))\n\n config.agent.set_global_plan(gps_route, self.route, wp_route)\n # Sample the scenarios to be used for this route instance.\n self.sampled_scenarios_definitions = self._scenario_sampling(potential_scenarios_definitions)\n # Timeout of scenario in seconds\n self.timeout = self._estimate_route_timeout()\n\n # Print route in debug mode\n if debug_mode:\n self._draw_waypoints(world, self.route, vertical_shift=1.0, persistency=50000.0)\n\n def _update_ego_vehicle(self):\n \"\"\"\n Set/Update the start position of the ego_vehicle\n \"\"\"\n # move ego to correct position\n elevate_transform = self.route[0][0]\n elevate_transform.location.z += 0.5\n\n ego_vehicle = CarlaDataProvider.request_new_actor('vehicle.lincoln.mkz2017',\n elevate_transform,\n rolename='hero')\n\n spectator = CarlaDataProvider.get_world().get_spectator()\n ego_trans = ego_vehicle.get_transform()\n spectator.set_transform(carla.Transform(ego_trans.location + carla.Location(z=50),\n carla.Rotation(pitch=-90)))\n\n return ego_vehicle\n\n def _estimate_route_timeout(self):\n \"\"\"\n Estimate the duration of the route\n \"\"\"\n route_length = 0.0 # in meters\n\n prev_point = self.route[0][0]\n for current_point, _ in self.route[1:]:\n dist = current_point.location.distance(prev_point.location)\n route_length += dist\n prev_point = current_point\n\n return int(SECONDS_GIVEN_PER_METERS * route_length + INITIAL_SECONDS_DELAY)\n\n # pylint: disable=no-self-use\n def _draw_waypoints(self, world, waypoints, vertical_shift, persistency=-1):\n \"\"\"\n Draw a list of waypoints at a certain height given in vertical_shift.\n \"\"\"\n for w in waypoints:\n wp = w[0].location + carla.Location(z=vertical_shift)\n\n size = 0.2\n if w[1] == RoadOption.LEFT: # Yellow\n color = carla.Color(255, 255, 0)\n elif w[1] == RoadOption.RIGHT: # Cyan\n color = carla.Color(0, 255, 255)\n elif w[1] == RoadOption.CHANGELANELEFT: # Orange\n color = carla.Color(255, 64, 0)\n elif w[1] == RoadOption.CHANGELANERIGHT: # Dark Cyan\n color = carla.Color(0, 64, 255)\n elif w[1] == RoadOption.STRAIGHT: # Gray\n color = carla.Color(128, 128, 128)\n else: # LANEFOLLOW\n color = carla.Color(0, 255, 0) # Green\n size = 0.1\n\n world.debug.draw_point(wp, size=size, color=color, life_time=persistency)\n\n world.debug.draw_point(waypoints[0][0].location + carla.Location(z=vertical_shift), size=0.2,\n color=carla.Color(0, 0, 255), life_time=persistency)\n world.debug.draw_point(waypoints[-1][0].location + carla.Location(z=vertical_shift), size=0.2,\n color=carla.Color(255, 0, 0), life_time=persistency)\n\n def _scenario_sampling(self, potential_scenarios_definitions, random_seed=0):\n \"\"\"\n The function used to sample the scenarios that are going to happen for this route.\n \"\"\"\n\n # fix the random seed for reproducibility\n rgn = random.RandomState(random_seed)\n\n def position_sampled(scenario_choice, sampled_scenarios):\n \"\"\"\n Check if a position was already sampled, i.e. used for another scenario\n \"\"\"\n for existent_scenario in sampled_scenarios:\n # If the scenarios have equal positions then it is true.\n if compare_scenarios(scenario_choice, existent_scenario):\n return True\n\n return False\n\n # def select_scenario(list_scenarios):\n # # priority to the scenarios with higher number: 10 has priority over 9, etc.\n # higher_id = -1\n # selected_scenario = None\n # for scenario in list_scenarios:\n # try:\n # scenario_number = int(scenario['name'].split('Scenario')[1])\n # except:\n # scenario_number = -1\n\n # if scenario_number >= higher_id:\n # higher_id = scenario_number\n # selected_scenario = scenario\n\n # return selected_scenario\n\n\n def select_scenario(list_scenarios):\n # priority to the scenarios with higher number: 10 has priority over 9, etc.\n selected_scenario = None\n for scenario in list_scenarios:\n try:\n scenario_number = int(scenario['name'].split('Scenario')[1])\n except:\n scenario_number = -1\n\n if scenario_number == 3:\n selected_scenario = scenario\n break\n if selected_scenario is None:\n return rgn.choice(list_scenarios)\n\n return selected_scenario\n\n def select_scenario_randomly(list_scenarios):\n # randomly select a scenario\n return rgn.choice(list_scenarios)\n\n # The idea is to randomly sample a scenario per trigger position.\n self.is_eval = (os.environ['IS_EVAL'] == \"True\")\n print(\"IS_EVAL\", self.is_eval)\n sampled_scenarios = []\n for trigger in potential_scenarios_definitions.keys():\n possible_scenarios = potential_scenarios_definitions[trigger]\n if self.is_eval:\n scenario_choice = select_scenario(possible_scenarios) # original prioritized sampling\n else:\n scenario_choice = select_scenario_randomly(possible_scenarios) # random sampling\n del possible_scenarios[possible_scenarios.index(scenario_choice)]\n # We keep sampling and testing if this position is present on any of the scenarios.\n while position_sampled(scenario_choice, sampled_scenarios):\n if possible_scenarios is None or not possible_scenarios:\n scenario_choice = None\n break\n scenario_choice = rgn.choice(possible_scenarios)\n del possible_scenarios[possible_scenarios.index(scenario_choice)]\n\n if scenario_choice is not None:\n sampled_scenarios.append(scenario_choice)\n\n return sampled_scenarios\n\n def _build_scenario_instances(self, world, ego_vehicle, scenario_definitions,\n scenarios_per_tick=5, timeout=300, debug_mode=False):\n \"\"\"\n Based on the parsed route and possible scenarios, build all the scenario classes.\n \"\"\"\n scenario_instance_vec = []\n\n if debug_mode:\n for scenario in scenario_definitions:\n loc = carla.Location(scenario['trigger_position']['x'],\n scenario['trigger_position']['y'],\n scenario['trigger_position']['z']) + carla.Location(z=2.0)\n world.debug.draw_point(loc, size=0.3, color=carla.Color(255, 0, 0), life_time=100000)\n world.debug.draw_string(loc, str(scenario['name']), draw_shadow=False,\n color=carla.Color(0, 0, 255), life_time=100000, persistent_lines=True)\n\n for scenario_number, definition in enumerate(scenario_definitions):\n # Get the class possibilities for this scenario number\n scenario_class = NUMBER_CLASS_TRANSLATION[definition['name']]\n\n # Create the other actors that are going to appear\n if definition['other_actors'] is not None:\n list_of_actor_conf_instances = self._get_actors_instances(definition['other_actors'])\n else:\n list_of_actor_conf_instances = []\n # Create an actor configuration for the ego-vehicle trigger position\n\n egoactor_trigger_position = convert_json_to_transform(definition['trigger_position'])\n scenario_configuration = ScenarioConfiguration()\n scenario_configuration.other_actors = list_of_actor_conf_instances\n scenario_configuration.trigger_points = [egoactor_trigger_position]\n scenario_configuration.subtype = definition['scenario_type']\n scenario_configuration.ego_vehicles = [ActorConfigurationData('vehicle.lincoln.mkz2017',\n ego_vehicle.get_transform(),\n 'hero')]\n route_var_name = \"ScenarioRouteNumber{}\".format(scenario_number)\n scenario_configuration.route_var_name = route_var_name\n try:\n scenario_instance = scenario_class(world, [ego_vehicle], scenario_configuration,\n criteria_enable=False, timeout=timeout)\n # Do a tick every once in a while to avoid spawning everything at the same time\n if scenario_number % scenarios_per_tick == 0:\n if CarlaDataProvider.is_sync_mode():\n world.tick()\n else:\n world.wait_for_tick()\n\n except Exception as e:\n print(\"Skipping scenario '{}' due to setup error: {}\".format(definition['name'], e))\n continue\n\n scenario_instance_vec.append(scenario_instance)\n\n return scenario_instance_vec\n\n def _get_actors_instances(self, list_of_antagonist_actors):\n \"\"\"\n Get the full list of actor instances.\n \"\"\"\n\n def get_actors_from_list(list_of_actor_def):\n \"\"\"\n Receives a list of actor definitions and creates an actual list of ActorConfigurationObjects\n \"\"\"\n sublist_of_actors = []\n for actor_def in list_of_actor_def:\n sublist_of_actors.append(convert_json_to_actor(actor_def))\n\n return sublist_of_actors\n\n list_of_actors = []\n # Parse vehicles to the left\n if 'front' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['front'])\n\n if 'left' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['left'])\n\n if 'right' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['right'])\n\n return list_of_actors\n\n # pylint: enable=no-self-use\n\n def _initialize_actors(self, config):\n \"\"\"\n Set other_actors to the superset of all scenario actors\n \"\"\"\n # Create the background activity of the route\n #TODO: tune to have some vehicle\n town_amount = {\n 'Town01': 120,\n 'Town02': 100,\n 'Town03': 120,\n 'Town04': 200,\n 'Town05': 120,\n 'Town06': 150,\n 'Town07': 110,\n 'Town08': 180,\n 'Town09': 300,\n 'Town10HD': 120, # town10 doesn't load properly for some reason\n }\n amount = town_amount[config.town] if config.town in town_amount else 0\n if os.environ[\"BENCHMARK\"] == \"town05long\":\n amount = 120\n print(\"----------------Eval with Town05 Long, amount=120\", flush=True)\n if os.environ[\"BENCHMARK\"] == \"longest6\":\n amount = 500\n print(\"----------------Eval with Longest6, amount=500\", flush=True)\n new_actors = CarlaDataProvider.request_new_batch_actors('vehicle.*',\n amount,\n carla.Transform(),\n autopilot=True,\n random_location=True,\n rolename='background')\n if new_actors is None:\n raise Exception(\"Error: Unable to add the background activity, all spawn points were occupied\")\n\n for _actor in new_actors:\n self.other_actors.append(_actor)\n\n # Add all the actors of the specific scenarios to self.other_actors\n for scenario in self.list_scenarios:\n self.other_actors.extend(scenario.other_actors)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n scenario_trigger_distance = 1.5 # Max trigger distance between route and scenario\n\n behavior = py_trees.composites.Parallel(policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n subbehavior = py_trees.composites.Parallel(name=\"Behavior\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n\n scenario_behaviors = []\n blackboard_list = []\n\n for i, scenario in enumerate(self.list_scenarios):\n if scenario.scenario.behavior is not None:\n route_var_name = scenario.config.route_var_name\n\n if route_var_name is not None:\n scenario_behaviors.append(scenario.scenario.behavior)\n blackboard_list.append([scenario.config.route_var_name,\n scenario.config.trigger_points[0].location])\n else:\n name = \"{} - {}\".format(i, scenario.scenario.behavior.name)\n oneshot_idiom = oneshot_behavior(\n name=name,\n variable_name=name,\n behaviour=scenario.scenario.behavior)\n scenario_behaviors.append(oneshot_idiom)\n\n # Add behavior that manages the scenarios trigger conditions\n scenario_triggerer = ScenarioTriggerer(\n self.ego_vehicles[0],\n self.route,\n blackboard_list,\n scenario_trigger_distance,\n repeat_scenarios=False\n )\n\n subbehavior.add_child(scenario_triggerer) # make ScenarioTriggerer the first thing to be checked\n subbehavior.add_children(scenario_behaviors)\n subbehavior.add_child(Idle()) # The behaviours cannot make the route scenario stop\n behavior.add_child(subbehavior)\n return behavior\n\n def _create_test_criteria(self):\n \"\"\"\n \"\"\"\n criteria = []\n route = convert_transform_to_location(self.route)\n collision_criterion = CollisionTest(self.ego_vehicles[0], terminate_on_failure=(not self.is_eval))\n\n route_criterion = InRouteTest(self.ego_vehicles[0],\n route=route,\n offroad_max=30,\n terminate_on_failure=True)\n \n completion_criterion = RouteCompletionTest(self.ego_vehicles[0], route=route)\n\n outsidelane_criterion = OutsideRouteLanesTest(self.ego_vehicles[0], route=route)\n\n red_light_criterion = RunningRedLightTest(self.ego_vehicles[0], terminate_on_failure=(not self.is_eval))\n\n stop_criterion = RunningStopTest(self.ego_vehicles[0])\n\n blocked_criterion = ActorSpeedAboveThresholdTest(self.ego_vehicles[0],\n speed_threshold=0.1,\n below_threshold_max_time=90.0,\n terminate_on_failure=True,\n name=\"AgentBlockedTest\")\n\n criteria.append(completion_criterion)\n criteria.append(outsidelane_criterion)\n criteria.append(collision_criterion)\n criteria.append(red_light_criterion)\n criteria.append(stop_criterion)\n criteria.append(route_criterion)\n criteria.append(blocked_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "leaderboard/leaderboard/scenarios/scenario_manager.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the ScenarioManager implementations.\nIt must not be modified and is for reference only!\n\"\"\"\n\nfrom __future__ import print_function\nimport signal\nimport sys\nimport time\n\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenariomanager.watchdog import Watchdog\n\nfrom leaderboard.autoagents.agent_wrapper import AgentWrapper, AgentError\nfrom leaderboard.envs.sensor_interface import SensorReceivedNoData\nfrom leaderboard.utils.result_writer import ResultOutputProvider\n\n\nclass ScenarioManager(object):\n\n \"\"\"\n Basic scenario manager class. This class holds all functionality\n required to start, run and stop a scenario.\n\n The user must not modify this class.\n\n To use the ScenarioManager:\n 1. Create an object via manager = ScenarioManager()\n 2. Load a scenario via manager.load_scenario()\n 3. Trigger the execution of the scenario manager.run_scenario()\n This function is designed to explicitly control start and end of\n the scenario execution\n 4. If needed, cleanup with manager.stop_scenario()\n \"\"\"\n\n\n def __init__(self, timeout, debug_mode=False):\n \"\"\"\n Setups up the parameters, which will be filled at load_scenario()\n \"\"\"\n self.scenario = None\n self.scenario_tree = None\n self.scenario_class = None\n self.ego_vehicles = None\n self.other_actors = None\n\n self._debug_mode = debug_mode\n self._agent = None\n self._running = False\n self._timestamp_last_run = 0.0\n self._timeout = float(timeout)\n\n # Used to detect if the simulation is down\n watchdog_timeout = max(5, self._timeout - 2)\n self._watchdog = Watchdog(watchdog_timeout)\n\n # Avoid the agent from freezing the simulation\n agent_timeout = watchdog_timeout - 1\n self._agent_watchdog = Watchdog(agent_timeout)\n\n self.scenario_duration_system = 0.0\n self.scenario_duration_game = 0.0\n self.start_system_time = None\n self.end_system_time = None\n self.end_game_time = None\n\n # Register the scenario tick as callback for the CARLA world\n # Use the callback_id inside the signal handler to allow external interrupts\n signal.signal(signal.SIGINT, self.signal_handler)\n\n def signal_handler(self, signum, frame):\n \"\"\"\n Terminate scenario ticking when receiving a signal interrupt\n \"\"\"\n self._running = False\n\n def cleanup(self):\n \"\"\"\n Reset all parameters\n \"\"\"\n self._timestamp_last_run = 0.0\n self.scenario_duration_system = 0.0\n self.scenario_duration_game = 0.0\n self.start_system_time = None\n self.end_system_time = None\n self.end_game_time = None\n\n def load_scenario(self, scenario, agent, rep_number):\n \"\"\"\n Load a new scenario\n \"\"\"\n\n GameTime.restart()\n self._agent = AgentWrapper(agent)\n self.scenario_class = scenario\n self.scenario = scenario.scenario\n self.scenario_tree = self.scenario.scenario_tree\n self.ego_vehicles = scenario.ego_vehicles\n self.other_actors = scenario.other_actors\n self.repetition_number = rep_number\n\n # To print the scenario tree uncomment the next line\n # py_trees.display.render_dot_tree(self.scenario_tree)\n CarlaDataProvider.set_ego(self.ego_vehicles[0])\n\n self._agent.setup_sensors(self.ego_vehicles[0], self._debug_mode)\n\n def run_scenario(self):\n \"\"\"\n Trigger the start of the scenario and wait for it to finish/fail\n \"\"\"\n self.start_system_time = time.time()\n self.start_game_time = GameTime.get_time()\n\n self._watchdog.start()\n self._running = True\n\n while self._running:\n timestamp = None\n world = CarlaDataProvider.get_world()\n CarlaDataProvider.set_time_step((GameTime.get_time()-self.start_game_time)*20) #20Hz\n if world:\n snapshot = world.get_snapshot()\n if snapshot:\n timestamp = snapshot.timestamp\n if timestamp:\n self._tick_scenario(timestamp)\n\n def _tick_scenario(self, timestamp):\n \"\"\"\n Run next tick of scenario and the agent and tick the world.\n \"\"\"\n\n if self._timestamp_last_run < timestamp.elapsed_seconds and self._running:\n self._timestamp_last_run = timestamp.elapsed_seconds\n\n self._watchdog.update()\n # Update game time and actor information\n GameTime.on_carla_tick(timestamp)\n CarlaDataProvider.on_carla_tick()\n\n try:\n ego_action = self._agent()\n\n # Special exception inside the agent that isn't caused by the agent\n except SensorReceivedNoData as e:\n raise RuntimeError(e)\n\n except Exception as e:\n raise AgentError(e)\n\n self.ego_vehicles[0].apply_control(ego_action)\n\n # Tick scenario\n self.scenario_tree.tick_once()\n\n if self._debug_mode:\n print(\"\\n\")\n py_trees.display.print_ascii_tree(\n self.scenario_tree, show_status=True)\n sys.stdout.flush()\n\n if self.scenario_tree.status != py_trees.common.Status.RUNNING:\n self._running = False\n\n spectator = CarlaDataProvider.get_world().get_spectator()\n ego_trans = self.ego_vehicles[0].get_transform()\n spectator.set_transform(carla.Transform(ego_trans.location + carla.Location(z=50),\n carla.Rotation(pitch=-90)))\n\n if self._running and self.get_running_status():\n CarlaDataProvider.get_world().tick(self._timeout)\n\n def get_running_status(self):\n \"\"\"\n returns:\n bool: False if watchdog exception occured, True otherwise\n \"\"\"\n return self._watchdog.get_status()\n\n def stop_scenario(self):\n \"\"\"\n This function triggers a proper termination of a scenario\n \"\"\"\n self._watchdog.stop()\n\n self.end_system_time = time.time()\n self.end_game_time = GameTime.get_time()\n\n self.scenario_duration_system = self.end_system_time - self.start_system_time\n self.scenario_duration_game = self.end_game_time - self.start_game_time\n\n if self.get_running_status():\n if self.scenario is not None:\n self.scenario.terminate()\n\n if self._agent is not None:\n self._agent.cleanup()\n self._agent = None\n\n self.analyze_scenario()\n\n def analyze_scenario(self):\n \"\"\"\n Analyzes and prints the results of the route\n \"\"\"\n global_result = '\\033[92m'+'SUCCESS'+'\\033[0m'\n\n for criterion in self.scenario.get_criteria():\n if criterion.test_status != \"SUCCESS\":\n global_result = '\\033[91m'+'FAILURE'+'\\033[0m'\n\n if self.scenario.timeout_node.timeout:\n global_result = '\\033[91m'+'FAILURE'+'\\033[0m'\n\n ResultOutputProvider(self, global_result)\n" }, { "path": "leaderboard/leaderboard/scenarios/scenarioatomics/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/scenarios/scenarioatomics/atomic_criteria.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides additional atomic evaluation criteria required\nto analyze if a scenario was completed successfully or failed.\n\nCriteria should run continuously to monitor the state of a single actor, multiple\nactors or environmental parameters. Hence, a termination is not required.\n\nThe atomic criteria are implemented with py_trees.\n\"\"\"\n\nimport py_trees\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import Criterion\nfrom srunner.scenariomanager.traffic_events import TrafficEvent, TrafficEventType\n\n\nclass ActorSpeedAboveThresholdTest(Criterion):\n \"\"\"\n This test will fail if the actor has had its linear velocity lower than a specific value for\n a specific amount of time\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - speed_threshold: speed required\n - below_threshold_max_time: Maximum time (in seconds) the actor can remain under the speed threshold\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n\n def __init__(self, actor, speed_threshold, below_threshold_max_time,\n name=\"ActorSpeedAboveThresholdTest\", terminate_on_failure=False):\n \"\"\"\n Class constructor.\n \"\"\"\n super(ActorSpeedAboveThresholdTest, self).__init__(name, actor, 0, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._speed_threshold = speed_threshold\n self._below_threshold_max_time = below_threshold_max_time\n self._time_last_valid_state = None\n\n def update(self):\n \"\"\"\n Check if the actor speed is above the speed_threshold\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n linear_speed = CarlaDataProvider.get_velocity(self._actor)\n if linear_speed is not None:\n if linear_speed < self._speed_threshold and self._time_last_valid_state:\n if (GameTime.get_time() - self._time_last_valid_state) > self._below_threshold_max_time:\n # Game over. The actor has been \"blocked\" for too long\n self.test_status = \"FAILURE\"\n\n # record event\n vehicle_location = CarlaDataProvider.get_location(self._actor)\n blocked_event = TrafficEvent(event_type=TrafficEventType.VEHICLE_BLOCKED)\n ActorSpeedAboveThresholdTest._set_event_message(blocked_event, vehicle_location)\n ActorSpeedAboveThresholdTest._set_event_dict(blocked_event, vehicle_location)\n self.list_traffic_events.append(blocked_event)\n else:\n self._time_last_valid_state = GameTime.get_time()\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n @staticmethod\n def _set_event_message(event, location):\n \"\"\"\n Sets the message of the event\n \"\"\"\n\n event.set_message('Agent got blocked at (x={}, y={}, z={})'.format(round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3)))\n @staticmethod\n def _set_event_dict(event, location):\n \"\"\"\n Sets the dictionary of the event\n \"\"\"\n event.set_dict({\n 'x': location.x,\n 'y': location.y,\n 'z': location.z,\n })\n" }, { "path": "leaderboard/leaderboard/utils/__init__.py", "content": "" }, { "path": "leaderboard/leaderboard/utils/checkpoint_tools.py", "content": "import json\ntry:\n import simplejson as json\nexcept ImportError:\n import json\nimport requests\nimport os.path\n\n\ndef autodetect_proxy():\n proxies = {}\n\n proxy_https = os.getenv('HTTPS_PROXY', os.getenv('https_proxy', None))\n proxy_http = os.getenv('HTTP_PROXY', os.getenv('http_proxy', None))\n\n if proxy_https:\n proxies['https'] = proxy_https\n if proxy_http:\n proxies['http'] = proxy_http\n\n return proxies\n\n\ndef fetch_dict(endpoint):\n data = None\n if endpoint.startswith(('http:', 'https:', 'ftp:')):\n proxies = autodetect_proxy()\n\n if proxies:\n response = requests.get(url=endpoint, proxies=proxies)\n else:\n response = requests.get(url=endpoint)\n\n try:\n data = response.json()\n except json.decoder.JSONDecodeError:\n data = {}\n else:\n data = {}\n if os.path.exists(endpoint):\n with open(endpoint) as fd:\n try:\n data = json.load(fd)\n except json.JSONDecodeError:\n data = {}\n\n return data\n\n\ndef create_default_json_msg():\n msg = {\n \"sensors\": [],\n \"values\": [],\n \"labels\": [],\n \"entry_status\": \"\",\n \"eligible\": \"\",\n \"_checkpoint\": {\n \"progress\": [],\n \"records\": [],\n \"global_record\": {}\n },\n }\n\n return msg\n\n\ndef save_dict(endpoint, data):\n if endpoint.startswith(('http:', 'https:', 'ftp:')):\n proxies = autodetect_proxy()\n\n if proxies:\n _ = requests.patch(url=endpoint, headers={'content-type':'application/json'}, data=json.dumps(data, indent=4, sort_keys=True), proxies=proxies)\n else:\n _ = requests.patch(url=endpoint, headers={'content-type':'application/json'}, data=json.dumps(data, indent=4, sort_keys=True))\n else:\n with open(endpoint, 'w') as fd:\n json.dump(data, fd, indent=4, sort_keys=True)\n" }, { "path": "leaderboard/leaderboard/utils/result_writer.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module contains the result gatherer and write for CARLA scenarios.\nIt shall be used from the ScenarioManager only.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport time\nfrom tabulate import tabulate\n\n\nclass ResultOutputProvider(object):\n\n \"\"\"\n This module contains the _result gatherer and write for CARLA scenarios.\n It shall be used from the ScenarioManager only.\n \"\"\"\n\n def __init__(self, data, global_result):\n \"\"\"\n - data contains all scenario-related information\n - global_result is overall pass/fail info\n \"\"\"\n self._data = data\n self._global_result = global_result\n\n self._start_time = time.strftime('%Y-%m-%d %H:%M:%S',\n time.localtime(self._data.start_system_time))\n self._end_time = time.strftime('%Y-%m-%d %H:%M:%S',\n time.localtime(self._data.end_system_time))\n\n print(self.create_output_text())\n\n def create_output_text(self):\n \"\"\"\n Creates the output message\n \"\"\"\n\n # Create the title\n output = \"\\n\"\n output += \"\\033[1m========= Results of {} (repetition {}) ------ {} \\033[1m=========\\033[0m\\n\".format(\n self._data.scenario_tree.name, self._data.repetition_number, self._global_result)\n output += \"\\n\"\n\n # Simulation part\n system_time = round(self._data.scenario_duration_system, 2)\n game_time = round(self._data.scenario_duration_game, 2)\n ratio = round(self._data.scenario_duration_game / self._data.scenario_duration_system, 3)\n\n list_statistics = [[\"Start Time\", \"{}\".format(self._start_time)]]\n list_statistics.extend([[\"End Time\", \"{}\".format(self._end_time)]])\n list_statistics.extend([[\"Duration (System Time)\", \"{}s\".format(system_time)]])\n list_statistics.extend([[\"Duration (Game Time)\", \"{}s\".format(game_time)]])\n list_statistics.extend([[\"Ratio (System Time / Game Time)\", \"{}\".format(ratio)]])\n\n output += tabulate(list_statistics, tablefmt='fancy_grid')\n output += \"\\n\\n\"\n\n # Criteria part\n header = ['Criterion', 'Result', 'Value']\n list_statistics = [header]\n\n for criterion in self._data.scenario.get_criteria():\n\n actual_value = criterion.actual_value\n expected_value = criterion.expected_value_success\n name = criterion.name\n\n result = criterion.test_status\n\n if result == \"SUCCESS\":\n result = '\\033[92m'+'SUCCESS'+'\\033[0m'\n elif result == \"FAILURE\":\n result = '\\033[91m'+'FAILURE'+'\\033[0m'\n\n if name == \"RouteCompletionTest\":\n actual_value = str(actual_value) + \" %\"\n elif name == \"OutsideRouteLanesTest\":\n actual_value = str(actual_value) + \" %\"\n elif name == \"CollisionTest\":\n actual_value = str(actual_value) + \" times\"\n elif name == \"RunningRedLightTest\":\n actual_value = str(actual_value) + \" times\"\n elif name == \"RunningStopTest\":\n actual_value = str(actual_value) + \" times\"\n elif name == \"InRouteTest\":\n actual_value = \"\"\n elif name == \"AgentBlockedTest\":\n actual_value = \"\"\n\n list_statistics.extend([[name, result, actual_value]])\n\n # Timeout\n name = \"Timeout\"\n\n actual_value = self._data.scenario_duration_game\n expected_value = self._data.scenario.timeout\n\n if self._data.scenario_duration_game < self._data.scenario.timeout:\n result = '\\033[92m'+'SUCCESS'+'\\033[0m'\n else:\n result = '\\033[91m'+'FAILURE'+'\\033[0m'\n\n list_statistics.extend([[name, result, '']])\n\n output += tabulate(list_statistics, tablefmt='fancy_grid')\n output += \"\\n\"\n\n return output\n" }, { "path": "leaderboard/leaderboard/utils/route_indexer.py", "content": "from collections import OrderedDict\nfrom dictor import dictor\n\nimport copy\n\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteScenarioConfiguration\n\n\nfrom leaderboard.utils.route_parser import RouteParser\nfrom leaderboard.utils.checkpoint_tools import fetch_dict, create_default_json_msg, save_dict\n\n\nclass RouteIndexer():\n def __init__(self, routes_file, scenarios_file, repetitions):\n self._routes_file = routes_file\n self._scenarios_file = scenarios_file\n self._repetitions = repetitions\n self._configs_dict = OrderedDict()\n self._configs_list = []\n self.routes_length = []\n self._index = 0\n\n # retrieve routes\n route_configurations = RouteParser.parse_routes_file(self._routes_file, self._scenarios_file, False)\n\n self.n_routes = len(route_configurations)\n self.total = self.n_routes*self._repetitions\n\n for i, config in enumerate(route_configurations):\n for repetition in range(repetitions):\n config.index = i * self._repetitions + repetition\n config.repetition_index = repetition\n self._configs_dict['{}.{}'.format(config.name, repetition)] = copy.copy(config)\n\n self._configs_list = list(self._configs_dict.items())\n\n def peek(self):\n return not (self._index >= len(self._configs_list))\n\n def next(self):\n if self._index >= len(self._configs_list):\n return None\n\n key, config = self._configs_list[self._index]\n self._index += 1\n\n return config\n\n def resume(self, endpoint):\n data = fetch_dict(endpoint)\n\n if data:\n checkpoint_dict = dictor(data, '_checkpoint')\n if checkpoint_dict and 'progress' in checkpoint_dict:\n progress = checkpoint_dict['progress']\n if not progress:\n current_route = 0\n else:\n current_route, total_routes = progress\n if current_route <= self.total:\n self._index = current_route\n else:\n print('Problem reading checkpoint. Route id {} '\n 'larger than maximum number of routes {}'.format(current_route, self.total))\n\n def save_state(self, endpoint):\n data = fetch_dict(endpoint)\n if not data:\n data = create_default_json_msg()\n data['_checkpoint']['progress'] = [self._index, self.total]\n\n save_dict(endpoint, data)\n" }, { "path": "leaderboard/leaderboard/utils/route_manipulation.py", "content": "#!/usr/bin/env python\n# Copyright (c) 2018-2019 Intel Labs.\n# authors: German Ros (german.ros@intel.com), Felipe Codevilla (felipe.alcm@gmail.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nModule to manipulate the routes, by making then more or less dense (Up to a certain parameter).\nIt also contains functions to convert the CARLA world location do GPS coordinates.\n\"\"\"\n\nimport math\nimport xml.etree.ElementTree as ET\n\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\nfrom agents.navigation.local_planner import RoadOption\n\n\ndef _location_to_gps(lat_ref, lon_ref, location):\n \"\"\"\n Convert from world coordinates to GPS coordinates\n :param lat_ref: latitude reference for the current map\n :param lon_ref: longitude reference for the current map\n :param location: location to translate\n :return: dictionary with lat, lon and height\n \"\"\"\n\n EARTH_RADIUS_EQUA = 6378137.0 # pylint: disable=invalid-name\n scale = math.cos(lat_ref * math.pi / 180.0)\n mx = scale * lon_ref * math.pi * EARTH_RADIUS_EQUA / 180.0\n my = scale * EARTH_RADIUS_EQUA * math.log(math.tan((90.0 + lat_ref) * math.pi / 360.0))\n mx += location.x\n my -= location.y\n\n lon = mx * 180.0 / (math.pi * EARTH_RADIUS_EQUA * scale)\n lat = 360.0 * math.atan(math.exp(my / (EARTH_RADIUS_EQUA * scale))) / math.pi - 90.0\n z = location.z\n\n return {'lat': lat, 'lon': lon, 'z': z}\n\n\ndef location_route_to_gps(route, lat_ref, lon_ref):\n \"\"\"\n Locate each waypoint of the route into gps, (lat long ) representations.\n :param route:\n :param lat_ref:\n :param lon_ref:\n :return:\n \"\"\"\n gps_route = []\n\n for transform, connection in route:\n gps_point = _location_to_gps(lat_ref, lon_ref, transform.location)\n gps_route.append((gps_point, connection))\n\n return gps_route\n\n\ndef _get_latlon_ref(world):\n \"\"\"\n Convert from waypoints world coordinates to CARLA GPS coordinates\n :return: tuple with lat and lon coordinates\n \"\"\"\n xodr = world.get_map().to_opendrive()\n tree = ET.ElementTree(ET.fromstring(xodr))\n\n # default reference\n lat_ref = 42.0\n lon_ref = 2.0\n\n for opendrive in tree.iter(\"OpenDRIVE\"):\n for header in opendrive.iter(\"header\"):\n for georef in header.iter(\"geoReference\"):\n if georef.text:\n str_list = georef.text.split(' ')\n for item in str_list:\n if '+lat_0' in item:\n lat_ref = float(item.split('=')[1])\n if '+lon_0' in item:\n lon_ref = float(item.split('=')[1])\n return lat_ref, lon_ref\n\n\ndef downsample_route(route, sample_factor):\n \"\"\"\n Downsample the route by some factor.\n :param route: the trajectory , has to contain the waypoints and the road options\n :param sample_factor: Maximum distance between samples\n :return: returns the ids of the final route that can\n \"\"\"\n\n ids_to_sample = []\n prev_option = None\n dist = 0\n\n for i, point in enumerate(route):\n curr_option = point[1]\n\n # Lane changing\n if curr_option in (RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT):\n ids_to_sample.append(i)\n dist = 0\n\n # When road option changes\n elif prev_option != curr_option and prev_option not in (RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT):\n ids_to_sample.append(i)\n dist = 0\n\n # After a certain max distance\n elif dist > sample_factor:\n ids_to_sample.append(i)\n dist = 0\n\n # At the end\n elif i == len(route) - 1:\n ids_to_sample.append(i)\n dist = 0\n\n # Compute the distance traveled\n else:\n curr_location = point[0].location\n prev_location = route[i-1][0].location\n dist += curr_location.distance(prev_location)\n\n prev_option = curr_option\n\n return ids_to_sample\n\n\ndef interpolate_trajectory(world, waypoints_trajectory, hop_resolution=1.0):\n \"\"\"\n Given some raw keypoints interpolate a full dense trajectory to be used by the user.\n returns the full interpolated route both in GPS coordinates and also in its original form.\n \n Args:\n - world: an reference to the CARLA world so we can use the planner\n - waypoints_trajectory: the current coarse trajectory\n - hop_resolution: is the resolution, how dense is the provided trajectory going to be made\n \"\"\"\n\n dao = GlobalRoutePlannerDAO(world.get_map(), hop_resolution)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n # Obtain route plan\n route = []\n wp_route = []\n for i in range(len(waypoints_trajectory) - 1): # Goes until the one before the last.\n\n waypoint = waypoints_trajectory[i]\n waypoint_next = waypoints_trajectory[i + 1]\n interpolated_trace = grp.trace_route(waypoint, waypoint_next)\n for wp_tuple in interpolated_trace:\n route.append((wp_tuple[0].transform, wp_tuple[1]))\n wp_route.append((wp_tuple[0], wp_tuple[1]))\n\n lat_ref, lon_ref = _get_latlon_ref(world)\n\n return location_route_to_gps(route, lat_ref, lon_ref), route, wp_route\n" }, { "path": "leaderboard/leaderboard/utils/route_parser.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nModule used to parse all the route and scenario configuration parameters.\n\"\"\"\nfrom collections import OrderedDict\nimport json\nimport math\nimport xml.etree.ElementTree as ET\n\nimport carla\nfrom agents.navigation.local_planner import RoadOption\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteScenarioConfiguration\n\n# check this threshold, it could be a bit larger but not so large that we cluster scenarios.\nTRIGGER_THRESHOLD = 2.0 # Threshold to say if a trigger position is new or repeated, works for matching positions\nTRIGGER_ANGLE_THRESHOLD = 10 # Threshold to say if two angles can be considering matching when matching transforms.\n\n# for loading predefined weathers while parsing routes\nWEATHERS = {\n '1': carla.WeatherParameters.ClearNoon,\n '2': carla.WeatherParameters.ClearSunset,\n '3': carla.WeatherParameters.CloudyNoon,\n '4': carla.WeatherParameters.CloudySunset,\n '5': carla.WeatherParameters.WetNoon,\n '6': carla.WeatherParameters.WetSunset,\n '7': carla.WeatherParameters.MidRainyNoon,\n '8': carla.WeatherParameters.MidRainSunset,\n '9': carla.WeatherParameters.WetCloudyNoon,\n '10': carla.WeatherParameters.WetCloudySunset,\n '11': carla.WeatherParameters.HardRainNoon,\n '12': carla.WeatherParameters.HardRainSunset,\n '13': carla.WeatherParameters.SoftRainNoon,\n '14': carla.WeatherParameters.SoftRainSunset,\n}\n\n\nclass RouteParser(object):\n\n \"\"\"\n Pure static class used to parse all the route and scenario configuration parameters.\n \"\"\"\n\n @staticmethod\n def parse_annotations_file(annotation_filename):\n \"\"\"\n Return the annotations of which positions where the scenarios are going to happen.\n :param annotation_filename: the filename for the anotations file\n :return:\n \"\"\"\n with open(annotation_filename, 'r') as f:\n annotation_dict = json.loads(f.read(), object_pairs_hook=OrderedDict)\n\n final_dict = OrderedDict()\n\n for town_dict in annotation_dict['available_scenarios']:\n final_dict.update(town_dict)\n\n return final_dict # the file has a current maps name that is an one element vec\n\n @staticmethod\n def parse_routes_file(route_filename, scenario_file, single_route=None):\n \"\"\"\n Returns a list of route elements.\n :param route_filename: the path to a set of routes.\n :param single_route: If set, only this route shall be returned\n :return: List of dicts containing the waypoints, id and town of the routes\n \"\"\"\n\n list_route_descriptions = []\n tree = ET.parse(route_filename)\n for route in tree.iter(\"route\"):\n\n route_id = route.attrib['id']\n if single_route and route_id != single_route:\n continue\n\n new_config = RouteScenarioConfiguration()\n new_config.town = route.attrib['town']\n new_config.name = \"RouteScenario_{}\".format(route_id)\n new_config.weather = RouteParser.parse_weather(route) # default: parse_weather(route)\n new_config.scenario_file = scenario_file\n\n waypoint_list = [] # the list of waypoints that can be found on this route\n for waypoint in route.iter('waypoint'):\n waypoint_list.append(carla.Location(x=float(waypoint.attrib['x']),\n y=float(waypoint.attrib['y']),\n z=float(waypoint.attrib['z'])))\n\n new_config.trajectory = waypoint_list\n\n list_route_descriptions.append(new_config)\n\n return list_route_descriptions\n\n @staticmethod\n def parse_weather(route):\n \"\"\"\n Returns a carla.WeatherParameters with the corresponding weather for that route. If the route\n has no weather attribute, the default one is triggered.\n \"\"\"\n\n route_weather = route.find(\"weather\")\n\n if route_weather is None:\n\n weather = carla.WeatherParameters(sun_altitude_angle=70, cloudiness=30)\n\n else:\n weather = carla.WeatherParameters()\n for weather_attrib in route.iter(\"weather\"):\n\n if 'cloudiness' in weather_attrib.attrib:\n weather.cloudiness = float(weather_attrib.attrib['cloudiness']) \n if 'precipitation' in weather_attrib.attrib:\n weather.precipitation = float(weather_attrib.attrib['precipitation'])\n if 'precipitation_deposits' in weather_attrib.attrib:\n weather.precipitation_deposits =float(weather_attrib.attrib['precipitation_deposits'])\n if 'wind_intensity' in weather_attrib.attrib:\n weather.wind_intensity = float(weather_attrib.attrib['wind_intensity'])\n if 'sun_azimuth_angle' in weather_attrib.attrib:\n weather.sun_azimuth_angle = float(weather_attrib.attrib['sun_azimuth_angle'])\n if 'sun_altitude_angle' in weather_attrib.attrib:\n weather.sun_altitude_angle = float(weather_attrib.attrib['sun_altitude_angle'])\n if 'wetness' in weather_attrib.attrib:\n weather.wetness = float(weather_attrib.attrib['wetness'])\n if 'fog_distance' in weather_attrib.attrib:\n weather.fog_distance = float(weather_attrib.attrib['fog_distance'])\n if 'fog_density' in weather_attrib.attrib:\n weather.fog_density = float(weather_attrib.attrib['fog_density'])\n if 'fog_falloff' in weather_attrib.attrib:\n weather.fog_falloff = float(weather_attrib.attrib['fog_falloff'])\n\n return weather\n\n @staticmethod\n def parse_preset_weather(route):\n \"\"\"\n Returns one of the 14 preset weather condition. If the route\n has no weather attribute, the default one is triggered.\n \"\"\"\n\n if 'weather' not in route.attrib:\n weather = carla.WeatherParameters(sun_altitude_angle=70, cloudiness=30)\n else:\n weather = WEATHERS[route.attrib['weather']]\n\n return weather\n\n @staticmethod\n def check_trigger_position(new_trigger, existing_triggers):\n \"\"\"\n Check if this trigger position already exists or if it is a new one.\n :param new_trigger:\n :param existing_triggers:\n :return:\n \"\"\"\n\n for trigger_id in existing_triggers.keys():\n trigger = existing_triggers[trigger_id]\n dx = trigger['x'] - new_trigger['x']\n dy = trigger['y'] - new_trigger['y']\n distance = math.sqrt(dx * dx + dy * dy)\n\n dyaw = (trigger['yaw'] - new_trigger['yaw']) % 360\n if distance < TRIGGER_THRESHOLD \\\n and (dyaw < TRIGGER_ANGLE_THRESHOLD or dyaw > (360 - TRIGGER_ANGLE_THRESHOLD)):\n return trigger_id\n\n return None\n\n @staticmethod\n def convert_waypoint_float(waypoint):\n \"\"\"\n Convert waypoint values to float\n \"\"\"\n waypoint['x'] = float(waypoint['x'])\n waypoint['y'] = float(waypoint['y'])\n waypoint['z'] = float(waypoint['z'])\n waypoint['yaw'] = float(waypoint['yaw'])\n\n @staticmethod\n def match_world_location_to_route(world_location, route_description):\n \"\"\"\n We match this location to a given route.\n world_location:\n route_description:\n \"\"\"\n def match_waypoints(waypoint1, wtransform):\n \"\"\"\n Check if waypoint1 and wtransform are similar\n \"\"\"\n dx = float(waypoint1['x']) - wtransform.location.x\n dy = float(waypoint1['y']) - wtransform.location.y\n dz = float(waypoint1['z']) - wtransform.location.z\n dpos = math.sqrt(dx * dx + dy * dy + dz * dz)\n\n dyaw = (float(waypoint1['yaw']) - wtransform.rotation.yaw) % 360\n\n return dpos < TRIGGER_THRESHOLD \\\n and (dyaw < TRIGGER_ANGLE_THRESHOLD or dyaw > (360 - TRIGGER_ANGLE_THRESHOLD))\n\n match_position = 0\n # this function can be optimized to run on Log(N) time\n for route_waypoint in route_description:\n if match_waypoints(world_location, route_waypoint[0]):\n return match_position\n match_position += 1\n\n return None\n\n @staticmethod\n def get_scenario_type(scenario, match_position, trajectory):\n \"\"\"\n Some scenarios have different types depending on the route.\n :param scenario: the scenario name\n :param match_position: the matching position for the scenarion\n :param trajectory: the route trajectory the ego is following\n :return: tag representing this subtype\n\n Also used to check which are not viable (Such as an scenario\n that triggers when turning but the route doesnt')\n WARNING: These tags are used at:\n - VehicleTurningRoute\n - SignalJunctionCrossingRoute\n and changes to these tags will affect them\n \"\"\"\n\n def check_this_waypoint(tuple_wp_turn):\n \"\"\"\n Decides whether or not the waypoint will define the scenario behavior\n \"\"\"\n if RoadOption.LANEFOLLOW == tuple_wp_turn[1]:\n return False\n elif RoadOption.CHANGELANELEFT == tuple_wp_turn[1]:\n return False\n elif RoadOption.CHANGELANERIGHT == tuple_wp_turn[1]:\n return False\n return True\n\n # Unused tag for the rest of scenarios,\n # can't be None as they are still valid scenarios\n subtype = 'valid'\n\n if scenario == 'Scenario4':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S4left'\n elif RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S4right'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario7':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S7left'\n elif RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S7right'\n elif RoadOption.STRAIGHT == tuple_wp_turn[1]:\n subtype = 'S7opposite'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario8':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S8left'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario9':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S9right'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n return subtype\n\n @staticmethod\n def scan_route_for_scenarios(route_name, trajectory, world_annotations):\n \"\"\"\n Just returns a plain list of possible scenarios that can happen in this route by matching\n the locations from the scenario into the route description\n\n :return: A list of scenario definitions with their correspondent parameters\n \"\"\"\n\n # the triggers dictionaries:\n existent_triggers = OrderedDict()\n # We have a table of IDs and trigger positions associated\n possible_scenarios = OrderedDict()\n\n # Keep track of the trigger ids being added\n latest_trigger_id = 0\n\n for town_name in world_annotations.keys():\n if town_name != route_name:\n continue\n\n scenarios = world_annotations[town_name]\n for scenario in scenarios: # For each existent scenario\n scenario_name = scenario[\"scenario_type\"]\n for event in scenario[\"available_event_configurations\"]:\n waypoint = event['transform'] # trigger point of this scenario\n RouteParser.convert_waypoint_float(waypoint)\n # We match trigger point to the route, now we need to check if the route affects\n match_position = RouteParser.match_world_location_to_route(\n waypoint, trajectory)\n if match_position is not None:\n # We match a location for this scenario, create a scenario object so this scenario\n # can be instantiated later\n\n if 'other_actors' in event:\n other_vehicles = event['other_actors']\n else:\n other_vehicles = None\n scenario_subtype = RouteParser.get_scenario_type(scenario_name, match_position,\n trajectory)\n if scenario_subtype is None:\n continue\n scenario_description = {\n 'name': scenario_name,\n 'other_actors': other_vehicles,\n 'trigger_position': waypoint,\n 'scenario_type': scenario_subtype, # some scenarios have route dependent configurations\n }\n\n trigger_id = RouteParser.check_trigger_position(waypoint, existent_triggers)\n if trigger_id is None:\n # This trigger does not exist create a new reference on existent triggers\n existent_triggers.update({latest_trigger_id: waypoint})\n # Update a reference for this trigger on the possible scenarios\n possible_scenarios.update({latest_trigger_id: []})\n trigger_id = latest_trigger_id\n # Increment the latest trigger\n latest_trigger_id += 1\n\n possible_scenarios[trigger_id].append(scenario_description)\n\n return possible_scenarios, existent_triggers\n" }, { "path": "leaderboard/leaderboard/utils/statistics_manager.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module contains a statistics manager for the CARLA AD leaderboard\n\"\"\"\n\nfrom __future__ import print_function\n\nfrom dictor import dictor\nimport math\nimport sys\n\nfrom srunner.scenariomanager.traffic_events import TrafficEventType\n\nfrom leaderboard.utils.checkpoint_tools import fetch_dict, save_dict, create_default_json_msg\n\nPENALTY_COLLISION_PEDESTRIAN = 0.50\nPENALTY_COLLISION_VEHICLE = 0.60\nPENALTY_COLLISION_STATIC = 0.65\nPENALTY_TRAFFIC_LIGHT = 0.70\nPENALTY_STOP = 0.80\nimport os\nif os.environ[\"BENCHMARK\"] == \"longest6\":\n PENALTY_STOP = 1.0\n print(\"----------------Eval with Longest6, PENALTY_STOP=1.0\")\n\nclass RouteRecord():\n def __init__(self):\n self.route_id = None\n self.index = None\n self.status = 'Started'\n self.infractions = {\n 'collisions_pedestrian': [],\n 'collisions_vehicle': [],\n 'collisions_layout': [],\n 'red_light': [],\n 'stop_infraction': [],\n 'outside_route_lanes': [],\n 'route_dev': [],\n 'route_timeout': [],\n 'vehicle_blocked': []\n }\n\n self.scores = {\n 'score_route': 0,\n 'score_penalty': 0,\n 'score_composed': 0\n }\n\n self.meta = {}\n\n\ndef to_route_record(record_dict):\n record = RouteRecord()\n for key, value in record_dict.items():\n setattr(record, key, value)\n\n return record\n\n\ndef compute_route_length(config):\n trajectory = config.trajectory\n\n route_length = 0.0\n previous_location = None\n for location in trajectory:\n if previous_location:\n dist = math.sqrt((location.x-previous_location.x)*(location.x-previous_location.x) +\n (location.y-previous_location.y)*(location.y-previous_location.y) +\n (location.z - previous_location.z) * (location.z - previous_location.z))\n route_length += dist\n previous_location = location\n\n return route_length\n\n\nclass StatisticsManager(object):\n\n \"\"\"\n This is the statistics manager for the CARLA leaderboard.\n It gathers data at runtime via the scenario evaluation criteria.\n \"\"\"\n\n def __init__(self):\n self._master_scenario = None\n self._registry_route_records = []\n\n def resume(self, endpoint):\n data = fetch_dict(endpoint)\n\n if data and dictor(data, '_checkpoint.records'):\n records = data['_checkpoint']['records']\n\n for record in records:\n self._registry_route_records.append(to_route_record(record))\n\n def set_route(self, route_id, index):\n\n self._master_scenario = None\n route_record = RouteRecord()\n route_record.route_id = route_id\n route_record.index = index\n\n if index < len(self._registry_route_records):\n # the element already exists and therefore we update it\n self._registry_route_records[index] = route_record\n else:\n self._registry_route_records.append(route_record)\n\n def set_scenario(self, scenario):\n \"\"\"\n Sets the scenario from which the statistics willb e taken\n \"\"\"\n self._master_scenario = scenario\n\n def compute_route_statistics(self, config, duration_time_system=-1, duration_time_game=-1, failure=\"\"):\n \"\"\"\n Compute the current statistics by evaluating all relevant scenario criteria\n \"\"\"\n index = config.index\n\n if not self._registry_route_records or index >= len(self._registry_route_records):\n raise Exception('Critical error with the route registry.')\n\n # fetch latest record to fill in\n route_record = self._registry_route_records[index]\n\n target_reached = False\n score_penalty = 1.0\n score_route = 0.0\n\n route_record.meta['duration_system'] = duration_time_system\n route_record.meta['duration_game'] = duration_time_game\n route_record.meta['route_length'] = compute_route_length(config)\n route_record.meta['folder_name'] = config.folder_name\n route_record.meta['total_steps'] = config.agent.step\n\n if self._master_scenario:\n if self._master_scenario.timeout_node.timeout:\n route_record.infractions['route_timeout'].append('Route timeout.')\n failure = \"Agent timed out\"\n\n for node in self._master_scenario.get_criteria():\n if node.list_traffic_events:\n # analyze all traffic events\n for event in node.list_traffic_events:\n if event.get_type() == TrafficEventType.COLLISION_STATIC:\n score_penalty *= PENALTY_COLLISION_STATIC\n route_record.infractions['collisions_layout'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.COLLISION_PEDESTRIAN:\n score_penalty *= PENALTY_COLLISION_PEDESTRIAN\n route_record.infractions['collisions_pedestrian'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.COLLISION_VEHICLE:\n score_penalty *= PENALTY_COLLISION_VEHICLE\n route_record.infractions['collisions_vehicle'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.OUTSIDE_ROUTE_LANES_INFRACTION:\n score_penalty *= (1 - event.get_dict()['percentage'] / 100)\n route_record.infractions['outside_route_lanes'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.TRAFFIC_LIGHT_INFRACTION:\n score_penalty *= PENALTY_TRAFFIC_LIGHT\n route_record.infractions['red_light'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.ROUTE_DEVIATION:\n route_record.infractions['route_dev'].append(event.get_message())\n failure = \"Agent deviated from the route\"\n\n elif event.get_type() == TrafficEventType.STOP_INFRACTION:\n score_penalty *= PENALTY_STOP\n route_record.infractions['stop_infraction'].append(event.get_message())\n\n elif event.get_type() == TrafficEventType.VEHICLE_BLOCKED:\n route_record.infractions['vehicle_blocked'].append(event.get_message())\n failure = \"Agent got blocked\"\n\n elif event.get_type() == TrafficEventType.ROUTE_COMPLETED:\n score_route = 100.0\n target_reached = True\n elif event.get_type() == TrafficEventType.ROUTE_COMPLETION:\n if not target_reached:\n if event.get_dict():\n score_route = event.get_dict()['route_completed']\n else:\n score_route = 0\n\n # update route scores\n route_record.scores['score_route'] = score_route\n route_record.scores['score_penalty'] = score_penalty\n route_record.scores['score_composed'] = max(score_route*score_penalty, 0.0)\n\n # update status\n if target_reached:\n route_record.status = 'Completed'\n else:\n route_record.status = 'Failed'\n if failure:\n route_record.status += ' - ' + failure\n\n return route_record\n\n def compute_global_statistics(self, total_routes):\n global_record = RouteRecord()\n global_record.route_id = -1\n global_record.index = -1\n global_record.status = 'Completed'\n\n if self._registry_route_records:\n for route_record in self._registry_route_records:\n global_record.scores['score_route'] += route_record.scores['score_route']\n global_record.scores['score_penalty'] += route_record.scores['score_penalty']\n global_record.scores['score_composed'] += route_record.scores['score_composed']\n\n for key in global_record.infractions.keys():\n route_length_kms = max(route_record.scores['score_route'] * route_record.meta['route_length'] / 1000.0, 0.001)\n if isinstance(global_record.infractions[key], list):\n global_record.infractions[key] = len(route_record.infractions[key]) / route_length_kms\n else:\n global_record.infractions[key] += len(route_record.infractions[key]) / route_length_kms\n\n if route_record.status is not 'Completed':\n global_record.status = 'Failed'\n if 'exceptions' not in global_record.meta:\n global_record.meta['exceptions'] = []\n global_record.meta['exceptions'].append((route_record.route_id,\n route_record.index,\n route_record.status))\n\n global_record.scores['score_route'] /= float(total_routes)\n global_record.scores['score_penalty'] /= float(total_routes)\n global_record.scores['score_composed'] /= float(total_routes)\n\n return global_record\n\n @staticmethod\n def save_record(route_record, index, endpoint):\n data = fetch_dict(endpoint)\n if not data:\n data = create_default_json_msg()\n\n stats_dict = route_record.__dict__\n record_list = data['_checkpoint']['records']\n if index > len(record_list):\n print('Error! No enough entries in the list')\n sys.exit(-1)\n elif index == len(record_list):\n record_list.append(stats_dict)\n else:\n record_list[index] = stats_dict\n\n save_dict(endpoint, data)\n\n @staticmethod\n def save_global_record(route_record, sensors, total_routes, endpoint):\n data = fetch_dict(endpoint)\n if not data:\n data = create_default_json_msg()\n\n stats_dict = route_record.__dict__\n data['_checkpoint']['global_record'] = stats_dict\n data['values'] = ['{:.3f}'.format(stats_dict['scores']['score_composed']),\n '{:.3f}'.format(stats_dict['scores']['score_route']),\n '{:.3f}'.format(stats_dict['scores']['score_penalty']),\n # infractions\n '{:.3f}'.format(stats_dict['infractions']['collisions_pedestrian']),\n '{:.3f}'.format(stats_dict['infractions']['collisions_vehicle']),\n '{:.3f}'.format(stats_dict['infractions']['collisions_layout']),\n '{:.3f}'.format(stats_dict['infractions']['red_light']),\n '{:.3f}'.format(stats_dict['infractions']['stop_infraction']),\n '{:.3f}'.format(stats_dict['infractions']['outside_route_lanes']),\n '{:.3f}'.format(stats_dict['infractions']['route_dev']),\n '{:.3f}'.format(stats_dict['infractions']['route_timeout']),\n '{:.3f}'.format(stats_dict['infractions']['vehicle_blocked'])\n ]\n\n data['labels'] = ['Avg. driving score',\n 'Avg. route completion',\n 'Avg. infraction penalty',\n 'Collisions with pedestrians',\n 'Collisions with vehicles',\n 'Collisions with layout',\n 'Red lights infractions',\n 'Stop sign infractions',\n 'Off-road infractions',\n 'Route deviations',\n 'Route timeouts',\n 'Agent blocked'\n ]\n\n entry_status = \"Finished\"\n eligible = True\n\n route_records = data[\"_checkpoint\"][\"records\"]\n progress = data[\"_checkpoint\"][\"progress\"]\n\n if progress[1] != total_routes:\n raise Exception('Critical error with the route registry.')\n\n if len(route_records) != total_routes or progress[0] != progress[1]:\n entry_status = \"Finished with missing data\"\n eligible = False\n else:\n for route in route_records:\n route_status = route[\"status\"]\n if \"Agent\" in route_status:\n entry_status = \"Finished with agent errors\"\n break\n\n data['entry_status'] = entry_status\n data['eligible'] = eligible\n\n save_dict(endpoint, data)\n\n @staticmethod\n def save_sensors(sensors, endpoint):\n data = fetch_dict(endpoint)\n if not data:\n data = create_default_json_msg()\n\n if not data['sensors']:\n data['sensors'] = sensors\n\n save_dict(endpoint, data)\n\n @staticmethod\n def save_entry_status(entry_status, eligible, endpoint):\n data = fetch_dict(endpoint)\n if not data:\n data = create_default_json_msg()\n\n data['entry_status'] = entry_status\n data['eligible'] = eligible\n save_dict(endpoint, data)\n\n @staticmethod\n def clear_record(endpoint):\n if not endpoint.startswith(('http:', 'https:', 'ftp:')):\n with open(endpoint, 'w') as fd:\n fd.truncate(0)\n" }, { "path": "leaderboard/scripts/collect_data.sh", "content": "#!/bin/bash\nexport CARLA_SERVER=${CARLA_ROOT}/CarlaUE4.sh\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg\nexport PYTHONPATH=$PYTHONPATH:leaderboard\nexport PYTHONPATH=$PYTHONPATH:leaderboard/team_code\nexport PYTHONPATH=$PYTHONPATH:scenario_runner\nexport PYTHONPATH=/mnt/petrelfs/share/pymc/new:$PYTHONPATH\n\n\nexport LEADERBOARD_ROOT=leaderboard\nexport CHALLENGE_TRACK_CODENAME=SENSORS\nexport PORT=$1\nexport TM_PORT=$2\nexport DEBUG_CHALLENGE=0\nexport REPETITIONS=1\nexport RESUME=$4\nexport BENCHMARK=COLLECT_DATA\n\n\n# Roach data collection\nexport ROUTES=leaderboard/data/routes_for_open_loop_training/routes_$3.xml\nexport TEAM_AGENT=team_code/$6.py\nexport TEAM_CONFIG=roach/config/config_agent.yaml\nexport CHECKPOINT_ENDPOINT=data_collect_$3_results.json\nexport SCENARIOS=leaderboard/data/scenarios/$7.json\nexport SAVE_PATH=dataset/$3\nexport ROUTE_FILE=$3\n\n\npython3 ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py \\\n--scenarios=${SCENARIOS} \\\n--routes=${ROUTES} \\\n--repetitions=${REPETITIONS} \\\n--track=${CHALLENGE_TRACK_CODENAME} \\\n--checkpoint=collect_data_json/${CHECKPOINT_ENDPOINT} \\\n--agent=${TEAM_AGENT} \\\n--agent-config=${TEAM_CONFIG} \\\n--debug=${DEBUG_CHALLENGE} \\\n--record=${RECORD_PATH} \\\n--resume=${RESUME} \\\n--port=${PORT} \\\n--trafficManagerPort=${TM_PORT} \\\n--is_local=$5 \\\n--is_eval=False\n" }, { "path": "leaderboard/scripts/evaluation_longest6.sh", "content": "#!/bin/bash\nexport CARLA_SERVER=${CARLA_ROOT}/CarlaUE4.sh\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg\nexport PYTHONPATH=$PYTHONPATH:leaderboard\nexport PYTHONPATH=$PYTHONPATH:leaderboard/team_code\nexport PYTHONPATH=$PYTHONPATH:scenario_runner\nexport PYTHONPATH=$PYTHONPATH:open_loop_training\nexport PYTHONPATH=/mnt/lustre/share/pymc/new:$PYTHONPATH\n\nexport LEADERBOARD_ROOT=leaderboard\nexport CHALLENGE_TRACK_CODENAME=SENSORS\nexport PORT=$1\nexport TM_PORT=$2\nexport DEBUG_CHALLENGE=0\nexport REPETITIONS=1 # multiple evaluation runs\nexport RESUME=$4\n\n#evaluation\nexport ROUTES=leaderboard/data/routes_for_evaluation/routes_longest6.xml\nexport TEAM_AGENT=team_code/$3.py\nexport TEAM_CONFIG=$6\nexport CHECKPOINT_ENDPOINT=closed_loop_eval_log/results_$8.json\nexport SCENARIOS=leaderboard/data/scenarios/$7.json\nexport SAVE_PATH=closed_loop_eval_log/eval_log/$8\nexport BENCHMARK=longest6\n\n\n\npython3 -q -X faulthandler ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py \\\n--scenarios=${SCENARIOS} \\\n--routes=${ROUTES} \\\n--repetitions=${REPETITIONS} \\\n--track=${CHALLENGE_TRACK_CODENAME} \\\n--checkpoint=${CHECKPOINT_ENDPOINT} \\\n--agent=${TEAM_AGENT} \\\n--agent-config=${TEAM_CONFIG} \\\n--debug=${DEBUG_CHALLENGE} \\\n--record=${RECORD_PATH} \\\n--resume=${RESUME} \\\n--port=${PORT} \\\n--trafficManagerPort=${TM_PORT} \\\n--is_local=$5 \\\n--is_eval=True\n" }, { "path": "leaderboard/scripts/evaluation_town05long.sh", "content": "#!/bin/bash\nexport CARLA_SERVER=${CARLA_ROOT}/CarlaUE4.sh\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-0.9.10-py3.7-linux-x86_64.egg\nexport PYTHONPATH=$PYTHONPATH:leaderboard\nexport PYTHONPATH=$PYTHONPATH:leaderboard/team_code\nexport PYTHONPATH=$PYTHONPATH:scenario_runner\nexport PYTHONPATH=$PYTHONPATH:open_loop_training\nexport PYTHONPATH=/mnt/lustre/share/pymc/new:$PYTHONPATH\n\nexport LEADERBOARD_ROOT=leaderboard\nexport CHALLENGE_TRACK_CODENAME=SENSORS\nexport PORT=$1\nexport TM_PORT=$2\nexport DEBUG_CHALLENGE=0\nexport REPETITIONS=1 # multiple evaluation runs\nexport RESUME=$4\n\n# evaluation\nexport ROUTES=leaderboard/data/routes_for_evaluation/routes_town05_long.xml\nexport TEAM_AGENT=team_code/$3.py\nexport TEAM_CONFIG=$6\nexport CHECKPOINT_ENDPOINT=closed_loop_eval_log/results_$8.json\nexport SCENARIOS=leaderboard/data/scenarios/$7.json\nexport SAVE_PATH=closed_loop_eval_log/eval_log/$8\nexport BENCHMARK=town05long\n\n\npython3 -q -X faulthandler ${LEADERBOARD_ROOT}/leaderboard/leaderboard_evaluator.py \\\n--scenarios=${SCENARIOS} \\\n--routes=${ROUTES} \\\n--repetitions=${REPETITIONS} \\\n--track=${CHALLENGE_TRACK_CODENAME} \\\n--checkpoint=${CHECKPOINT_ENDPOINT} \\\n--agent=${TEAM_AGENT} \\\n--agent-config=${TEAM_CONFIG} \\\n--debug=${DEBUG_CHALLENGE} \\\n--record=${RECORD_PATH} \\\n--resume=${RESUME} \\\n--port=${PORT} \\\n--trafficManagerPort=${TM_PORT} \\\n--is_local=$5 \\\n--is_eval=True\n" }, { "path": "leaderboard/team_code/auto_pilot.py", "content": "import os\nimport time\nimport datetime\nimport pathlib\nimport json\nimport random\n\nimport numpy as np\nimport cv2\nimport carla\nfrom PIL import Image\n\nfrom team_code.map_agent import MapAgent\nfrom team_code.pid_controller import PIDController\n\n\n# WEATHERS = {\n# 'ClearNoon': carla.WeatherParameters.ClearNoon,\n# 'ClearSunset': carla.WeatherParameters.ClearSunset,\n\n# 'CloudyNoon': carla.WeatherParameters.CloudyNoon,\n# 'CloudySunset': carla.WeatherParameters.CloudySunset,\n\n# 'WetNoon': carla.WeatherParameters.WetNoon,\n# 'WetSunset': carla.WeatherParameters.WetSunset,\n\n# 'MidRainyNoon': carla.WeatherParameters.MidRainyNoon,\n# 'MidRainSunset': carla.WeatherParameters.MidRainSunset,\n\n# 'WetCloudyNoon': carla.WeatherParameters.WetCloudyNoon,\n# 'WetCloudySunset': carla.WeatherParameters.WetCloudySunset,\n\n# 'HardRainNoon': carla.WeatherParameters.HardRainNoon,\n# 'HardRainSunset': carla.WeatherParameters.HardRainSunset,\n\n# 'SoftRainNoon': carla.WeatherParameters.SoftRainNoon,\n# 'SoftRainSunset': carla.WeatherParameters.SoftRainSunset,\n# }\n# WEATHERS_IDS = list(WEATHERS)\n\nWEATHERS = {\n\t\t'ClearNoon': carla.WeatherParameters.ClearNoon,\n\t\t'ClearSunset': carla.WeatherParameters.ClearSunset,\n\t\t'WetNoon': carla.WeatherParameters.WetNoon,\n\t\t# 'WetSunset': carla.WeatherParameters.WetSunset,\n\t\t'HardRainNoon': carla.WeatherParameters.HardRainNoon,\n\t\t# 'SoftRainSunset': carla.WeatherParameters.SoftRainSunset,\n}\nWEATHERS_IDS = list(WEATHERS)\n\n\ndef get_entry_point():\n return 'AutoPilot'\n\n\ndef _numpy(carla_vector, normalize=False):\n result = np.float32([carla_vector.x, carla_vector.y])\n\n if normalize:\n return result / (np.linalg.norm(result) + 1e-4)\n\n return result\n\n\ndef _location(x, y, z):\n return carla.Location(x=float(x), y=float(y), z=float(z))\n\n\ndef _orientation(yaw):\n return np.float32([np.cos(np.radians(yaw)), np.sin(np.radians(yaw))])\n\n\ndef get_collision(p1, v1, p2, v2):\n A = np.stack([v1, -v2], 1)\n b = p2 - p1\n\n if abs(np.linalg.det(A)) < 1e-3:\n return False, None\n\n x = np.linalg.solve(A, b)\n collides = all(x >= 0) and all(x <= 1) # how many seconds until collision\n\n return collides, p1 + x[0] * v1\n\n\ndef check_episode_has_noise(lat_noise_percent, long_noise_percent):\n lat_noise = False\n long_noise = False\n if random.randint(0, 101) < lat_noise_percent:\n lat_noise = True\n\n if random.randint(0, 101) < long_noise_percent:\n long_noise = True\n\n return lat_noise, long_noise\n\n\nclass AutoPilot(MapAgent):\n\n # for stop signs\n PROXIMITY_THRESHOLD = 30.0 # meters\n SPEED_THRESHOLD = 0.1\n WAYPOINT_STEP = 1.0 # meters\n\n def setup(self, path_to_conf_file):\n super().setup(path_to_conf_file)\n \n def _init(self):\n super()._init()\n\n self._turn_controller = PIDController(K_P=1.25, K_I=0.75, K_D=0.3, n=40)\n self._speed_controller = PIDController(K_P=5.0, K_I=0.5, K_D=1.0, n=40)\n\n # for stop signs\n self._target_stop_sign = None # the stop sign affecting the ego vehicle\n self._stop_completed = False # if the ego vehicle has completed the stop sign\n self._affected_by_stop = False # if the ego vehicle is influenced by a stop sign\n\n def _get_angle_to(self, pos, theta, target):\n R = np.array([\n [np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)],\n ])\n\n aim = R.T.dot(target - pos)\n angle = -np.degrees(np.arctan2(-aim[1], aim[0]))\n angle = 0.0 if np.isnan(angle) else angle \n\n return angle\n\n def _get_control(self, target, far_target, tick_data):\n pos = self._get_position(tick_data)\n theta = tick_data['compass']\n speed = tick_data['speed']\n\n # Steering.\n angle_unnorm = self._get_angle_to(pos, theta, target)\n angle = angle_unnorm / 90\n\n steer = self._turn_controller.step(angle)\n steer = np.clip(steer, -1.0, 1.0)\n steer = round(steer, 3)\n\n # Acceleration.\n angle_far_unnorm = self._get_angle_to(pos, theta, far_target)\n should_slow = abs(angle_far_unnorm) > 45.0 or abs(angle_unnorm) > 5.0\n target_speed = 4.0 if should_slow else 7.0\n brake = self._should_brake()\n target_speed = target_speed if not brake else 0.0\n \n self.should_slow = int(should_slow)\n self.should_brake = int(brake)\n self.angle = angle\n self.angle_unnorm = angle_unnorm\n self.angle_far_unnorm = angle_far_unnorm\n \n delta = np.clip(target_speed - speed, 0.0, 0.25)\n throttle = self._speed_controller.step(delta)\n throttle = np.clip(throttle, 0.0, 0.75)\n\n if brake:\n steer *= 0.5\n throttle = 0.0\n\n return steer, throttle, brake, target_speed\n\n def run_step(self, input_data, timestamp):\n if not self.initialized:\n self._init()\n\n # change weather for visual diversity\n if self.step % 10 == 0:\n index = random.choice(range(len(WEATHERS)))\n self.weather_id = WEATHERS_IDS[index]\n weather = WEATHERS[WEATHERS_IDS[index]]\n print (self.weather_id, weather)\n self._world.set_weather(weather)\n\n data = self.tick(input_data)\n gps = self._get_position(data)\n\n near_node, near_command = self._waypoint_planner.run_step(gps)\n far_node, far_command = self._command_planner.run_step(gps)\n\n steer, throttle, brake, target_speed = self._get_control(near_node, far_node, data)\n\n control = carla.VehicleControl()\n control.steer = steer + 1e-2 * np.random.randn()\n control.throttle = throttle\n control.brake = float(brake)\n\n if self.step % 10 == 0 and self.save_path is not None:\n self.save(near_node, far_node, near_command, steer, throttle, brake, target_speed, data)\n\n return control\n\n def _should_brake(self):\n actors = self._world.get_actors()\n\n vehicle = self._is_vehicle_hazard(actors.filter('*vehicle*'))\n light = self._is_light_red(actors.filter('*traffic_light*'))\n walker = self._is_walker_hazard(actors.filter('*walker*'))\n stop_sign = self._is_stop_sign_hazard(actors.filter('*stop*'))\n\n self.is_vehicle_present = 1 if vehicle is not None else 0\n self.is_red_light_present = 1 if light is not None else 0\n self.is_pedestrian_present = 1 if walker is not None else 0\n self.is_stop_sign_present = 1 if stop_sign is not None else 0\n\n return any(x is not None for x in [vehicle, light, walker, stop_sign])\n\n def _point_inside_boundingbox(self, point, bb_center, bb_extent):\n A = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y - bb_extent.y)\n B = carla.Vector2D(bb_center.x + bb_extent.x, bb_center.y - bb_extent.y)\n D = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y + bb_extent.y)\n M = carla.Vector2D(point.x, point.y)\n\n AB = B - A\n AD = D - A\n AM = M - A\n am_ab = AM.x * AB.x + AM.y * AB.y\n ab_ab = AB.x * AB.x + AB.y * AB.y\n am_ad = AM.x * AD.x + AM.y * AD.y\n ad_ad = AD.x * AD.x + AD.y * AD.y\n\n return am_ab > 0 and am_ab < ab_ab and am_ad > 0 and am_ad < ad_ad\n\n def _get_forward_speed(self, transform=None, velocity=None):\n \"\"\" Convert the vehicle transform directly to forward speed \"\"\"\n if not velocity:\n velocity = self._vehicle.get_velocity()\n if not transform:\n transform = self._vehicle.get_transform()\n\n vel_np = np.array([velocity.x, velocity.y, velocity.z])\n pitch = np.deg2rad(transform.rotation.pitch)\n yaw = np.deg2rad(transform.rotation.yaw)\n orientation = np.array([np.cos(pitch) * np.cos(yaw), np.cos(pitch) * np.sin(yaw), np.sin(pitch)])\n speed = np.dot(vel_np, orientation)\n return speed\n\n def _is_actor_affected_by_stop(self, actor, stop, multi_step=20):\n \"\"\"\n Check if the given actor is affected by the stop\n \"\"\"\n affected = False\n # first we run a fast coarse test\n current_location = actor.get_location()\n stop_location = stop.get_transform().location\n if stop_location.distance(current_location) > self.PROXIMITY_THRESHOLD:\n return affected\n\n stop_t = stop.get_transform()\n transformed_tv = stop_t.transform(stop.trigger_volume.location)\n\n # slower and accurate test based on waypoint's horizon and geometric test\n list_locations = [current_location]\n waypoint = self._world.get_map().get_waypoint(current_location)\n for _ in range(multi_step):\n if waypoint:\n waypoint = waypoint.next(self.WAYPOINT_STEP)[0]\n if not waypoint:\n break\n list_locations.append(waypoint.transform.location)\n\n for actor_location in list_locations:\n if self._point_inside_boundingbox(actor_location, transformed_tv, stop.trigger_volume.extent):\n affected = True\n\n return affected\n\n def _is_stop_sign_hazard(self, stop_sign_list):\n if self._affected_by_stop:\n if not self._stop_completed:\n current_speed = self._get_forward_speed()\n if current_speed < self.SPEED_THRESHOLD:\n self._stop_completed = True\n return None\n else:\n return self._target_stop_sign\n else:\n # reset if the ego vehicle is outside the influence of the current stop sign\n if not self._is_actor_affected_by_stop(self._vehicle, self._target_stop_sign):\n self._affected_by_stop = False\n self._stop_completed = False\n self._target_stop_sign = None\n return None\n\n ve_tra = self._vehicle.get_transform()\n ve_dir = ve_tra.get_forward_vector()\n\n wp = self._world.get_map().get_waypoint(ve_tra.location)\n wp_dir = wp.transform.get_forward_vector()\n\n dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z\n\n if dot_ve_wp > 0: # Ignore all when going in a wrong lane\n for stop_sign in stop_sign_list:\n if self._is_actor_affected_by_stop(self._vehicle, stop_sign):\n # this stop sign is affecting the vehicle\n self._affected_by_stop = True\n self._target_stop_sign = stop_sign\n return self._target_stop_sign\n\n return None\n\n def _is_light_red(self, lights_list):\n if self._vehicle.get_traffic_light_state() != carla.libcarla.TrafficLightState.Green:\n affecting = self._vehicle.get_traffic_light()\n\n for light in self._traffic_lights:\n if light.id == affecting.id:\n return affecting\n\n return None\n\n def _is_walker_hazard(self, walkers_list):\n z = self._vehicle.get_location().z\n p1 = _numpy(self._vehicle.get_location())\n v1 = 10.0 * _orientation(self._vehicle.get_transform().rotation.yaw)\n\n for walker in walkers_list:\n v2_hat = _orientation(walker.get_transform().rotation.yaw)\n s2 = np.linalg.norm(_numpy(walker.get_velocity()))\n\n if s2 < 0.05:\n v2_hat *= s2\n\n p2 = -3.0 * v2_hat + _numpy(walker.get_location())\n v2 = 8.0 * v2_hat\n\n collides, collision_point = get_collision(p1, v1, p2, v2)\n\n if collides:\n return walker\n\n return None\n\n def _is_vehicle_hazard(self, vehicle_list):\n z = self._vehicle.get_location().z\n\n o1 = _orientation(self._vehicle.get_transform().rotation.yaw)\n p1 = _numpy(self._vehicle.get_location())\n s1 = max(10, 3.0 * np.linalg.norm(_numpy(self._vehicle.get_velocity()))) # increases the threshold distance\n v1_hat = o1\n v1 = s1 * v1_hat\n\n for target_vehicle in vehicle_list:\n if target_vehicle.id == self._vehicle.id:\n continue\n\n o2 = _orientation(target_vehicle.get_transform().rotation.yaw)\n p2 = _numpy(target_vehicle.get_location())\n s2 = max(5.0, 2.0 * np.linalg.norm(_numpy(target_vehicle.get_velocity())))\n v2_hat = o2\n v2 = s2 * v2_hat\n\n p2_p1 = p2 - p1\n distance = np.linalg.norm(p2_p1)\n p2_p1_hat = p2_p1 / (distance + 1e-4)\n\n angle_to_car = np.degrees(np.arccos(v1_hat.dot(p2_p1_hat)))\n angle_between_heading = np.degrees(np.arccos(o1.dot(o2)))\n\n # to consider -ve angles too\n angle_to_car = min(angle_to_car, 360.0 - angle_to_car)\n angle_between_heading = min(angle_between_heading, 360.0 - angle_between_heading)\n\n if angle_between_heading > 60.0 and not (angle_to_car < 15 and distance < s1):\n continue\n elif angle_to_car > 30.0:\n continue\n elif distance > s1:\n continue\n\n return target_vehicle\n\n return None\n \n" }, { "path": "leaderboard/team_code/base_agent.py", "content": "import time\nimport os\nimport datetime\nimport pathlib\nimport json\n\nimport cv2\nimport carla\n\nfrom leaderboard.autoagents import autonomous_agent\nfrom team_code.planner import RoutePlanner\n\nimport numpy as np\nfrom PIL import Image, ImageDraw\n\n\nSAVE_PATH = os.environ.get('SAVE_PATH', None)\n\n\nclass BaseAgent(autonomous_agent.AutonomousAgent):\n def setup(self, path_to_conf_file):\n self.track = autonomous_agent.Track.SENSORS\n self.config_path = path_to_conf_file\n self.step = -1\n self.wall_start = time.time()\n self.initialized = False\n\n self._sensor_data = {\n 'width': 400,\n 'height': 300,\n 'fov': 100\n }\n\n self._3d_bb_distance = 50\n self.weather_id = None\n\n self.save_path = None\n\n if SAVE_PATH is not None:\n now = datetime.datetime.now()\n string = pathlib.Path(os.environ['ROUTES']).stem + '_'\n string += '_'.join(map(lambda x: '%02d' % x, (now.month, now.day, now.hour, now.minute, now.second)))\n\n print (string)\n\n self.save_path = pathlib.Path(os.environ['SAVE_PATH']) / string\n self.save_path.mkdir(parents=True, exist_ok=False)\n \n for sensor in self.sensors():\n if hasattr(sensor, 'save') and sensor['save']:\n (self.save_path / sensor['id']).mkdir()\n\n (self.save_path / '3d_bbs').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'affordances').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'measurements').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'lidar').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'semantic_lidar').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'topdown').mkdir(parents=True, exist_ok=True)\n\n for pos in ['front', 'left', 'right', 'rear']:\n for sensor_type in ['rgb', 'seg', 'depth', '2d_bbs']:\n name = sensor_type + '_' + pos\n (self.save_path / name).mkdir()\n\n def _init(self):\n self._command_planner = RoutePlanner(7.5, 25.0, 257)\n self._command_planner.set_route(self._global_plan, True)\n\n self.initialized = True\n\n self._sensor_data['calibration'] = self._get_camera_to_car_calibration(self._sensor_data)\n \n self._sensors = self.sensor_interface._sensors_objects\n\n\n\n def _get_position(self, tick_data):\n gps = tick_data['gps']\n gps = (gps - self._command_planner.mean) * self._command_planner.scale\n\n return gps\n\n def sensors(self):\n return [\n {\n 'type': 'sensor.camera.rgb',\n 'x': -1.5, 'y': 0.0, 'z': 2.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 900, 'height': 256, 'fov': 100,\n 'id': 'rgb_front'\n },\n {\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': 1.3, 'y': 0.0, 'z': 2.3,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n 'id': 'seg_front'\n },\n {\n 'type': 'sensor.camera.depth',\n 'x': 1.3, 'y': 0.0, 'z': 2.3,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n 'id': 'depth_front'\n },\n # {\n # 'type': 'sensor.camera.rgb',\n # 'x': -1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'rgb_rear'\n # },\n # {\n # 'type': 'sensor.camera.semantic_segmentation',\n # 'x': -1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'seg_rear'\n # },\n # {\n # 'type': 'sensor.camera.depth',\n # 'x': -1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'depth_rear'\n # },\n # {\n # 'type': 'sensor.camera.rgb',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': -60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'rgb_left'\n # },\n # {\n # 'type': 'sensor.camera.semantic_segmentation',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': -60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'seg_left'\n # },\n # {\n # 'type': 'sensor.camera.depth',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': -60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'depth_left'\n # },\n # {\n # 'type': 'sensor.camera.rgb',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'rgb_right'\n # },\n # {\n # 'type': 'sensor.camera.semantic_segmentation',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'seg_right'\n # },\n # {\n # 'type': 'sensor.camera.depth',\n # 'x': 1.3, 'y': 0.0, 'z': 2.3,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': 60.0,\n # 'width': self._sensor_data['width'], 'height': self._sensor_data['height'], 'fov': self._sensor_data['fov'],\n # 'id': 'depth_right'\n # },\n # { \n # 'type': 'sensor.lidar.ray_cast',\n # 'x': 1.3, 'y': 0.0, 'z': 2.5,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n # 'id': 'lidar'\n # },\n # { \n # 'type': 'sensor.lidar.ray_cast_semantic',\n # 'x': 1.3, 'y': 0.0, 'z': 2.5,\n # 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n # 'id': 'semantic_lidar'\n # },\n {\n 'type': 'sensor.other.imu',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.05,\n 'id': 'imu'\n },\n {\n 'type': 'sensor.other.gnss',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.01,\n 'id': 'gps'\n },\n {\n 'type': 'sensor.speedometer',\n 'reading_frequency': 20,\n 'id': 'speed'\n }\n ]\n\n def tick(self, input_data):\n self.step += 1\n\n # affordances = self._get_affordances()\n\n # traffic_lights = self._find_obstacle('*traffic_light*')\n # stop_signs = self._find_obstacle('*stop*')\n\n depth = {}\n seg = {}\n\n # bb_3d = self._get_3d_bbs(max_distance=self._3d_bb_distance)\n\n bb_2d = {}\n\n # for pos in ['front', 'left', 'right', 'rear']:\n # seg_cam = 'seg_' + pos\n # depth_cam = 'depth_' + pos\n # _segmentation = np.copy(input_data[seg_cam][1][:, :, 2])\n\n # depth[pos] = self._get_depth(input_data[depth_cam][1][:, :, :3])\n \n # self._change_seg_tl(_segmentation, depth[pos], traffic_lights)\n # self._change_seg_stop(_segmentation, depth[pos], stop_signs, seg_cam) \n\n # bb_2d[pos] = self._get_2d_bbs(seg_cam, affordances, bb_3d, _segmentation)\n\n # #self._draw_2d_bbs(_segmentation, bb_2d[pos])\n \n # seg[pos] = _segmentation\n\n \n rgb_front = cv2.cvtColor(input_data['rgb_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # rgb_rear = cv2.cvtColor(input_data['rgb_rear'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # rgb_left = cv2.cvtColor(input_data['rgb_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # rgb_right = cv2.cvtColor(input_data['rgb_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n gps = input_data['gps'][1][:2]\n speed = input_data['speed'][1]['speed']\n compass = input_data['imu'][1][-1]\n\n # depth_front = cv2.cvtColor(input_data['depth_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # depth_left = cv2.cvtColor(input_data['depth_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # depth_right = cv2.cvtColor(input_data['depth_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n # depth_rear = cv2.cvtColor(input_data['depth_rear'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n\n weather = self._weather_to_dict(self._world.get_weather())\n\n # return {\n # 'rgb_front': rgb_front,\n # 'seg_front': seg['front'],\n # 'depth_front': depth_front,\n # '2d_bbs_front': bb_2d['front'],\n # 'rgb_rear': rgb_rear,\n # 'seg_rear': seg['rear'],\n # 'depth_rear': depth_rear,\n # '2d_bbs_rear': bb_2d['rear'],\n # 'rgb_left': rgb_left,\n # 'seg_left': seg['left'],\n # 'depth_left': depth_left,\n # '2d_bbs_left': bb_2d['left'],\n # 'rgb_right': rgb_right,\n # 'seg_right': seg['right'],\n # 'depth_right': depth_right,\n # '2d_bbs_right': bb_2d['right'],\n # 'lidar' : input_data['lidar'][1],\n # 'semantic_lidar': input_data['semantic_lidar'][1],\n # 'gps': gps,\n # 'speed': speed,\n # 'compass': compass,\n # 'weather': weather,\n # 'affordances': affordances,\n # '3d_bbs': bb_3d\n # }\n\n return {\n 'rgb_front': rgb_front,\n \n 'gps': gps,\n 'speed': speed,\n 'compass': compass,\n 'weather': weather,\n }\n\n\n def save(self, near_node, far_node, near_command, steer, throttle, brake, target_speed, tick_data):\n frame = self.step // 10\n\n pos = self._get_position(tick_data)\n theta = tick_data['compass']\n speed = tick_data['speed']\n weather = tick_data['weather']\n\n data = {\n 'x': pos[0],\n 'y': pos[1],\n 'theta': theta,\n 'speed': speed,\n 'target_speed': target_speed,\n 'x_command': far_node[0],\n 'y_command': far_node[1],\n 'command': near_command.value,\n 'steer': steer,\n 'throttle': throttle,\n 'brake': brake,\n 'weather': weather,\n 'weather_id': self.weather_id,\n 'near_node_x': near_node[0],\n 'near_node_y': near_node[1],\n 'far_node_x': far_node[0],\n 'far_node_y': far_node[1],\n 'is_vehicle_present': self.is_vehicle_present,\n 'is_pedestrian_present': self.is_pedestrian_present,\n 'is_red_light_present': self.is_red_light_present,\n 'is_stop_sign_present': self.is_stop_sign_present,\n 'should_slow': self.should_slow,\n 'should_brake': self.should_brake,\n 'angle': self.angle,\n 'angle_unnorm': self.angle_unnorm,\n 'angle_far_unnorm': self.angle_far_unnorm,\n }\n\n measurements_file = self.save_path / 'measurements' / ('%04d.json' % frame)\n f = open(measurements_file, 'w')\n json.dump(data, f, indent=4)\n f.close()\n\n # for pos in ['front', 'left', 'right', 'rear']:\n for pos in ['front']:\n name = 'rgb_' + pos\n Image.fromarray(tick_data[name]).save(self.save_path / name / ('%04d.png' % frame))\n # for sensor_type in ['seg', 'depth']:\n # name = sensor_type + '_' + pos\n # Image.fromarray(tick_data[name]).save(self.save_path / name / ('%04d.png' % frame))\n # for sensor_type in ['2d_bbs']:\n # name = sensor_type + '_' + pos\n # np.save(self.save_path / name / ('%04d.npy' % frame), tick_data[name], allow_pickle=True)\n\n # Image.fromarray(tick_data['topdown']).save(self.save_path / 'topdown' / ('%04d.png' % frame))\n # np.save(self.save_path / 'lidar' / ('%04d.npy' % frame), tick_data['lidar'], allow_pickle=True)\n # np.save(self.save_path / 'semantic_lidar' / ('%04d.npy' % frame), tick_data['semantic_lidar'], allow_pickle=True)\n # np.save(self.save_path / '3d_bbs' / ('%04d.npy' % frame), tick_data['3d_bbs'], allow_pickle=True)\n # np.save(self.save_path / 'affordances' / ('%04d.npy' % frame), tick_data['affordances'], allow_pickle=True)\n \n\n def _weather_to_dict(self, carla_weather):\n weather = {\n 'cloudiness': carla_weather.cloudiness,\n 'precipitation': carla_weather.precipitation,\n 'precipitation_deposits': carla_weather.precipitation_deposits,\n 'wind_intensity': carla_weather.wind_intensity,\n 'sun_azimuth_angle': carla_weather.sun_azimuth_angle,\n 'sun_altitude_angle': carla_weather.sun_altitude_angle,\n 'fog_density': carla_weather.fog_density,\n 'fog_distance': carla_weather.fog_distance,\n 'wetness': carla_weather.wetness,\n 'fog_falloff': carla_weather.fog_falloff,\n }\n\n return weather\n\n def _create_bb_points(self, bb):\n \"\"\"\n Returns 3D bounding box world coordinates.\n \"\"\"\n\n cords = np.zeros((8, 4))\n extent = bb[1]\n loc = bb[0]\n cords[0, :] = np.array([loc[0] + extent[0], loc[1] + extent[1], loc[2] - extent[2], 1])\n cords[1, :] = np.array([loc[0] - extent[0], loc[1] + extent[1], loc[2] - extent[2], 1])\n cords[2, :] = np.array([loc[0] - extent[0], loc[1] - extent[1], loc[2] - extent[2], 1])\n cords[3, :] = np.array([loc[0] + extent[0], loc[1] - extent[1], loc[2] - extent[2], 1])\n cords[4, :] = np.array([loc[0] + extent[0], loc[1] + extent[1], loc[2] + extent[2], 1])\n cords[5, :] = np.array([loc[0] - extent[0], loc[1] + extent[1], loc[2] + extent[2], 1])\n cords[6, :] = np.array([loc[0] - extent[0], loc[1] - extent[1], loc[2] + extent[2], 1])\n cords[7, :] = np.array([loc[0] + extent[0], loc[1] - extent[1], loc[2] + extent[2], 1])\n return cords\n\n def _translate_tl_state(self, state):\n\n if state == carla.TrafficLightState.Red:\n return 0\n elif state == carla.TrafficLightState.Yellow:\n return 1\n elif state == carla.TrafficLightState.Green:\n return 2\n elif state == carla.TrafficLightState.Off:\n return 3\n elif state == carla.TrafficLightState.Unknown:\n return 4\n else:\n return None\n\n\n def _get_affordances(self):\n \n # affordance tl\n affordances = {}\n affordances[\"traffic_light\"] = None\n \n affecting = self._vehicle.get_traffic_light()\n if affecting is not None:\n for light in self._traffic_lights:\n if light.id == affecting.id:\n affordances[\"traffic_light\"] = self._translate_tl_state(self._vehicle.get_traffic_light_state())\n\n affordances[\"stop_sign\"] = self._affected_by_stop\n\n return affordances\n \n def _get_3d_bbs(self, max_distance=50):\n\n bounding_boxes = {\n \"traffic_lights\": [],\n \"stop_signs\": [],\n \"vehicles\": [],\n \"pedestrians\": []\n }\n\n bounding_boxes['traffic_lights'] = self._find_obstacle_3dbb('*traffic_light*', max_distance)\n bounding_boxes['stop_signs'] = self._find_obstacle_3dbb('*stop*', max_distance)\n bounding_boxes['vehicles'] = self._find_obstacle_3dbb('*vehicle*', max_distance)\n bounding_boxes['pedestrians'] = self._find_obstacle_3dbb('*walker*', max_distance)\n\n return bounding_boxes\n\n def _get_2d_bbs(self, seg_cam, affordances, bb_3d, seg_img):\n \"\"\"Returns a dict of all 2d boundingboxes given a camera position, affordances and 3d bbs\n\n Args:\n seg_cam ([type]): [description]\n affordances ([type]): [description]\n bb_3d ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n\n bounding_boxes = {\n \"traffic_light\": list(),\n \"stop_sign\": list(),\n \"vehicles\": list(),\n \"pedestrians\": list()\n }\n\n \n if affordances['stop_sign']:\n baseline = self._get_2d_bb_baseline(self._target_stop_sign)\n bb = self._baseline_to_box(baseline, seg_cam)\n\n if bb is not None:\n bounding_boxes[\"stop_sign\"].append(bb)\n\n if affordances['traffic_light'] is not None:\n baseline = self._get_2d_bb_baseline(self._vehicle.get_traffic_light(), distance=8)\n\n tl_bb = self._baseline_to_box(baseline, seg_cam, height=.5)\n\n if tl_bb is not None:\n bounding_boxes[\"traffic_light\"].append({\n \"bb\": tl_bb, \n \"state\": self._translate_tl_state(self._vehicle.get_traffic_light_state())\n })\n\n for vehicle in bb_3d[\"vehicles\"]:\n\n trig_loc_world = self._create_bb_points(vehicle).T\n cords_x_y_z = self._world_to_sensor(trig_loc_world, self._get_sensor_position(seg_cam), False)\n \n cords_x_y_z = np.array(cords_x_y_z)[:3, :]\n veh_bb = self._coords_to_2d_bb(cords_x_y_z)\n\n if veh_bb is not None:\n if np.any(seg_img[veh_bb[0][1]:veh_bb[1][1],veh_bb[0][0]:veh_bb[1][0]] == 10):\n bounding_boxes[\"vehicles\"].append(veh_bb)\n\n for pedestrian in bb_3d[\"pedestrians\"]:\n\n trig_loc_world = self._create_bb_points(pedestrian).T\n cords_x_y_z = self._world_to_sensor(trig_loc_world, self._get_sensor_position(seg_cam), False)\n \n cords_x_y_z = np.array(cords_x_y_z)[:3, :]\n\n ped_bb = self._coords_to_2d_bb(cords_x_y_z)\n\n if ped_bb is not None:\n if np.any(seg_img[ped_bb[0][1]:ped_bb[1][1],ped_bb[0][0]:ped_bb[1][0]] == 4):\n bounding_boxes[\"pedestrians\"].append(ped_bb)\n\n return bounding_boxes\n\n def _draw_2d_bbs(self, seg_img, bbs):\n \"\"\"For debugging only\n\n Args:\n seg_img ([type]): [description]\n bbs ([type]): [description]\n \"\"\" \n\n for bb_type in bbs:\n \n _region = np.zeros(seg_img.shape)\n \n if bb_type == \"traffic_light\":\n for bb in bbs[bb_type]:\n _region = np.zeros(seg_img.shape)\n box = bb['bb']\n _region[box[0][1]:box[1][1],box[0][0]:box[1][0]] = 1\n seg_img[(_region == 1)] = 23\n else:\n \n for bb in bbs[bb_type]:\n\n _region[bb[0][1]:bb[1][1],bb[0][0]:bb[1][0]] = 1\n\n if bb_type == \"stop_sign\": \n seg_img[(_region == 1)] = 26\n elif bb_type == \"vehicles\":\n seg_img[(_region == 1)] = 10\n elif bb_type == \"pedestrians\":\n seg_img[(_region == 1)] = 4\n\n def _find_obstacle_3dbb(self, obstacle_type, max_distance=50):\n \"\"\"Returns a list of 3d bounding boxes of type obstacle_type.\n If the object does have a bounding box, this is returned. Otherwise a bb\n of size 0.5,0.5,2 is returned at the origin of the object.\n\n Args:\n obstacle_type (String): Regular expression\n max_distance (int, optional): max search distance. Returns all bbs in this radius. Defaults to 50.\n\n Returns:\n List: List of Boundingboxes\n \"\"\" \n obst = list()\n \n _actors = self._world.get_actors()\n _obstacles = _actors.filter(obstacle_type)\n\n for _obstacle in _obstacles: \n distance_to_car = _obstacle.get_transform().location.distance(self._vehicle.get_location())\n\n if 0 < distance_to_car <= max_distance:\n \n if hasattr(_obstacle, 'bounding_box'): \n loc = _obstacle.bounding_box.location\n _obstacle.get_transform().transform(loc)\n\n extent = _obstacle.bounding_box.extent\n _rotation_matrix = self.get_matrix(carla.Transform(carla.Location(0,0,0), _obstacle.get_transform().rotation))\n\n rotated_extent = np.squeeze(np.array((np.array([[extent.x, extent.y, extent.z, 1]]) @ _rotation_matrix)[:3]))\n\n bb = np.array([\n [loc.x, loc.y, loc.z],\n [rotated_extent[0], rotated_extent[1], rotated_extent[2]]\n ])\n\n else:\n loc = _obstacle.get_transform().location\n bb = np.array([\n [loc.x, loc.y, loc.z],\n [0.5, 0.5, 2]\n ])\n\n obst.append(bb)\n\n return obst\n\n def _get_2d_bb_baseline(self, obstacle, distance=2, cam='seg_front'):\n \"\"\"Returns 2 coordinates for the baseline for 2d bbs in world coordinates\n (distance behind trigger volume, as seen from camera)\n\n Args:\n obstacle (Actor): obstacle with \n distance (int, optional): Distance behind trigger volume. Defaults to 2.\n\n Returns:\n np.ndarray: Baseline\n \"\"\" \n trigger = obstacle.trigger_volume\n bb = self._create_2d_bb_points(trigger)\n trig_loc_world = self._trig_to_world(bb, obstacle, trigger)\n #self._draw_line(trig_loc_world[:,0], trig_loc_world[:,3], 0.7, color=(0, 255, 255))\n\n\n cords_x_y_z = np.array(self._world_to_sensor(trig_loc_world, self._get_sensor_position(cam)))\n indices = (-cords_x_y_z[0]).argsort()\n\n # check crooked up boxes\n if self._get_dist(cords_x_y_z[:,indices[0]],cords_x_y_z[:,indices[1]]) < self._get_dist(cords_x_y_z[:,indices[0]],cords_x_y_z[:,indices[2]]):\n cords = cords_x_y_z[:, [indices[0],indices[2]]] + np.array([[distance],[0],[0],[0]])\n else:\n cords = cords_x_y_z[:, [indices[0],indices[1]]] + np.array([[distance],[0],[0],[0]])\n\n sensor_world_matrix = self.get_matrix(self._get_sensor_position(cam))\n baseline = np.dot(sensor_world_matrix, cords)\n\n return baseline\n\n def _baseline_to_box(self, baseline, cam, height=1):\n \"\"\"Transforms a baseline (in world coords) into a 2d box (in sensor coords)\n\n Args:\n baseline ([type]): [description]\n cam ([type]): [description]\n height (int, optional): Box height. Defaults to 1.\n\n Returns:\n [type]: Box in sensor coords\n \"\"\" \n cords_x_y_z = np.array(self._world_to_sensor(baseline, self._get_sensor_position(cam))[:3, :])\n \n cords = np.hstack((cords_x_y_z, np.fliplr(cords_x_y_z + np.array([[0],[0],[height]])))) \n \n return self._coords_to_2d_bb(cords)\n\n \n def _coords_to_2d_bb(self, cords):\n \"\"\"Returns coords of a 2d box given points in sensor coords\n\n Args:\n cords ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n cords_y_minus_z_x = np.vstack((cords[1, :], -cords[2, :], cords[0, :]))\n \n bbox = (self._sensor_data['calibration'] @ cords_y_minus_z_x).T\n\n camera_bbox = np.vstack([bbox[:, 0] / bbox[:, 2], bbox[:, 1] / bbox[:, 2], bbox[:, 2]]).T\n\n if np.any(camera_bbox[:,2] > 0):\n\n camera_bbox = np.array(camera_bbox)\n _positive_bb = camera_bbox[camera_bbox[:,2] > 0]\n\n min_x = int(np.clip(np.min(_positive_bb[:,0]), 0, self._sensor_data['width']))\n min_y = int(np.clip(np.min(_positive_bb[:,1]), 0, self._sensor_data['height']))\n max_x = int(np.clip(np.max(_positive_bb[:,0]), 0, self._sensor_data['width']))\n max_y = int(np.clip(np.max(_positive_bb[:,1]), 0, self._sensor_data['height']))\n\n return [(min_x,min_y),(max_x,max_y)]\n else:\n return None\n\n\n def _change_seg_stop(self, seg_img, depth_img, stop_signs, cam, _region_size=6):\n \"\"\"Adds a stop class to the segmentation image\n\n Args:\n seg_img ([type]): [description]\n depth_img ([type]): [description]\n stop_signs ([type]): [description]\n cam ([type]): [description]\n _region_size (int, optional): [description]. Defaults to 6.\n \"\"\" \n for stop in stop_signs:\n\n _dist = self._get_distance(stop.get_transform().location)\n \n _region = np.abs(depth_img - _dist)\n\n seg_img[(_region < _region_size) & (seg_img == 12)] = 26\n\n # lane markings\n trigger = stop.trigger_volume\n\n _trig_loc_world = self._trig_to_world(np.array([[0], [0], [0], [1.0]]).T, stop, trigger)\n _x = self._world_to_sensor(_trig_loc_world, self._get_sensor_position(cam))[0,0]\n\n if _x > 0: # stop is in front of camera\n\n bb = self._create_2d_bb_points(trigger, 4)\n trig_loc_world = self._trig_to_world(bb, stop, trigger)\n cords_x_y_z = self._world_to_sensor(trig_loc_world, self._get_sensor_position(cam), True)\n\n #if cords_x_y_z.size: \n cords_x_y_z = cords_x_y_z[:3, :]\n cords_y_minus_z_x = np.concatenate([cords_x_y_z[1, :], -cords_x_y_z[2, :], cords_x_y_z[0, :]])\n bbox = (self._sensor_data['calibration'] @ cords_y_minus_z_x).T\n\n camera_bbox = np.concatenate([bbox[:, 0] / bbox[:, 2], bbox[:, 1] / bbox[:, 2], bbox[:, 2]], axis=1)\n\n if np.any(camera_bbox[:,2] > 0):\n\n camera_bbox = np.array(camera_bbox)\n\n polygon = [(camera_bbox[i, 0], camera_bbox[i, 1]) for i in range(len(camera_bbox))]\n\n img = Image.new('L', (self._sensor_data['width'], self._sensor_data['height']), 0)\n ImageDraw.Draw(img).polygon(polygon, outline=1, fill=1)\n _region = np.array(img)\n\n #seg_img[(_region == 1)] = 27\n seg_img[(_region == 1) & (seg_img == 6)] = 27\n\n\n def _trig_to_world(self, bb, parent, trigger):\n \"\"\"Transforms the trigger coordinates to world coordinates\n\n Args:\n bb ([type]): [description]\n parent ([type]): [description]\n trigger ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n bb_transform = carla.Transform(trigger.location)\n bb_vehicle_matrix = self.get_matrix(bb_transform)\n vehicle_world_matrix = self.get_matrix(parent.get_transform())\n bb_world_matrix = vehicle_world_matrix @ bb_vehicle_matrix\n world_cords = bb_world_matrix @ bb.T\n return world_cords\n\n def _create_2d_bb_points(self, actor_bb, scale_factor=1):\n \"\"\"\n Returns 2D floor bounding box for an actor.\n \"\"\"\n\n cords = np.zeros((4, 4))\n extent = actor_bb.extent\n x = extent.x * scale_factor\n y = extent.y * scale_factor\n z = extent.z * scale_factor\n cords[0, :] = np.array([x, y, 0, 1])\n cords[1, :] = np.array([-x, y, 0, 1])\n cords[2, :] = np.array([-x, -y, 0, 1])\n cords[3, :] = np.array([x, -y, 0, 1])\n return cords\n\n def _get_sensor_position(self, cam):\n \"\"\"returns the sensor position and rotation\n\n Args:\n cam ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n sensor_transform = self._sensors[cam].get_transform()\n \n return sensor_transform\n\n def _world_to_sensor(self, cords, sensor, move_cords=False):\n \"\"\"\n Transforms world coordinates to sensor.\n \"\"\"\n\n sensor_world_matrix = self.get_matrix(sensor)\n world_sensor_matrix = np.linalg.inv(sensor_world_matrix)\n sensor_cords = np.dot(world_sensor_matrix, cords)\n\n if move_cords:\n _num_cords = range(sensor_cords.shape[1])\n modified_cords = np.array([])\n for i in _num_cords:\n if sensor_cords[0,i] < 0:\n for j in _num_cords:\n if sensor_cords[0,j] > 0:\n _direction = sensor_cords[:,i] - sensor_cords[:,j]\n _distance = -sensor_cords[0,j] / _direction[0]\n new_cord = sensor_cords[:,j] + _distance[0,0] * _direction * 0.9999\n modified_cords = np.hstack([modified_cords, new_cord]) if modified_cords.size else new_cord\n else:\n modified_cords = np.hstack([modified_cords, sensor_cords[:,i]]) if modified_cords.size else sensor_cords[:,i]\n\n return modified_cords\n else:\n return sensor_cords\n\n def get_matrix(self, transform):\n \"\"\"\n Creates matrix from carla transform.\n \"\"\"\n\n rotation = transform.rotation\n location = transform.location\n c_y = np.cos(np.radians(rotation.yaw))\n s_y = np.sin(np.radians(rotation.yaw))\n c_r = np.cos(np.radians(rotation.roll))\n s_r = np.sin(np.radians(rotation.roll))\n c_p = np.cos(np.radians(rotation.pitch))\n s_p = np.sin(np.radians(rotation.pitch))\n matrix = np.matrix(np.identity(4))\n matrix[0, 3] = location.x\n matrix[1, 3] = location.y\n matrix[2, 3] = location.z\n matrix[0, 0] = c_p * c_y\n matrix[0, 1] = c_y * s_p * s_r - s_y * c_r\n matrix[0, 2] = -c_y * s_p * c_r - s_y * s_r\n matrix[1, 0] = s_y * c_p\n matrix[1, 1] = s_y * s_p * s_r + c_y * c_r\n matrix[1, 2] = -s_y * s_p * c_r + c_y * s_r\n matrix[2, 0] = s_p\n matrix[2, 1] = -c_p * s_r\n matrix[2, 2] = c_p * c_r\n return matrix\n\n def _change_seg_tl(self, seg_img, depth_img, traffic_lights, _region_size=4):\n \"\"\"Adds 3 traffic light classes (green, yellow, red) to the segmentation image\n\n Args:\n seg_img ([type]): [description]\n depth_img ([type]): [description]\n traffic_lights ([type]): [description]\n _region_size (int, optional): [description]. Defaults to 4.\n \"\"\" \n for tl in traffic_lights:\n _dist = self._get_distance(tl.get_transform().location)\n \n _region = np.abs(depth_img - _dist)\n\n if tl.get_state() == carla.TrafficLightState.Red:\n state = 23\n elif tl.get_state() == carla.TrafficLightState.Yellow:\n state = 24\n elif tl.get_state() == carla.TrafficLightState.Green:\n state = 25\n else: #none of the states above, do not change class\n state = 18\n\n #seg_img[(_region >= _region_size)] = 0\n seg_img[(_region < _region_size) & (seg_img == 18)] = state\n\n\n def _get_dist(self, p1, p2):\n \"\"\"Returns the distance between p1 and p2\n\n Args:\n target ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n\n distance = np.sqrt(\n (p1[0] - p2[0]) ** 2 +\n (p1[1] - p2[1]) ** 2 +\n (p1[2] - p2[2]) ** 2)\n\n return distance\n\n def _get_distance(self, target):\n \"\"\"Returns the distance from the (rgb_front) camera to the target\n\n Args:\n target ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n sensor_transform = self._sensors['rgb_front'].get_transform()\n\n distance = np.sqrt(\n (sensor_transform.location.x - target.x) ** 2 +\n (sensor_transform.location.y - target.y) ** 2 +\n (sensor_transform.location.z - target.z) ** 2)\n\n return distance\n\n\n def _get_depth(self, data):\n \"\"\"Transforms the depth image into meters\n\n Args:\n data ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n\n data = data.astype(np.float32)\n\n normalized = np.dot(data, [65536.0, 256.0, 1.0]) \n normalized /= (256 * 256 * 256 - 1)\n in_meters = 1000 * normalized\n\n return in_meters\n\n def _find_obstacle(self, obstacle_type='*traffic_light*'):\n \"\"\"Find all actors of a certain type that are close to the vehicle\n\n Args:\n obstacle_type (str, optional): [description]. Defaults to '*traffic_light*'.\n\n Returns:\n [type]: [description]\n \"\"\" \n obst = list()\n \n _actors = self._world.get_actors()\n _obstacles = _actors.filter(obstacle_type)\n\n\n for _obstacle in _obstacles:\n trigger = _obstacle.trigger_volume\n\n _obstacle.get_transform().transform(trigger.location)\n \n distance_to_car = trigger.location.distance(self._vehicle.get_location())\n\n a = np.sqrt(\n trigger.extent.x ** 2 +\n trigger.extent.y ** 2 +\n trigger.extent.z ** 2)\n b = np.sqrt(\n self._vehicle.bounding_box.extent.x ** 2 +\n self._vehicle.bounding_box.extent.y ** 2 +\n self._vehicle.bounding_box.extent.z ** 2)\n\n s = a + b + 10\n \n\n if distance_to_car <= s:\n # the actor is affected by this obstacle.\n obst.append(_obstacle)\n\n return obst\n\n def _get_camera_to_car_calibration(self, sensor):\n \"\"\"returns the calibration matrix for the given sensor\n\n Args:\n sensor ([type]): [description]\n\n Returns:\n [type]: [description]\n \"\"\" \n calibration = np.identity(3)\n calibration[0, 2] = sensor[\"width\"] / 2.0\n calibration[1, 2] = sensor[\"height\"] / 2.0\n calibration[0, 0] = calibration[1, 1] = sensor[\"width\"] / (2.0 * np.tan(sensor[\"fov\"] * np.pi / 360.0))\n return calibration" }, { "path": "leaderboard/team_code/map_agent.py", "content": "import numpy as np\nfrom PIL import Image, ImageDraw\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\nfrom team_code.base_agent import BaseAgent\nfrom team_code.planner import RoutePlanner\n\n\nclass MapAgent(BaseAgent):\n def sensors(self):\n result = super().sensors()\n result.append({\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': 0.0, 'y': 0.0, 'z': 100.0,\n 'roll': 0.0, 'pitch': -90.0, 'yaw': 0.0,\n 'width': 512, 'height': 512, 'fov': 5 * 10.0,\n 'id': 'map'\n })\n\n return result\n\n def set_global_plan(self, global_plan_gps, global_plan_world_coord, wp=None):\n super().set_global_plan(global_plan_gps, global_plan_world_coord)\n\n self._plan_HACK = global_plan_world_coord\n self._plan_gps_HACK = global_plan_gps\n\n def _init(self):\n super()._init()\n\n self._vehicle = CarlaDataProvider.get_hero_actor()\n self._world = self._vehicle.get_world()\n\n self._waypoint_planner = RoutePlanner(4.0, 50)\n self._waypoint_planner.set_route(self._plan_gps_HACK, True)\n\n self._traffic_lights = list()\n\n def tick(self, input_data):\n self._actors = self._world.get_actors()\n self._traffic_lights = get_nearby_lights(self._vehicle, self._actors.filter('*traffic_light*'))\n self._stop_signs = get_nearby_lights(self._vehicle, self._actors.filter('*stop*'))\n\n topdown = input_data['map'][1][:, :, 2]\n topdown = draw_traffic_lights(topdown, self._vehicle, self._traffic_lights)\n topdown = draw_stop_signs(topdown, self._vehicle, self._stop_signs)\n\n result = super().tick(input_data)\n result['topdown'] = topdown\n\n return result\n\n\ndef get_nearby_lights(vehicle, lights, pixels_per_meter=5.5, size=512, radius=5):\n result = list()\n\n transform = vehicle.get_transform()\n pos = transform.location\n theta = np.radians(90 + transform.rotation.yaw)\n R = np.array([\n [np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)],\n ])\n\n for light in lights:\n delta = light.get_transform().location - pos\n\n target = R.T.dot([delta.x, delta.y])\n target *= pixels_per_meter\n target += size // 2\n\n if min(target) < 0 or max(target) >= size:\n continue\n\n trigger = light.trigger_volume\n light.get_transform().transform(trigger.location)\n dist = trigger.location.distance(vehicle.get_location())\n a = np.sqrt(\n trigger.extent.x ** 2 +\n trigger.extent.y ** 2 +\n trigger.extent.z ** 2)\n b = np.sqrt(\n vehicle.bounding_box.extent.x ** 2 +\n vehicle.bounding_box.extent.y ** 2 +\n vehicle.bounding_box.extent.z ** 2)\n\n if dist > a + b:\n continue\n\n result.append(light)\n\n return result\n\n\ndef draw_traffic_lights(image, vehicle, lights, pixels_per_meter=5.5, size=512, radius=5):\n image = Image.fromarray(image)\n draw = ImageDraw.Draw(image)\n\n transform = vehicle.get_transform()\n pos = transform.location\n theta = np.radians(90 + transform.rotation.yaw)\n R = np.array([\n [np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)],\n ])\n\n for light in lights:\n delta = light.get_transform().location - pos\n\n target = R.T.dot([delta.x, delta.y])\n target *= pixels_per_meter\n target += size // 2\n\n if min(target) < 0 or max(target) >= size:\n continue\n\n trigger = light.trigger_volume\n light.get_transform().transform(trigger.location)\n dist = trigger.location.distance(vehicle.get_location())\n a = np.sqrt(\n trigger.extent.x ** 2 +\n trigger.extent.y ** 2 +\n trigger.extent.z ** 2)\n b = np.sqrt(\n vehicle.bounding_box.extent.x ** 2 +\n vehicle.bounding_box.extent.y ** 2 +\n vehicle.bounding_box.extent.z ** 2)\n\n if dist > a + b:\n continue\n\n x, y = target\n draw.ellipse((x-radius, y-radius, x+radius, y+radius), 23 + light.state.real) # 13 changed to 23 for carla 0.9.10\n\n return np.array(image)\n\n\ndef draw_stop_signs(image, vehicle, lights, pixels_per_meter=5.5, size=512, radius=5):\n image = Image.fromarray(image)\n draw = ImageDraw.Draw(image)\n\n transform = vehicle.get_transform()\n pos = transform.location\n theta = np.radians(90 + transform.rotation.yaw)\n R = np.array([\n [np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)],\n ])\n\n for light in lights:\n delta = light.get_transform().location - pos\n\n target = R.T.dot([delta.x, delta.y])\n target *= pixels_per_meter\n target += size // 2\n\n if min(target) < 0 or max(target) >= size:\n continue\n\n trigger = light.trigger_volume\n light.get_transform().transform(trigger.location)\n dist = trigger.location.distance(vehicle.get_location())\n a = np.sqrt(\n trigger.extent.x ** 2 +\n trigger.extent.y ** 2 +\n trigger.extent.z ** 2)\n b = np.sqrt(\n vehicle.bounding_box.extent.x ** 2 +\n vehicle.bounding_box.extent.y ** 2 +\n vehicle.bounding_box.extent.z ** 2)\n\n if dist > a + b:\n continue\n\n x, y = target\n draw.ellipse((x-radius, y-radius, x+radius, y+radius), 26)\n\n return np.array(image)" }, { "path": "leaderboard/team_code/pid_controller.py", "content": "from collections import deque\n\nimport numpy as np\n\n\nclass PIDController(object):\n def __init__(self, K_P=1.0, K_I=0.0, K_D=0.0, n=20):\n self._K_P = K_P\n self._K_I = K_I\n self._K_D = K_D\n\n self._window = deque([0 for _ in range(n)], maxlen=n)\n self._max = 0.0\n self._min = 0.0\n\n def step(self, error):\n self._window.append(error)\n self._max = max(self._max, abs(error))\n self._min = -abs(self._max)\n\n if len(self._window) >= 2:\n integral = np.mean(self._window)\n derivative = (self._window[-1] - self._window[-2])\n else:\n integral = 0.0\n derivative = 0.0\n\n return self._K_P * error + self._K_I * integral + self._K_D * derivative\n" }, { "path": "leaderboard/team_code/planner.py", "content": "import os\nfrom collections import deque\n\nimport numpy as np\n\n\nDEBUG = int(os.environ.get('HAS_DISPLAY', 0))\n\n\nclass Plotter(object):\n def __init__(self, size):\n self.size = size\n self.clear()\n self.title = str(self.size)\n\n def clear(self):\n from PIL import Image, ImageDraw\n\n self.img = Image.fromarray(np.zeros((self.size, self.size, 3), dtype=np.uint8))\n self.draw = ImageDraw.Draw(self.img)\n\n def dot(self, pos, node, color=(255, 255, 255), r=2):\n x, y = 5.5 * (pos - node)\n x += self.size / 2\n y += self.size / 2\n\n self.draw.ellipse((x-r, y-r, x+r, y+r), color)\n\n def show(self):\n if not DEBUG:\n return\n\n import cv2\n\n cv2.imshow(self.title, cv2.cvtColor(np.array(self.img), cv2.COLOR_BGR2RGB))\n cv2.waitKey(1)\n\n\nclass RoutePlanner(object):\n def __init__(self, min_distance, max_distance, debug_size=256):\n self.route = deque()\n self.min_distance = min_distance\n self.max_distance = max_distance\n\n # self.mean = np.array([49.0, 8.0]) # for carla 9.9\n # self.scale = np.array([111324.60662786, 73032.1570362]) # for carla 9.9\n self.mean = np.array([0.0, 0.0]) # for carla 9.10\n self.scale = np.array([111324.60662786, 111319.490945]) # for carla 9.10\n\n self.debug = Plotter(debug_size)\n\n def set_route(self, global_plan, gps=False, global_plan_world = None):\n self.route.clear()\n\n if global_plan_world:\n for (pos, cmd), (pos_word, _ )in zip(global_plan, global_plan_world):\n if gps:\n pos = np.array([pos['lat'], pos['lon']])\n pos -= self.mean\n pos *= self.scale\n else:\n pos = np.array([pos.location.x, pos.location.y])\n pos -= self.mean\n \n self.route.append((pos, cmd, pos_word))\n else:\n for pos, cmd in global_plan:\n if gps:\n pos = np.array([pos['lat'], pos['lon']])\n pos -= self.mean\n pos *= self.scale\n else:\n pos = np.array([pos.location.x, pos.location.y])\n pos -= self.mean\n\n self.route.append((pos, cmd))\n\n def run_step(self, gps):\n self.debug.clear()\n\n if len(self.route) == 1:\n return self.route[0]\n\n to_pop = 0\n farthest_in_range = -np.inf\n cumulative_distance = 0.0\n\n for i in range(1, len(self.route)):\n if cumulative_distance > self.max_distance:\n break\n\n cumulative_distance += np.linalg.norm(self.route[i][0] - self.route[i-1][0])\n distance = np.linalg.norm(self.route[i][0] - gps)\n\n if distance <= self.min_distance and distance > farthest_in_range:\n farthest_in_range = distance\n to_pop = i\n\n r = 255 * int(distance > self.min_distance)\n g = 255 * int(self.route[i][1].value == 4)\n b = 255\n self.debug.dot(gps, self.route[i][0], (r, g, b))\n\n for _ in range(to_pop):\n if len(self.route) > 2:\n self.route.popleft()\n\n self.debug.dot(gps, self.route[0][0], (0, 255, 0))\n self.debug.dot(gps, self.route[1][0], (255, 0, 0))\n self.debug.dot(gps, gps, (0, 0, 255))\n self.debug.show()\n\n return self.route[1]\n" }, { "path": "leaderboard/team_code/roach_ap_agent_data_collection.py", "content": "import os\nimport carla\n\nimport json\nimport datetime\nimport pathlib\nimport time\nimport cv2\nfrom collections import deque\nimport random\n\nimport torch\nimport numpy as np\nfrom PIL import Image\n\nfrom leaderboard.autoagents import autonomous_agent\nimport numpy as np\nfrom omegaconf import OmegaConf\nimport copy\n\nfrom roach.criteria import run_stop_sign\nfrom roach.obs_manager.birdview.chauffeurnet import ObsManager\nfrom roach.utils.config_utils import load_entry_point\nimport roach.utils.transforms as trans_utils\nfrom roach.utils.expert_noiser import ExpertNoiser\nfrom roach.utils.traffic_light import TrafficLightHandler\nimport io\nfrom io import BytesIO\n\ndef array_to_bytes(x: np.ndarray) -> bytes:\n np_bytes = BytesIO()\n np.save(np_bytes, x, allow_pickle=True)\n return np_bytes.getvalue()\n\ndef bytes_to_array(b: bytes) -> np.ndarray:\n np_bytes = BytesIO(b)\n return np.load(np_bytes, allow_pickle=True)\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom leaderboard.utils.route_manipulation import downsample_route\nfrom agents.navigation.local_planner import RoadOption\n\nfrom team_code.planner import RoutePlanner\ndef np_encoder(object):\n if isinstance(object, np.generic):\n return object.item()\n\nSAVE_PATH = os.environ.get('SAVE_PATH', None)\nconf_path = '~/petreloss.conf'\n\n\n\ndef get_entry_point():\n return 'ROACHAgent'\n\ndef _numpy(carla_vector, normalize=False):\n result = np.float32([carla_vector.x, carla_vector.y])\n if normalize:\n return result / (np.linalg.norm(result) + 1e-4)\n return result\n\n\ndef _location(x, y, z):\n return carla.Location(x=float(x), y=float(y), z=float(z))\n\n\ndef get_xyz(_):\n return np.array([_.x, _.y, _.z])\n\n\ndef _orientation(yaw):\n return np.float32([np.cos(np.radians(yaw)), np.sin(np.radians(yaw))])\n\n\ndef get_collision(p1, v1, p2, v2):\n A = np.stack([v1, -v2], 1)\n b = p2 - p1\n if abs(np.linalg.det(A)) < 1e-3:\n return False, None\n x = np.linalg.solve(A, b)\n collides = all(x >= 0) and all(x <= 1) # how many seconds until collision\n\n return collides, p1 + x[0] * v1\n\n\ndef get_nearby_object(vehicle_position, actor_list, radius):\n nearby_objects = []\n for actor in actor_list:\n trigger_box_global_pos = actor.get_transform().transform(actor.trigger_volume.location)\n trigger_box_global_pos = carla.Location(x=trigger_box_global_pos.x, y=trigger_box_global_pos.y, z=trigger_box_global_pos.z)\n if (trigger_box_global_pos.distance(vehicle_position) < radius):\n nearby_objects.append(actor)\n return nearby_objects\n\nclass ROACHAgent(autonomous_agent.AutonomousAgent):\n def setup(self, path_to_conf_file, ckpt=\"roach/log/ckpt_11833344.pth\"):\n self.is_local = (os.environ['IS_LOCAL'] == \"True\") ## Ignore non-local situation since we collect data in a cluster with ceph\n if not self.is_local:\n from petrel_client.client import Client\n self.client = Client(conf_path)\n \n self._render_dict = None\n self.supervision_dict = None\n self._ckpt = ckpt\n cfg = OmegaConf.load(path_to_conf_file)\n cfg = OmegaConf.to_container(cfg)\n self.cfg = cfg\n self._obs_configs = cfg['obs_configs']\n self._train_cfg = cfg['training']\n self._policy_class = load_entry_point(cfg['policy']['entry_point'])\n self._policy_kwargs = cfg['policy']['kwargs']\n if self._ckpt is None:\n self._policy = None\n else:\n self._policy, self._train_cfg['kwargs'] = self._policy_class.load(self._ckpt)\n self._policy = self._policy.eval()\n self._wrapper_class = load_entry_point(cfg['env_wrapper']['entry_point'])\n self._wrapper_kwargs = cfg['env_wrapper']['kwargs']\n\n self.track = autonomous_agent.Track.SENSORS\n self.config_path = path_to_conf_file\n self.step = -1\n self.wall_start = time.time()\n self.initialized = False\n\n self.prev_lidar = None ## The frequency of lidar is 10 Hz while the frequency of simulation is 20Hz -> In each frame, the sensor only returns half lidar points.\n self._active_traffic_light = None\n self.save_path = None\n if SAVE_PATH is not None:\n now = datetime.datetime.now()\n time_string = '_'.join(map(lambda x: '%02d' % x, (now.month, now.day, now.hour, now.minute, now.second)))\n string = pathlib.Path(os.environ['ROUTES']).stem + '_'\n string += time_string\n if self.is_local:\n self.save_path = pathlib.Path(os.environ['SAVE_PATH']) / string\n self.save_path.mkdir(parents=True, exist_ok=False)\n (self.save_path / '3d_bbs').mkdir(parents=True, exist_ok=True)\n\n (self.save_path / 'rgb_front').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_left').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_right').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_back').mkdir(parents=True, exist_ok=True)\n\n (self.save_path / 'seg_front').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'seg_left').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'seg_right').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'seg_back').mkdir(parents=True, exist_ok=True)\n\n (self.save_path / 'depth_front').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'depth_left').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'depth_right').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'depth_back').mkdir(parents=True, exist_ok=True)\n\n (self.save_path / 'lidar').mkdir(parents=True, exist_ok=True)\n\n (self.save_path / 'measurements').mkdir()\n (self.save_path / 'supervision').mkdir()\n (self.save_path / 'bev').mkdir() ## Visualization of Scene for debug\n (self.save_path / 'bev_seg_label').mkdir()\n else:\n ## Ignore non-local situation since we collect data in a cluster with ceph\n self.save_path = \"s3://CarlaData/Multiple/\" + os.environ['ROUTE_FILE'] + \"/\" + string\n self.folder_name = string\n\n def _init(self):\n self._waypoint_planner = RoutePlanner(4.0, 50)\n self._waypoint_planner.set_route(self._plan_gps_HACK, True)\n\n self._command_planner = RoutePlanner(7.5, 25.0, 257)\n self._command_planner.set_route(self._global_plan, True)\n\n self._route_planner = RoutePlanner(4.0, 50.0)\n self._route_planner.set_route(self._global_plan, True)\n\n self._world = CarlaDataProvider.get_world()\n self._map = self._world.get_map()\n\n self._ego_vehicle = CarlaDataProvider.get_ego()\n\n self._last_route_location = self._ego_vehicle.get_location()\n self._criteria_stop = run_stop_sign.RunStopSign(self._world)\n self.birdview_obs_manager = ObsManager(self.cfg['obs_configs']['birdview'], self._criteria_stop)\n self.birdview_obs_manager.attach_ego_vehicle(self._ego_vehicle)\n\n self.navigation_idx = -1\n # for stop signs\n self._target_stop_sign = None # the stop sign affecting the ego vehicle\n self._stop_completed = False # if the ego vehicle has completed the stop sign\n self._affected_by_stop = False # if the ego vehicle is influenced by a stop sign\n TrafficLightHandler.reset(self._world)\n print(\"initialized\")\n self.initialized = True\n\n def _get_angle_to(self, pos, theta, target):\n R = np.array([\n [np.cos(theta), -np.sin(theta)],\n [np.sin(theta), np.cos(theta)],\n ])\n aim = R.T.dot(target - pos)\n angle = -np.degrees(np.arctan2(-aim[1], aim[0]))\n angle = 0.0 if np.isnan(angle) else angle\n return angle\n\n\n def _truncate_global_route_till_local_target(self, windows_size=5):\n ev_location = self._ego_vehicle.get_location()\n closest_idx = 0\n for i in range(len(self._global_route)-1):\n if i > windows_size:\n break\n loc0 = self._global_route[i][0].transform.location\n loc1 = self._global_route[i+1][0].transform.location\n wp_dir = loc1 - loc0\n wp_veh = ev_location - loc0\n dot_ve_wp = wp_veh.x * wp_dir.x + wp_veh.y * wp_dir.y + wp_veh.z * wp_dir.z\n if dot_ve_wp > 0:\n closest_idx = i+1\n if closest_idx > 0:\n self._last_route_location = carla.Location(self._global_route[0][0].transform.location)\n self._global_route = self._global_route[closest_idx:]\n\n def _get_position(self, tick_data):\n gps = tick_data['gps']\n gps = (gps - self._command_planner.mean) * self._command_planner.scale\n return gps\n\n def set_global_plan(self, global_plan_gps, global_plan_world_coord, wp_route):\n \"\"\"\n Set the plan (route) for the agent\n \"\"\"\n self._global_route = wp_route\n ds_ids = downsample_route(global_plan_world_coord, 50)\n self._global_plan = [global_plan_gps[x] for x in ds_ids]\n self._global_plan_world_coord = [(global_plan_world_coord[x][0], global_plan_world_coord[x][1]) for x in ds_ids]\n self._plan_gps_HACK = global_plan_gps\n self._plan_HACK = global_plan_world_coord\n\n def sensors(self):\n return [\n {\n 'type': 'sensor.camera.rgb',\n 'x': 1.5, 'y': 0.0, 'z':2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_front'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': -0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_left'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': 0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_right'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': -1.6, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_back'\n },\n {\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': 1.3, 'y': 0.0, 'z':2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'seg_front'\n },\n {\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': 0, 'y': -0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'seg_left'\n },\n {\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': 0, 'y': 0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'seg_right'\n },\n {\n 'type': 'sensor.camera.semantic_segmentation',\n 'x': -1.6, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'seg_back'\n },\n {\n 'type': 'sensor.camera.depth',\n 'x': 1.3, 'y': 0.0, 'z':2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'depth_front'\n },\n {\n 'type': 'sensor.camera.depth',\n 'x': 0, 'y': -0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'depth_left'\n },\n {\n 'type': 'sensor.camera.depth',\n 'x': 0, 'y': 0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'depth_right'\n },\n {\n 'type': 'sensor.camera.depth',\n 'x': -1.6, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'depth_back'\n },\n\n { 'type': 'sensor.lidar.ray_cast',\n 'x': 0.0, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'id': 'lidar'\n },\n {\n 'type': 'sensor.other.imu',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.05,\n 'id': 'imu'\n },\n {\n 'type': 'sensor.other.gnss',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.01,\n 'id': 'gps'\n },\n {\n 'type': 'sensor.speedometer',\n 'reading_frequency': 20,\n 'id': 'speed'\n }\n ]\n\n def tick(self, input_data, timestamp):\n self._truncate_global_route_till_local_target()\n\n birdview_obs = self.birdview_obs_manager.get_observation(self._global_route)\n control = self._ego_vehicle.get_control()\n throttle = np.array([control.throttle], dtype=np.float32)\n steer = np.array([control.steer], dtype=np.float32)\n brake = np.array([control.brake], dtype=np.float32)\n gear = np.array([control.gear], dtype=np.float32)\n\n ev_transform = self._ego_vehicle.get_transform()\n vel_w = self._ego_vehicle.get_velocity()\n vel_ev = trans_utils.vec_global_to_ref(vel_w, ev_transform.rotation)\n vel_xy = np.array([vel_ev.x, vel_ev.y], dtype=np.float32)\n self._criteria_stop.tick(self._ego_vehicle, timestamp)\n\n state_list = []\n state_list.append(throttle)\n state_list.append(steer)\n state_list.append(brake)\n state_list.append(gear)\n state_list.append(vel_xy)\n state = np.concatenate(state_list)\n obs_dict = {\n 'state': state.astype(np.float32),\n 'birdview': birdview_obs['masks'],\n }\n # Roach Input: Road Mask - 0, Route Mask - 1, Lane_Mask (broken lane - 0.5) -2, 4 * vehicle - 3456, 4 * walker, 4 * traffic_light [-16, -11, -6, -1] 10hz\n bev_seg_label = birdview_obs['masks'].copy().astype(np.float32)/255.0\n\n rgb_front = cv2.cvtColor(input_data['rgb_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_left = cv2.cvtColor(input_data['rgb_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_right = cv2.cvtColor(input_data['rgb_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_back = cv2.cvtColor(input_data['rgb_back'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n\n gps = input_data['gps'][1][:2]\n speed = input_data['speed'][1]['speed']\n compass = input_data['imu'][1][-1]\n acceleration = input_data['imu'][1][:3]\n angular_velocity = input_data['imu'][1][3:6]\n target_gps, target_command = self.get_target_gps(input_data['gps'][1], compass)\n weather = self._weather_to_dict(self._world.get_weather())\n\n seg_front = np.copy(input_data[\"seg_front\"][1][:, :, 2])\n seg_left = np.copy(input_data[\"seg_left\"][1][:, :, 2])\n seg_right = np.copy(input_data[\"seg_right\"][1][:, :, 2])\n seg_back = np.copy(input_data[\"seg_back\"][1][:, :, 2])\n\n depth_front = cv2.cvtColor(input_data['depth_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n depth_left = cv2.cvtColor(input_data['depth_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n depth_right = cv2.cvtColor(input_data['depth_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n depth_back = cv2.cvtColor(input_data['depth_back'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n\n result = {\n 'rgb_front': rgb_front,\n 'rgb_left': rgb_left,\n 'rgb_right': rgb_right,\n 'rgb_back': rgb_back,\n 'seg_front': seg_front,\n 'seg_left': seg_left,\n 'seg_right': seg_right,\n 'seg_back': seg_back,\n 'depth_front': depth_front,\n 'depth_left': depth_left,\n 'depth_right': depth_right,\n 'depth_back': depth_back,\n 'lidar' : input_data['lidar'][1],\n 'gps': gps,\n 'speed': speed,\n 'compass': compass,\n 'weather': weather,\n \"acceleration\":acceleration,\n \"angular_velocity\":angular_velocity,\n \"bev_seg_label\": bev_seg_label\n }\n next_wp, next_cmd = self._route_planner.run_step(self._get_position(result))\n result['next_command'] = next_cmd.value\n result['x_target'] = next_wp[0]\n result['y_target'] = next_wp[1]\n return result, obs_dict, birdview_obs['rendered'], target_gps, target_command, None\n\n def im_render(self, render_dict):\n im_birdview = render_dict['rendered']\n h, w, c = im_birdview.shape\n im = np.zeros([h, w*2, c], dtype=np.uint8)\n im[:h, :w] = im_birdview\n action_str = np.array2string(render_dict['action'], precision=2, separator=',', suppress_small=True)\n txt_1 = f'a{action_str}'\n im = cv2.putText(im, txt_1, (3, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)\n debug_texts = [\n 'should_brake: ' + render_dict['should_brake'],\n ]\n for i, txt in enumerate(debug_texts):\n im = cv2.putText(im, txt, (w, (i+2)*12), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)\n return im\n\n @torch.no_grad()\n def run_step(self, input_data, timestamp):\n if not self.initialized:\n self._init()\n self.step += 1\n\n if self.step < 20:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n self.last_control = control\n self.prev_lidar = input_data['lidar'][1]\n self.prev_matrix = self._ego_vehicle.get_transform().get_matrix()\n return control\n\n if self.step % 2 != 0:\n self.prev_lidar = input_data['lidar'][1]\n self.prev_matrix = self._ego_vehicle.get_transform().get_matrix()\n return self.last_control\n tick_data, policy_input, rendered, target_gps, target_command, _ = self.tick(input_data, timestamp)\n\n gps = self._get_position(tick_data)\n near_node, near_command = self._waypoint_planner.run_step(gps)\n far_node, far_command = self._command_planner.run_step(gps)\n ## Roach forward\n actions, values, log_probs, mu, sigma, features, cnn_feature = self._policy.forward(\n policy_input, deterministic=True, clip_action=True)\n control = self.process_act(actions)\n render_dict = {\"rendered\": rendered, \"action\": actions}\n ## Rules for emergency brake \n should_brake = self.collision_detect()\n only_ap_brake = True if (control.brake <= 0 and should_brake) else False\n if should_brake:\n control.steer = control.steer * 0.5\n control.throttle = 0.0\n control.brake = 1.0\n render_dict = {\"rendered\": rendered, \"action\": actions, \"should_brake\":str(should_brake),}\n\n render_img = self.im_render(render_dict)\n supervision_dict = {\n 'action': np.array([control.throttle, control.steer, control.brake], dtype=np.float32),\n 'value': values[0],\n 'action_mu': mu[0],\n 'action_sigma': sigma[0],\n 'features': features[0],\n 'cnn_features':cnn_feature,\n 'speed': tick_data['speed'],\n 'target_gps': target_gps,\n 'target_command': target_command,\n 'should_brake': should_brake,\n 'only_ap_brake': only_ap_brake,\n }\n\n\n if SAVE_PATH is not None and self.step % 10 == 0:\n self.save(near_node, far_node, near_command, far_command, tick_data, supervision_dict, render_img, should_brake)\n\n steer = control.steer\n control.steer = steer + 1e-2 * np.random.randn() ## Random noise for robustness\n self.last_control = control\n self.prev_lidar = input_data['lidar'][1]\n self.prev_matrix = self._ego_vehicle.get_transform().get_matrix()\n return control\n\n def collision_detect(self):\n actors = self._world.get_actors()\n vehicle = self._is_vehicle_hazard(actors.filter('*vehicle*'))\n walker = self._is_walker_hazard(actors.filter('*walker*'))\n self.is_vehicle_present = 1 if vehicle is not None else 0\n self.is_pedestrian_present = 1 if walker is not None else 0\n return any(x is not None for x in [vehicle, walker])\n\n def _is_walker_hazard(self, walkers_list):\n z = self._ego_vehicle.get_location().z\n p1 = _numpy(self._ego_vehicle.get_location())\n v1 = 10.0 * _orientation(self._ego_vehicle.get_transform().rotation.yaw)\n\n for walker in walkers_list:\n v2_hat = _orientation(walker.get_transform().rotation.yaw)\n s2 = np.linalg.norm(_numpy(walker.get_velocity()))\n if s2 < 0.05:\n v2_hat *= s2\n p2 = -3.0 * v2_hat + _numpy(walker.get_location())\n v2 = 8.0 * v2_hat\n collides, collision_point = get_collision(p1, v1, p2, v2)\n if collides:\n return walker\n return None\n\n def _is_vehicle_hazard(self, vehicle_list):\n z = self._ego_vehicle.get_location().z\n o1 = _orientation(self._ego_vehicle.get_transform().rotation.yaw)\n p1 = _numpy(self._ego_vehicle.get_location())\n s1 = max(10, 3.0 * np.linalg.norm(_numpy(self._ego_vehicle.get_velocity()))) # increases the threshold distance\n v1_hat = o1\n v1 = s1 * v1_hat\n for target_vehicle in vehicle_list:\n if target_vehicle.id == self._ego_vehicle.id:\n continue\n o2 = _orientation(target_vehicle.get_transform().rotation.yaw)\n p2 = _numpy(target_vehicle.get_location())\n s2 = max(5.0, 2.0 * np.linalg.norm(_numpy(target_vehicle.get_velocity())))\n v2_hat = o2\n v2 = s2 * v2_hat\n p2_p1 = p2 - p1\n distance = np.linalg.norm(p2_p1)\n p2_p1_hat = p2_p1 / (distance + 1e-4)\n\n angle_to_car = np.degrees(np.arccos(v1_hat.dot(p2_p1_hat)))\n angle_between_heading = np.degrees(np.arccos(o1.dot(o2)))\n\n # to consider -ve angles too\n angle_to_car = min(angle_to_car, 360.0 - angle_to_car)\n angle_between_heading = min(angle_between_heading, 360.0 - angle_between_heading)\n\n if angle_between_heading > 60.0 and not (angle_to_car < 15 and distance < s1):\n continue\n elif angle_to_car > 30.0:\n continue\n elif distance > s1:\n continue\n return target_vehicle\n return None\n\n def save(self, near_node, far_node, near_command, far_command, tick_data, supervision_dict, render_img, should_brake):\n frame = self.step // 10 - 2\n if self.is_local:\n Image.fromarray(tick_data['rgb_front']).save(self.save_path / 'rgb_front' / ('%04d.png' % frame))\n Image.fromarray(tick_data['rgb_left']).save(self.save_path / 'rgb_left' / ('%04d.png' % frame))\n Image.fromarray(tick_data['rgb_right']).save(self.save_path / 'rgb_right' / ('%04d.png' % frame))\n Image.fromarray(tick_data['rgb_back']).save(self.save_path / 'rgb_back' / ('%04d.png' % frame))\n Image.fromarray(tick_data['seg_front']).save(self.save_path / 'seg_front' / ('%04d.png' % frame))\n Image.fromarray(tick_data['seg_left']).save(self.save_path / 'seg_left' / ('%04d.png' % frame))\n Image.fromarray(tick_data['seg_right']).save(self.save_path / 'seg_right' / ('%04d.png' % frame))\n Image.fromarray(tick_data['seg_back']).save(self.save_path / 'seg_back' / ('%04d.png' % frame))\n Image.fromarray(tick_data['depth_front']).save(self.save_path / 'depth_front' / ('%04d.png' % frame))\n Image.fromarray(tick_data['depth_left']).save(self.save_path / 'depth_left' / ('%04d.png' % frame))\n Image.fromarray(tick_data['depth_right']).save(self.save_path / 'depth_right' / ('%04d.png' % frame))\n Image.fromarray(tick_data['depth_back']).save(self.save_path / 'depth_back' / ('%04d.png' % frame))\n Image.fromarray(render_img).save(self.save_path / 'bev' / ('%04d.png' % frame))\n np.save(self.save_path / 'bev_seg_label' / ('%04d.npy' % frame), tick_data['bev_seg_label'], allow_pickle=True)\n with open(self.save_path / 'supervision' / ('%04d.npy' % frame), 'wb') as f:\n np.save(f, supervision_dict)\n else:\n for key_name in [\"rgb_front\", \"rgb_left\", \"rgb_right\", \"rgb_back\", \"seg_front\", \"seg_left\", \"seg_right\", \"seg_back\", \"depth_front\", \"depth_left\", \"depth_right\", \"depth_back\", \"topdown\"]:\n self.client.put(os.path.join(self.save_path, key_name, '%04d.png' % frame), tick_data[key_name].tostring())\n self.client.put(os.path.join(self.save_path, \"bev_seg_label\", '%04d.npy' % frame), tick_data['bev_seg_label'].tostring())\n self.client.put(os.path.join(self.save_path, \"supervision\", '%04d.npy' % frame), array_to_bytes(supervision_dict))\n\n \n current_inv_mat = np.array(self._ego_vehicle.get_transform().get_inverse_matrix())\n relative_transform_mat = np.dot(current_inv_mat , np.array(self.prev_matrix)) #4 * 4\n transformed_prev_lidar_xyz = np.concatenate([self.prev_lidar[:, :3], np.ones((self.prev_lidar.shape[0], 1))], axis=1) # N * 4\n transformed_prev_lidar_xyz = np.einsum(\"ij,kj->ki\", relative_transform_mat, transformed_prev_lidar_xyz)\n transformed_prev_lidar_xyz = np.concatenate([transformed_prev_lidar_xyz[:, :3], self.prev_lidar[:, 3][:, np.newaxis]], axis=1)\n saved_lidar = np.concatenate([transformed_prev_lidar_xyz, tick_data['lidar']], axis=0)\n saved_lidar[:, 2] += 2.5 #offset lidar z\n \n actor_lis = self._get_3d_bbs(lidar=saved_lidar, max_distance=50)\n pos = self._get_position(tick_data)\n theta = tick_data['compass']\n speed = tick_data['speed']\n weather = tick_data['weather']\n data = {\n 'x': pos[0],\n 'y': pos[1],\n 'theta': theta,\n 'speed': speed,\n 'x_command_far': far_node[0],\n 'y_command_far': far_node[1],\n 'command_far': far_command.value,\n 'x_command_near': near_node[0],\n 'y_command_near': near_node[1],\n 'command_near': near_command.value,\n 'should_brake': should_brake,\n 'x_target': tick_data['x_target'],\n 'y_target': tick_data['y_target'],'target_command': tick_data['next_command'],\n 'weather': weather,\n \"acceleration\":tick_data[\"acceleration\"].tolist(),\n \"angular_velocity\":tick_data[\"angular_velocity\"].tolist()\n }\n\n if self.is_local:\n outfile = open(self.save_path / '3d_bbs' / ('%04d.json' % frame), 'w')\n json.dump(actor_lis, outfile, indent=4, default=np_encoder)\n outfile.close()\n\n outfile = open(self.save_path / 'measurements' / ('%04d.json' % frame), 'w')\n json.dump(data, outfile, indent=4)\n outfile.close()\n\n np.save(self.save_path / 'lidar' / ('%04d.npy' % frame), saved_lidar.astype(np.float32), allow_pickle=True)\n else:\n self.client.put(os.path.join(self.save_path, '3d_bbs', '%04d.json' % frame), json.dumps(actor_lis, default=np_encoder).encode('utf-8'))\n self.client.put(os.path.join(self.save_path, 'measurements', '%04d.json' % frame), json.dumps(data, default=np_encoder).encode('utf-8'))\n self.client.put(os.path.join(self.save_path, 'lidar', '%04d.npy' % frame), array_to_bytes(saved_lidar))\n\n\n def get_target_gps(self, gps, compass):\n # target gps\n def gps_to_location(gps):\n lat, lon, z = gps\n lat = float(lat)\n lon = float(lon)\n z = float(z)\n\n location = carla.Location(z=z)\n xy = (gps[:2] - self._command_planner.mean) * self._command_planner.scale\n location.x = xy[0]\n location.y = -xy[1] ## Dose not matter, because no explicit judge left or right\n return location\n global_plan_gps = self._global_plan\n next_gps, _ = global_plan_gps[self.navigation_idx+1]\n next_gps = np.array([next_gps['lat'], next_gps['lon'], next_gps['z']])\n next_vec_in_global = gps_to_location(next_gps) - gps_to_location(gps)\n ref_rot_in_global = carla.Rotation(yaw=np.rad2deg(compass)-90.0)\n loc_in_ev = trans_utils.vec_global_to_ref(next_vec_in_global, ref_rot_in_global)\n\n if np.sqrt(loc_in_ev.x**2+loc_in_ev.y**2) < 12.0 and loc_in_ev.x < 0.0:\n self.navigation_idx += 1\n\n self.navigation_idx = min(self.navigation_idx, len(global_plan_gps)-2)\n\n _, road_option_0 = global_plan_gps[max(0, self.navigation_idx)]\n gps_point, road_option_1 = global_plan_gps[self.navigation_idx+1]\n gps_point = np.array([gps_point['lat'], gps_point['lon'], gps_point['z']])\n\n if (road_option_0 in [RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT]) \\\n and (road_option_1 not in [RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT]):\n road_option = road_option_1\n else:\n road_option = road_option_0\n\n return np.array(gps_point, dtype=np.float32), np.array([road_option.value], dtype=np.int8)\n\n\n def process_act(self, action):\n acc = action[0][0]\n steer = action[0][1]\n if acc >= 0.0:\n throttle = acc\n brake = 0.0\n else:\n throttle = 0.0\n brake = np.abs(acc)\n\n throttle = np.clip(throttle, 0, 1)\n steer = np.clip(steer, -1, 1)\n brake = np.clip(brake, 0, 1)\n control = carla.VehicleControl(throttle=throttle, steer=steer, brake=brake)\n return control\n\n def _weather_to_dict(self, carla_weather):\n weather = {\n 'cloudiness': carla_weather.cloudiness,\n 'precipitation': carla_weather.precipitation,\n 'precipitation_deposits': carla_weather.precipitation_deposits,\n 'wind_intensity': carla_weather.wind_intensity,\n 'sun_azimuth_angle': carla_weather.sun_azimuth_angle,\n 'sun_altitude_angle': carla_weather.sun_altitude_angle,\n 'fog_density': carla_weather.fog_density,\n 'fog_distance': carla_weather.fog_distance,\n 'wetness': carla_weather.wetness,\n 'fog_falloff': carla_weather.fog_falloff,\n }\n return weather\n\n def _get_forward_speed(self, transform=None, velocity=None):\n \"\"\" Convert the vehicle transform directly to forward speed \"\"\"\n if not velocity:\n velocity = self._ego_vehicle.get_velocity()\n if not transform:\n transform = self._ego_vehicle.get_transform()\n vel_np = np.array([velocity.x, velocity.y, velocity.z])\n pitch = np.deg2rad(transform.rotation.pitch)\n yaw = np.deg2rad(transform.rotation.yaw)\n orientation = np.array([np.cos(pitch) * np.cos(yaw), np.cos(pitch) * np.sin(yaw), np.sin(pitch)])\n speed = np.dot(vel_np, orientation)\n return speed\n\n def get_points_in_bbox(self, ego_mat, vec_inv_mat, dx, lidar):\n transform_mat = np.dot(vec_inv_mat, ego_mat)\n lidar = np.concatenate([lidar[:, :3], np.ones((lidar.shape[0], 1))], axis=1)\n lidar = np.einsum(\"ij,kj->ki\", transform_mat, lidar)\n # check points in bbox\n x, y, z = dx / 2.\n num_points = ((lidar[:, 0] < x) & (lidar[:, 0] > -x) &\n (lidar[:, 1] < y) & (lidar[:, 1] > -y) &\n (lidar[:, 2] < z) & (lidar[:, 2] > -z)).sum()\n return num_points\n\n def _get_3d_bbs(self, lidar=None, max_distance=50):\n results = []\n ego_rotation = self._ego_vehicle.get_transform().rotation\n ego_matrix = np.array(self._ego_vehicle.get_transform().get_matrix()).astype(np.float32)\n ego_extent = self._ego_vehicle.bounding_box.extent\n ego_dx = np.array([ego_extent.x, ego_extent.y, ego_extent.z]).astype(np.float32) * 2.\n ego_yaw = ego_rotation.yaw/180.0*np.pi\n # also add ego box for visulization\n relative_rotation = [0, 0, 0]\n relative_pos = [0, 0, 0]\n # add vehicle velocity and brake flag\n ego_transform = self._ego_vehicle.get_transform()\n ego_control = self._ego_vehicle.get_control()\n ego_velocity = self._ego_vehicle.get_velocity()\n ego_speed = self._get_forward_speed(transform=ego_transform, velocity=ego_velocity) # In m/s\n ego_brake = ego_control.brake\n ego_inv_mat = np.array(self._ego_vehicle.get_transform().get_inverse_matrix()).astype(np.float32)\n ego_loc = self._ego_vehicle.get_location()\n # the position is in lidar coordinates\n result = {\"class\": \"vehicle\",\n \"extent\": [ego_dx[0], ego_dx[1], ego_dx[2],],\n \"relative_position\": [relative_pos[0], relative_pos[1], relative_pos[2]],\n #roll pitch yaw\n \"relative_rotation\": relative_rotation,\n \"yaw\":ego_yaw,\n \"num_points\": -1,\n \"distance\": -1,\n \"speed\": ego_speed,\n \"brake\": ego_brake,\n \"id\": int(self._ego_vehicle.id),\n 'ego_matrix': ego_matrix.tolist(),\n \"ego_inv_matrix\": ego_inv_mat.tolist(),\n }\n results = {}\n results[\"ego\"] = result\n results[\"vehicle\"+str(self._ego_vehicle.id)] = result\n\n self._actors = self._world.get_actors()\n for actor_type in [\"vehicle\", \"walker\", \"traffic_light\", \"stop\"]:\n selected_actors = self._actors.filter('*' + actor_type + '*')\n for selected_actor in selected_actors:\n if (selected_actor.get_location().distance(ego_loc) < max_distance):\n if (actor_type != \"vehicle\") or (selected_actor.id != self._ego_vehicle.id):\n selected_actor_rotation = selected_actor.get_transform().rotation\n selected_actor_matrix = np.array(selected_actor.get_transform().get_matrix())\n selected_actor_inv_matrix = np.array(selected_actor.get_transform().get_inverse_matrix())\n selected_actor_id = selected_actor.id\n if hasattr(selected_actor, 'bounding_box'):\n selected_actor_extent = selected_actor.bounding_box.extent\n dx = np.array([selected_actor_extent.x, selected_actor_extent.y, selected_actor_extent.z]).astype(np.float32) * 2.\n else:\n dx = np.array([0.5, 0.5, 2]) * 2.\n yaw = selected_actor_rotation.yaw/180*np.pi\n relative_yaw = yaw - ego_yaw\n relative_rotation = [selected_actor_rotation.roll-ego_rotation.roll, selected_actor_rotation.pitch-ego_rotation.pitch, selected_actor_rotation.yaw-ego_rotation.yaw]\n relative_rotation = [_/180*np.pi for _ in relative_rotation]\n relative_pos = np.dot(ego_inv_mat, selected_actor_matrix)[:3, 3].astype(np.float32)\n selected_actor_transform = selected_actor.get_transform()\n selected_actor_control = selected_actor_velocity = selected_actor_speed = selected_actor_brake = None\n if actor_type in [\"vehicle\", \"walker\"]:\n selected_actor_control = selected_actor.get_control()\n selected_actor_velocity = selected_actor.get_velocity()\n selected_actor_speed = self._get_forward_speed(transform=selected_actor_transform, velocity=selected_actor_velocity) # In m/s\n if actor_type == \"vehicle\":\n selected_actor_brake = selected_actor_control.brake\n #vehicle_brake = selected_actor_control.brake\n # filter bbox that didn't contains points of contains less points\n num_in_bbox_points = self.get_points_in_bbox(ego_matrix, selected_actor_inv_matrix, dx, lidar)\n distance = np.linalg.norm(relative_pos).astype(np.float32)\n result = {\n \"class\": actor_type,\n \"extent\": [dx[0], dx[1], dx[2],],\n \"relative_position\": [relative_pos[0], relative_pos[1], relative_pos[2]],\n \"relative_rotation\": relative_rotation,\n \"num_points\": int(num_in_bbox_points),\n \"distance\": distance,\n \"speed\": selected_actor_speed,\n \"brake\": selected_actor_brake,\n \"id\": int(selected_actor_id),\n \"ego_matrix\": selected_actor_matrix.tolist(),\n \"ego_inv_matrix\":selected_actor_inv_matrix.tolist(),\n }\n results[actor_type+str(selected_actor_id)] = result\n return results\n\n\n\n def _find_obstacle_3dbb(self, obstacle_type, max_distance=50):\n \"\"\"Returns a list of 3d bounding boxes of type obstacle_type.\n If the object does have a bounding box, this is returned. Otherwise a bb\n of size 0.5,0.5,2 is returned at the origin of the object.\n\n Args:\n obstacle_type (String): Regular expression\n max_distance (int, optional): max search distance. Returns all bbs in this radius. Defaults to 50.\n\n Returns:\n List: List of Boundingboxes\n \"\"\"\n obst = list()\n\n _actors = self._world.get_actors()\n _obstacles = _actors.filter(obstacle_type)\n\n for _obstacle in _obstacles:\n distance_to_car = _obstacle.get_transform().location.distance(self._ego_vehicle.get_location())\n\n if 0 < distance_to_car <= max_distance:\n\n if hasattr(_obstacle, 'bounding_box'):\n loc = _obstacle.bounding_box.location\n _obstacle.get_transform().transform(loc)\n\n extent = _obstacle.bounding_box.extent\n _rotation_matrix = self.get_matrix(carla.Transform(carla.Location(0,0,0), _obstacle.get_transform().rotation))\n\n rotated_extent = np.squeeze(np.array((np.array([[extent.x, extent.y, extent.z, 1]]) @ _rotation_matrix)[:3]))\n\n bb = np.array([\n [loc.x, loc.y, loc.z],\n [rotated_extent[0], rotated_extent[1], rotated_extent[2]]\n ])\n\n else:\n loc = _obstacle.get_transform().location\n bb = np.array([\n [loc.x, loc.y, loc.z],\n [0.5, 0.5, 2]\n ])\n\n obst.append(bb)\n\n return obst\n\n def get_matrix(self, transform):\n \"\"\"\n Creates matrix from carla transform.\n \"\"\"\n\n rotation = transform.rotation\n location = transform.location\n c_y = np.cos(np.radians(rotation.yaw))\n s_y = np.sin(np.radians(rotation.yaw))\n c_r = np.cos(np.radians(rotation.roll))\n s_r = np.sin(np.radians(rotation.roll))\n c_p = np.cos(np.radians(rotation.pitch))\n s_p = np.sin(np.radians(rotation.pitch))\n matrix = np.matrix(np.identity(4))\n matrix[0, 3] = location.x\n matrix[1, 3] = location.y\n matrix[2, 3] = location.z\n matrix[0, 0] = c_p * c_y\n matrix[0, 1] = c_y * s_p * s_r - s_y * c_r\n matrix[0, 2] = -c_y * s_p * c_r - s_y * s_r\n matrix[1, 0] = s_y * c_p\n matrix[1, 1] = s_y * s_p * s_r + c_y * c_r\n matrix[1, 2] = -s_y * s_p * c_r + c_y * s_r\n matrix[2, 0] = s_p\n matrix[2, 1] = -c_p * s_r\n matrix[2, 2] = c_p * c_r\n return matrix\n\n" }, { "path": "leaderboard/team_code/thinktwice_agent.py", "content": "import os\nimport json\nimport datetime\nimport pathlib\nfrom select import select\nimport time\nfrom unittest import result\nimport cv2\nimport carla\nfrom collections import deque\nimport math\nfrom collections import OrderedDict\nimport pickle\nimport copy\nimport torch\nimport carla\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import transforms as T\n\nfrom leaderboard.autoagents import autonomous_agent\nimport sys\nfrom team_code.planner import RoutePlanner\nfrom importlib import import_module\nSAVE_PATH = os.environ.get('SAVE_PATH', None)\nimport cv2\nimport mmcv\nfrom mmcv import Config\nfrom mmdet3d.models import build_model\nfrom mmcv.runner import (get_dist_info, init_dist, load_checkpoint,\n wrap_fp16_model)\nfrom mmdet3d.datasets.pipelines import Compose\nfrom mmdet3d.core.points import get_points_type\n\nimport open_loop_training.code.datasets.carla_dataset as ds_module\nfrom mmcv.parallel.collate import collate as mm_collate_to_batch_form\n\n\ndef get_entry_point():\n return 'ThinkTwiceAgent'\n\nclass GlobalConfig:\n def __init__(self, init_dic):\n for k, v in init_dic.items():\n setattr(self, k, v)\n\ndef obtain_transform_matrix(x, y, yaw):\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n mat = np.array([\n [cp * cy, cy * sp * sr - sy * cr, -cy * sp * cr - sy * sr, x,],\n [cp * sy, sy * sp * sr + cy * cr, -sy * sp * cr + cy * sr, y],\n [sp, -cp * sr, cp * cr, 0],\n [0.0, 0.0, 0.0, 1.0],\n ])\n return mat\n\ndef InverseRotateVector(x, y, yaw):\n z = 0\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n out_x = x * (cp * cy) + y * (cp * sy) + z * (sp)\n out_y = x * (cy * sp * sr - sy * cr) + y * (sy * sp * sr + cy * cr) + z * (-cp * sr)\n return out_x, out_y\n\ndef obtain_inv_transform_matrix(x, y, yaw):\n x = -x\n y = -y\n x, y = InverseRotateVector(x, y, yaw)\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n inv_mat = np.array([\n [cp * cy, cp * sy, sp, x,],\n [cy * sp * sr - sy * cr, sy * sp * sr + cy * cr, -cp * sr, y,],\n [-cy * sp * cr - sy * sr, -sy * sp * cr + cy * sr, cp * cr, 0,],\n [0., 0., 0., 1.0],\n ])\n return inv_mat\n# Taken from World on Rails\nclass EgoModel():\n def __init__(self, dt=1./4):\n self.dt = dt\n \n # Kinematic bicycle model. Numbers are the tuned parameters from World on Rails\n self.front_wb = -0.090769015\n self.rear_wb = 1.4178275\n\n self.steer_gain = 0.36848336\n self.brake_accel = -4.952399\n self.throt_accel = 0.5633837\n\n def forward(self, locs, yaws, spds, acts):\n # Kinematic bicycle model. Numbers are the tuned parameters from World on Rails\n steer = acts[..., 0:1].item()\n throt = acts[..., 1:2].item()\n brake = acts[..., 2:3].astype(np.uint8)\n\n if (brake):\n accel = self.brake_accel\n else:\n accel = self.throt_accel * throt\n\n wheel = self.steer_gain * steer\n\n beta = math.atan(self.rear_wb / (self.front_wb + self.rear_wb) * math.tan(wheel))\n yaws = yaws.item()\n spds = spds.item()\n next_locs_0 = locs[0].item() + spds * math.cos(yaws + beta) * self.dt\n next_locs_1 = locs[1].item() + spds * math.sin(yaws + beta) * self.dt\n next_yaws = yaws + spds / self.rear_wb * math.sin(beta) * self.dt\n next_spds = spds + accel * self.dt\n next_spds = next_spds * (next_spds > 0.0) # Fast ReLU\n\n next_locs = np.array([next_locs_0, next_locs_1])\n next_yaws = np.array(next_yaws)\n next_spds = np.array(next_spds)\n\n return next_locs, next_yaws, next_spds\n \nclass ThinkTwiceAgent(autonomous_agent.AutonomousAgent):\n def setup(self, path_to_conf_file):\n self.device = \"cuda:0\"\n self.track = autonomous_agent.Track.SENSORS\n\n ### For temporal information\n self.data_queue = deque()\n self.data_queue_len = 31 ### Under 20 Hz\n self.pred_len = 4\n self.points_class = get_points_type('LIDAR')\n \n self.config_path = path_to_conf_file\n self.step = -1\n self.wall_start = time.time()\n self.initialized = False\n\n path_to_conf_file = path_to_conf_file.split(\"+\")\n ckpt_path = path_to_conf_file[0]\n config_path = path_to_conf_file[1]\n cfg = Config.fromfile(config_path)\n\n ## For mmcv\n if hasattr(cfg, 'plugin'):\n if cfg.plugin:\n import importlib\n if hasattr(cfg, 'plugin_dir'):\n plugin_dir = cfg.plugin_dir\n plugin_dir = os.path.join(\"open_loop_training\", plugin_dir)\n _module_dir = os.path.dirname(plugin_dir)\n _module_dir = _module_dir.split('/')\n _module_path = _module_dir[0]\n for m in _module_dir[1:]:\n _module_path = _module_path + '.' + m\n print(_module_path)\n plg_lib = importlib.import_module(_module_path)\n\n self.model = build_model(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))\n print(ckpt_path)\n checkpoint = load_checkpoint(self.model, ckpt_path, map_location='cpu')\n self.model = self.model.to(self.device)\n self.model.eval()\n\n ## For mmcv to preprocess single frames\n self.test_pipeline = []\n cfg.val_full_queue_pipeline[0].cfg.use_depth = False ##No gt for testing\n cfg.val_full_queue_pipeline[0].cfg.use_seg = False ##No gt for testing\n for test_pipeline in cfg.test_pipeline:\n if test_pipeline[\"type\"] not in [\"LoadMultiImages\", \"LoadPoints\", 'LoadDepth', 'LoadSeg']:\n self.test_pipeline .append(test_pipeline)\n self.test_pipeline = Compose(self.test_pipeline)\n ## For mmcv to preprocess temporal information\n self.seq_test_pipeline = Compose(cfg.val_full_queue_pipeline)\n\n self.save_path = None\n if SAVE_PATH is not None:\n now = datetime.datetime.now()\n string = pathlib.Path(os.environ['ROUTES']).stem + '_'\n string += '_'.join(map(lambda x: '%02d' % x, (now.month, now.day, now.hour, now.minute, now.second)))\n if len(path_to_conf_file) > 2:\n string += \"_index\" + str(path_to_conf_file[2]) + \"_\" + str(path_to_conf_file[3])\n self.save_path = pathlib.Path(os.environ['SAVE_PATH']) / string\n self.save_path.mkdir(parents=True, exist_ok=False)\n (self.save_path / 'meta').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_front').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_left').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_right').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_back').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'topdown').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'lidar').mkdir(parents=True, exist_ok=True)\n\n self.cfg = cfg\n self.folder_name = string\n \n ## Creep\n self.stuck_detector = 0\n self.stuck_threshold = 20.0\n\n\n topdown_extrinsics = np.array([[0.0, -0.0, -1.0, 50.0], [0.0, 1.0, -0.0, 0.0], [1.0, -0.0, 0.0, -0.0], [0.0, 0.0, 0.0, 1.0]])\n unreal2cam = np.array([[0,1,0,0], [0,0,-1,0], [1,0,0,0], [0,0,0,1]])\n self.coor2topdown = unreal2cam @ topdown_extrinsics\n topdown_intrinsics = np.array([[548.993771650447, 0.0, 256.0, 0], [0.0, 548.993771650447, 256.0, 0], [0.0, 0.0, 1.0, 0], [0, 0, 0, 1.0]])\n self.coor2topdown = topdown_intrinsics @ self.coor2topdown\n\n self.ego_model = EgoModel(dt=1.0 / 20.0)\n self.gps_buffer = deque(maxlen=100)\n\n def _init(self):\n self._route_planner = RoutePlanner(4.0, 50.0)\n self._route_planner.set_route(self._global_plan, True)\n self.prev_lidar = None\n self.prev_matrix = None\n self.initialized = True\n\n def _get_position(self, tick_data):\n gps = tick_data['gps']\n gps = (gps - self._route_planner.mean) * self._route_planner.scale\n return gps\n\n def sensors(self):\n return [\n {\n 'type': 'sensor.camera.rgb',\n 'x': 1.5, 'y': 0.0, 'z':2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_front'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': -0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_left'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': 0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_right'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': -1.6, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_back'\n },\n { 'type': 'sensor.lidar.ray_cast',\n 'x': 0.0, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'id': 'lidar'\n },\n {\n 'type': 'sensor.other.imu',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.05,\n 'id': 'imu'\n },\n {\n 'type': 'sensor.other.gnss',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.01,\n 'id': 'gps'\n },\n {\n 'type': 'sensor.speedometer',\n 'reading_frequency': 20,\n 'id': 'speed'\n },\n ### Debug sensor, not used by the model\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0.0, 'y': 0.0, 'z': 50.0,\n 'roll': 0.0, 'pitch': -90.0, 'yaw': 0.0,\n 'width': 512, 'height': 512, 'fov': 5 * 10.0,\n 'id': 'topdown'\n },\t\n ]\n def tick(self, input_data):\n self.step += 1\n \n topdown = cv2.cvtColor(input_data['topdown'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_front = cv2.cvtColor(input_data['rgb_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_left = cv2.cvtColor(input_data['rgb_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_right = cv2.cvtColor(input_data['rgb_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_back = cv2.cvtColor(input_data['rgb_back'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n\n gps = input_data['gps'][1][:2]\n speed = input_data['speed'][1]['speed']\n compass = input_data['imu'][1][-1]\n acceleration = input_data['imu'][1][:3]\n angular_velocity = input_data['imu'][1][3:6]\n if (math.isnan(compass) == True): #It can happen that the compass sends nan for a few frames\n compass = 0.0\n acceleration = np.zeros(3)\n angular_velocity = np.zeros(3)\n \n result = {\n 'rgb_front': rgb_front,\n 'rgb_left': rgb_left,\n 'rgb_right': rgb_right,\n 'rgb_back': rgb_back,\n 'gps': gps,\n 'speed': speed,\n 'theta': compass,\n \"acceleration\":acceleration,\n \"angular_velocity\":angular_velocity,\n \"topdown\": topdown,\n \"acceleration\":acceleration.tolist(),\n \"angular_velocity\":angular_velocity.tolist()\n }\n \n pos = self._get_position(result)\n self.gps_buffer.append(pos)\n pos = np.average(self.gps_buffer, axis=0)\n\n result[\"x\"] = pos[0]\n result[\"y\"] = pos[1]\n result['gps'] = pos\n next_wp, next_cmd = self._route_planner.run_step(pos)\n result['next_command'] = next_cmd.value\n result['x_target'] = next_wp[0]\n result['y_target'] = next_wp[1]\n\n ## Lidar is 10Hz and the simulator is running 20Hz -> Each frame the Lidar only returns 180 degree point clouds\n now_lidar = input_data['lidar'][1]\n if self.prev_lidar is not None:\n now_inv_mat = obtain_inv_transform_matrix(pos[1], -pos[0], compass-np.pi/2)\n relative_transform_mat = np.dot(now_inv_mat , np.array(self.prev_matrix)) #4 * 4\n transformed_prev_lidar_xyz = np.concatenate([self.prev_lidar[:, :3], np.ones((self.prev_lidar.shape[0], 1))], axis=1) # N * 4\n transformed_prev_lidar_xyz = np.einsum(\"ij,kj->ki\", relative_transform_mat, transformed_prev_lidar_xyz)\n transformed_prev_lidar_xyz = np.concatenate([transformed_prev_lidar_xyz[:, :3], self.prev_lidar[:, 3][:, np.newaxis]], axis=1)\n saved_lidar = np.concatenate([transformed_prev_lidar_xyz, now_lidar], axis=0).copy()\n saved_lidar[:, 2] += 2.5\n else:\n saved_lidar = now_lidar.copy()\n saved_lidar[:, 2] += 2.5\n\n\n result[\"lidar\"] = saved_lidar.astype(np.float32)\n self.prev_lidar = now_lidar\n self.prev_matrix = obtain_transform_matrix(pos[1], -pos[0], compass-np.pi/2)\n return result\n \n def offset_then_rotate(self, target_2d_world_coor, ref_2d_wolrd_coor, ref_yaw):\n final_coor = target_2d_world_coor - ref_2d_wolrd_coor\n R = np.array([\n [np.cos(ref_yaw), -np.sin(ref_yaw)],\n [np.sin(ref_yaw), np.cos(ref_yaw)]\n ])\n return np.einsum(\"ij,kj->ki\", R.T, final_coor)\n\n @torch.no_grad()\n def run_step(self, input_data, timestamp):\n if not self.initialized:\n self._init()\n tick_data = self.tick(input_data)\n ################# Preprocess ################\n results = {}\n ego_theta = tick_data[\"theta\"] if not np.isnan(tick_data[\"theta\"]) else 0\n ego_theta = ego_theta - np.pi/2\n results[\"input_theta\"] = ego_theta\n\n results[\"input_x\"] = tick_data[\"y\"]\n results[\"input_y\"] = -tick_data[\"x\"]\n ego_xy = np.stack([results[\"input_x\"], results[\"input_y\"]], axis=-1)\n results[\"speed\"] = tick_data[\"speed\"]\n results[\"can_bus\"] = np.zeros(18)\n results[\"can_bus\"][0] = results[\"input_x\"] #Gloabal\n results[\"can_bus\"][1] = results[\"input_y\"] #Global\n accel = np.array(tick_data[\"acceleration\"])\n accel[:2] = self.offset_then_rotate(np.array(accel[:2])[np.newaxis, :], np.array([0, 0]), ego_theta).squeeze(0)\n results[\"can_bus\"][7:10] = accel\n results[\"can_bus\"][10:13] = tick_data[\"angular_velocity\"]\n results[\"can_bus\"][13] = tick_data[\"speed\"]\n results[\"can_bus\"][-2] = ego_theta\n results[\"can_bus\"][-1] = ego_theta / np.pi * 180\n \n results['target_point'] = self.offset_then_rotate(np.array([[tick_data[\"y_target\"], -tick_data[\"x_target\"]]]), ego_xy, ego_theta).squeeze(0)\n\n command = tick_data['next_command']\n if command < 0:\n command = 4\n command -= 1\n results['target_command_raw'] = torch.tensor(command).long()\n assert command in [0, 1, 2, 3, 4, 5]\n cmd_one_hot = [0] * 6\n cmd_one_hot[command] = 1\n results['target_command'] = torch.tensor(cmd_one_hot)\n\n ## Inference mode, all empty\n results['waypoints'] = np.zeros(4)\n results[\"action\"] = np.zeros(3)\n results[\"action_mu\"] = np.zeros(2)\n results[\"action_sigma\"] = np.zeros(2)\n results['future_action_mu'] = np.zeros((self.pred_len, 2))\n results['future_action_sigma'] = np.zeros((self.pred_len, 2)) \n results['future_action'] = np.zeros((self.pred_len, 3)) \n results[\"value\"] = 0\n results[\"feature\"] = np.zeros(1)\n results[\"future_feature\"] = np.zeros((self.pred_len, 1))\n \n results[\"img\"] = [tick_data[camera_name] for camera_name in self.cfg[\"camera_list\"]]\n\n points = tick_data[\"lidar\"]\n points = self.points_class(\n points, points_dim=points.shape[-1], attribute_dims=None)\n results[\"points\"] = points\n results = self.test_pipeline(results)\n\n if len(self.data_queue) >= self.data_queue_len:\n self.data_queue.popleft()\n self.data_queue.append(results)\n\n if self.step < self.data_queue_len:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n self.update_gps_buffer(control, tick_data['theta'], tick_data['speed'])\n return control\n \n ## Preprocess\n selected_index_lis = []\n for selected_index in self.cfg[\"cfg\"][\"history_query_index_lis\"][:-1]:\n selected_index = selected_index * 10 ## train in 2Hz while Simulator is in 20 Hz\n selected_index_lis.append(selected_index-1)\n selected_index_lis.append(-1)\n input_data_queue = [copy.deepcopy(self.data_queue[selected_index]) for selected_index in selected_index_lis]\n input_data_union = ds_module.union2one(self.seq_test_pipeline, input_data_queue)\n input_data_batch = mm_collate_to_batch_form([input_data_union], samples_per_gpu=1)\n input_data_batch[\"img\"] = input_data_batch[\"img\"].data[0]\n if len(selected_index_lis) == 1:\n input_data_batch[\"img\"] = input_data_batch[\"img\"].squeeze(0)\n input_data_batch[\"points\"] = input_data_batch[\"points\"].data[0]\n input_data_batch[\"img_metas\"] = input_data_batch[\"img_metas\"].data[0]\n for _ in input_data_batch:\n if torch.is_tensor(input_data_batch[_]):\n input_data_batch[_] = input_data_batch[_].to(self.device)\n\n with torch.no_grad():\n pred = self.model.forward_inference(input_data_batch)\n gt_velocity = torch.FloatTensor([tick_data['speed']]).to(self.device, dtype=torch.float32)\n steer_ctrl, throttle_ctrl, brake_ctrl, metadata = self.model.process_action(pred, tick_data['next_command'], gt_velocity, results['target_point'])\n \n steer_traj, throttle_traj, brake_traj, metadata_traj = self.model.control_pid(pred['pred_wp'][:, -1, :, :], gt_velocity, results['target_point'])\n \n if brake_traj < 0.05: brake_traj = 0.0\n if throttle_traj > brake_traj: brake_traj = 0.0\n\n overall_pred_is_accel = ((throttle_traj>0) or (throttle_ctrl>0) or (brake_traj<0.95) or (brake_ctrl<0.95))\n overall_pred_is_brake = ((brake_traj>0.2) or (brake_ctrl>0.2))\n\n control = carla.VehicleControl()\n control.steer = steer_ctrl\n is_turn = False\n if abs(control.steer) > 0.07: ## In turning\n is_turn = True\n speed_threshold = 1.5 ## Reduce stuck during turning\n else:\n speed_threshold = 3.5 ## Recude red light infraction/collision\n\n if overall_pred_is_brake:\n control.brake=1.0\n control.throttle=0.0\n else:\n control.brake=0.0\n control.throttle=1.0\n\n is_stuck = False\n # By transfuser, crawl\n if(self.stuck_detector > self.stuck_threshold):\n print(\"Detected agent being stuck.\", \"Frame:\", self.step // 10)\n is_stuck = True\n if overall_pred_is_accel:\n control.brake = 0.0\n control.throttle = 1.0\n else:\n control.brake = 1.0\n control.throttle = 0.0\n \n if(float(gt_velocity) < 0.5): # just an arbitrary low number to threshhold when the car is stopped\n self.stuck_detector += 1\n elif(float(gt_velocity) > 0.5):\n self.stuck_detector = 0\n \n if float(tick_data['speed']) > speed_threshold:\n max_throttle = 0.05\n else:\n if is_turn:\n max_throttle = 0.4\n else:\n max_throttle = 0.6\n control.throttle = np.clip(control.throttle, a_min=0.0, a_max=max_throttle)\n\n self.pid_metadata = {}\n self.pid_metadata['steer_ctrl'] = float(steer_ctrl)\n self.pid_metadata['steer_traj'] = float(steer_traj)\n self.pid_metadata['throttle_ctrl'] = float(throttle_ctrl)\n self.pid_metadata['throttle_traj'] = float(throttle_traj)\n self.pid_metadata['brake_ctrl'] = float(brake_ctrl)\n self.pid_metadata['brake_traj'] = float(brake_traj)\n self.pid_metadata[\"is_stuck\"] = is_stuck\n self.pid_metadata[\"stuck_detector\"] = self.stuck_detector\n self.pid_metadata['steer'] = control.steer\n self.pid_metadata[\"brake\"] = control.brake\n self.pid_metadata[\"throttle\"] = control.throttle\n self.pid_metadata[\"speed\"] = float(tick_data['speed'])\n\n if SAVE_PATH is not None and self.step % 10 == 0:\n self.save(tick_data)\n \n self.update_gps_buffer(control, tick_data['theta'], tick_data['speed']) \n return control\n\n def save(self, tick_data):\n frame = self.step // 10\n Image.fromarray(tick_data['rgb_front']).save(self.save_path / 'rgb_front' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_left']).save(self.save_path / 'rgb_left' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_right']).save(self.save_path / 'rgb_right' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_back']).save(self.save_path / 'rgb_back' / ('%04d.png' % frame))\n Image.fromarray(tick_data['topdown']).save(self.save_path / 'topdown' / ('%04d.png' % frame))\n outfile = open(self.save_path / 'meta' / ('%04d.json' % frame), 'w')\n json.dump(self.pid_metadata, outfile, indent=4)\n outfile.close()\n #np.save(self.save_path / 'lidar' / ('%04d.npy' % frame), tick_data[\"lidar\"].astype(np.float32), allow_pickle=True)\n\n def destroy(self):\n del self.model\n torch.cuda.empty_cache()\n\n def update_gps_buffer(self, control, theta, speed):\n yaw = np.array([(theta - np.pi/2.0)])\n speed = np.array([speed])\n action = np.array(np.stack([control.steer, control.throttle, control.brake], axis=-1))\n\n #Update gps locations\n for i in range(len(self.gps_buffer)):\n loc =self.gps_buffer[i]\n loc_temp = np.array([loc[1], -loc[0]]) #Bicycle model uses a different coordinate system\n next_loc_tmp, _, _ = self.ego_model.forward(loc_temp, yaw, speed, action)\n next_loc = np.array([-next_loc_tmp[1], next_loc_tmp[0]])\n self.gps_buffer[i] = next_loc\n return None" }, { "path": "leaderboard/team_code/thinktwice_agent_old.py", "content": "import os\nimport json\nimport datetime\nimport pathlib\nfrom select import select\nimport time\nfrom unittest import result\nimport cv2\nimport carla\nfrom collections import deque\nimport math\nfrom collections import OrderedDict\nimport pickle\nimport copy\nimport torch\nimport carla\nimport numpy as np\nfrom PIL import Image\nfrom torchvision import transforms as T\n\nfrom leaderboard.autoagents import autonomous_agent\nimport sys\nfrom team_code.planner import RoutePlanner\nfrom importlib import import_module\nSAVE_PATH = os.environ.get('SAVE_PATH', None)\nimport cv2\nimport mmcv\nfrom mmcv import Config\nfrom mmdet3d.models import build_model\nfrom mmcv.runner import (get_dist_info, init_dist, load_checkpoint,\n wrap_fp16_model)\nfrom mmdet3d.datasets.pipelines import Compose\nfrom mmdet3d.core.points import get_points_type\n\nimport open_loop_training.code.datasets.carla_dataset as ds_module\nfrom mmcv.parallel.collate import collate as mm_collate_to_batch_form\n\n\ndef get_entry_point():\n return 'ThinkTwiceAgent'\n\nclass GlobalConfig:\n def __init__(self, init_dic):\n for k, v in init_dic.items():\n setattr(self, k, v)\n\ndef obtain_transform_matrix(x, y, yaw):\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n mat = np.array([\n [cp * cy, cy * sp * sr - sy * cr, -cy * sp * cr - sy * sr, x,],\n [cp * sy, sy * sp * sr + cy * cr, -sy * sp * cr + cy * sr, y],\n [sp, -cp * sr, cp * cr, 0],\n [0.0, 0.0, 0.0, 1.0],\n ])\n return mat\n\ndef InverseRotateVector(x, y, yaw):\n z = 0\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n out_x = x * (cp * cy) + y * (cp * sy) + z * (sp)\n out_y = x * (cy * sp * sr - sy * cr) + y * (sy * sp * sr + cy * cr) + z * (-cp * sr)\n return out_x, out_y\n\ndef obtain_inv_transform_matrix(x, y, yaw):\n x = -x\n y = -y\n x, y = InverseRotateVector(x, y, yaw)\n cy = math.cos(yaw)\n sy = math.sin(yaw)\n cr = 1\n sr = 0\n cp = 1\n sp = 0\n inv_mat = np.array([\n [cp * cy, cp * sy, sp, x,],\n [cy * sp * sr - sy * cr, sy * sp * sr + cy * cr, -cp * sr, y,],\n [-cy * sp * cr - sy * sr, -sy * sp * cr + cy * sr, cp * cr, 0,],\n [0., 0., 0., 1.0],\n ])\n return inv_mat\n# Taken from World on Rails\nclass EgoModel():\n def __init__(self, dt=1./4):\n self.dt = dt\n \n # Kinematic bicycle model. Numbers are the tuned parameters from World on Rails\n self.front_wb = -0.090769015\n self.rear_wb = 1.4178275\n\n self.steer_gain = 0.36848336\n self.brake_accel = -4.952399\n self.throt_accel = 0.5633837\n\n def forward(self, locs, yaws, spds, acts):\n # Kinematic bicycle model. Numbers are the tuned parameters from World on Rails\n steer = acts[..., 0:1].item()\n throt = acts[..., 1:2].item()\n brake = acts[..., 2:3].astype(np.uint8)\n\n if (brake):\n accel = self.brake_accel\n else:\n accel = self.throt_accel * throt\n\n wheel = self.steer_gain * steer\n\n beta = math.atan(self.rear_wb / (self.front_wb + self.rear_wb) * math.tan(wheel))\n yaws = yaws.item()\n spds = spds.item()\n next_locs_0 = locs[0].item() + spds * math.cos(yaws + beta) * self.dt\n next_locs_1 = locs[1].item() + spds * math.sin(yaws + beta) * self.dt\n next_yaws = yaws + spds / self.rear_wb * math.sin(beta) * self.dt\n next_spds = spds + accel * self.dt\n next_spds = next_spds * (next_spds > 0.0) # Fast ReLU\n\n next_locs = np.array([next_locs_0, next_locs_1])\n next_yaws = np.array(next_yaws)\n next_spds = np.array(next_spds)\n\n return next_locs, next_yaws, next_spds\n \nclass ThinkTwiceAgent(autonomous_agent.AutonomousAgent):\n def setup(self, path_to_conf_file):\n self.device = \"cuda:0\"\n self.track = autonomous_agent.Track.SENSORS\n\n ### For temporal information\n self.data_queue = deque()\n self.data_queue_len = 31 ### Under 20 Hz\n self.pred_len = 4\n self.points_class = get_points_type('LIDAR')\n \n self.config_path = path_to_conf_file\n self.step = -1\n self.wall_start = time.time()\n self.initialized = False\n\n path_to_conf_file = path_to_conf_file.split(\"+\")\n ckpt_path = path_to_conf_file[0]\n config_path = path_to_conf_file[1]\n cfg = Config.fromfile(config_path)\n\n ## For mmcv\n if hasattr(cfg, 'plugin'):\n if cfg.plugin:\n import importlib\n if hasattr(cfg, 'plugin_dir'):\n plugin_dir = cfg.plugin_dir\n plugin_dir = os.path.join(\"open_loop_training\", plugin_dir)\n _module_dir = os.path.dirname(plugin_dir)\n _module_dir = _module_dir.split('/')\n _module_path = _module_dir[0]\n for m in _module_dir[1:]:\n _module_path = _module_path + '.' + m\n print(_module_path)\n plg_lib = importlib.import_module(_module_path)\n\n self.model = build_model(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))\n print(ckpt_path)\n checkpoint = load_checkpoint(self.model, ckpt_path, map_location='cpu')\n self.model = self.model.to(self.device)\n self.model.eval()\n\n ## For mmcv to preprocess single frames\n self.test_pipeline = []\n cfg.val_full_queue_pipeline[0].cfg.use_depth = False ##No gt for testing\n cfg.val_full_queue_pipeline[0].cfg.use_seg = False ##No gt for testing\n for test_pipeline in cfg.test_pipeline:\n if test_pipeline[\"type\"] not in [\"LoadMultiImages\", \"LoadPoints\", 'LoadDepth', 'LoadSeg']:\n self.test_pipeline .append(test_pipeline)\n self.test_pipeline = Compose(self.test_pipeline)\n ## For mmcv to preprocess temporal information\n self.seq_test_pipeline = Compose(cfg.val_full_queue_pipeline)\n\n self.save_path = None\n if SAVE_PATH is not None:\n now = datetime.datetime.now()\n string = pathlib.Path(os.environ['ROUTES']).stem + '_'\n string += '_'.join(map(lambda x: '%02d' % x, (now.month, now.day, now.hour, now.minute, now.second)))\n if len(path_to_conf_file) > 2:\n string += \"_index\" + str(path_to_conf_file[2]) + \"_\" + str(path_to_conf_file[3])\n self.save_path = pathlib.Path(os.environ['SAVE_PATH']) / string\n self.save_path.mkdir(parents=True, exist_ok=False)\n (self.save_path / 'meta').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_front').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_left').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_right').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'rgb_back').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'topdown').mkdir(parents=True, exist_ok=True)\n (self.save_path / 'lidar').mkdir(parents=True, exist_ok=True)\n\n self.cfg = cfg\n self.folder_name = string\n \n ## Creep\n self.stuck_detector = 0\n self.stuck_threshold = 20.0\n\n\n topdown_extrinsics = np.array([[0.0, -0.0, -1.0, 50.0], [0.0, 1.0, -0.0, 0.0], [1.0, -0.0, 0.0, -0.0], [0.0, 0.0, 0.0, 1.0]])\n unreal2cam = np.array([[0,1,0,0], [0,0,-1,0], [1,0,0,0], [0,0,0,1]])\n self.coor2topdown = unreal2cam @ topdown_extrinsics\n topdown_intrinsics = np.array([[548.993771650447, 0.0, 256.0, 0], [0.0, 548.993771650447, 256.0, 0], [0.0, 0.0, 1.0, 0], [0, 0, 0, 1.0]])\n self.coor2topdown = topdown_intrinsics @ self.coor2topdown\n\n self.ego_model = EgoModel(dt=1.0 / 20.0)\n self.gps_buffer = deque(maxlen=100)\n\n def _init(self):\n self._route_planner = RoutePlanner(4.0, 50.0)\n self._route_planner.set_route(self._global_plan, True)\n self.prev_lidar = None\n self.prev_matrix = None\n self.initialized = True\n\n def _get_position(self, tick_data):\n gps = tick_data['gps']\n gps = (gps - self._route_planner.mean) * self._route_planner.scale\n return gps\n\n def sensors(self):\n return [\n {\n 'type': 'sensor.camera.rgb',\n 'x': 1.5, 'y': 0.0, 'z':2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_front'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': -0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': -90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_left'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0, 'y': 0.3, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 90.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_right'\n },\n {\n 'type': 'sensor.camera.rgb',\n 'x': -1.6, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 180.0,\n 'width': 1600, 'height': 900, 'fov': 150,\n 'id': 'rgb_back'\n },\n { 'type': 'sensor.lidar.ray_cast',\n 'x': 0.0, 'y': 0.0, 'z': 2.5,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'id': 'lidar'\n },\n {\n 'type': 'sensor.other.imu',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.05,\n 'id': 'imu'\n },\n {\n 'type': 'sensor.other.gnss',\n 'x': 0.0, 'y': 0.0, 'z': 0.0,\n 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'sensor_tick': 0.01,\n 'id': 'gps'\n },\n {\n 'type': 'sensor.speedometer',\n 'reading_frequency': 20,\n 'id': 'speed'\n },\n ### Debug sensor, not used by the model\n {\n 'type': 'sensor.camera.rgb',\n 'x': 0.0, 'y': 0.0, 'z': 50.0,\n 'roll': 0.0, 'pitch': -90.0, 'yaw': 0.0,\n 'width': 512, 'height': 512, 'fov': 5 * 10.0,\n 'id': 'topdown'\n },\t\n ]\n def tick(self, input_data):\n self.step += 1\n \n topdown = cv2.cvtColor(input_data['topdown'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_front = cv2.cvtColor(input_data['rgb_front'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_left = cv2.cvtColor(input_data['rgb_left'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_right = cv2.cvtColor(input_data['rgb_right'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n rgb_back = cv2.cvtColor(input_data['rgb_back'][1][:, :, :3], cv2.COLOR_BGR2RGB)\n\n gps = input_data['gps'][1][:2]\n speed = input_data['speed'][1]['speed']\n compass = input_data['imu'][1][-1]\n acceleration = input_data['imu'][1][:3]\n angular_velocity = input_data['imu'][1][3:6]\n if (math.isnan(compass) == True): #It can happen that the compass sends nan for a few frames\n compass = 0.0\n acceleration = np.zeros(3)\n angular_velocity = np.zeros(3)\n \n result = {\n 'rgb_front': rgb_front,\n 'rgb_left': rgb_left,\n 'rgb_right': rgb_right,\n 'rgb_back': rgb_back,\n 'gps': gps,\n 'speed': speed,\n 'theta': compass,\n \"acceleration\":acceleration,\n \"angular_velocity\":angular_velocity,\n \"topdown\": topdown,\n \"acceleration\":acceleration.tolist(),\n \"angular_velocity\":angular_velocity.tolist()\n }\n \n pos = self._get_position(result)\n self.gps_buffer.append(pos)\n pos = np.average(self.gps_buffer, axis=0)\n\n result[\"x\"] = pos[0]\n result[\"y\"] = pos[1]\n result['gps'] = pos\n next_wp, next_cmd = self._route_planner.run_step(pos)\n result['next_command'] = next_cmd.value\n result['x_target'] = next_wp[0]\n result['y_target'] = next_wp[1]\n\n ## Lidar is 10Hz and the simulator is running 20Hz -> Each frame the Lidar only returns 180 degree point clouds\n now_lidar = input_data['lidar'][1]\n if self.prev_lidar is not None:\n now_inv_mat = obtain_inv_transform_matrix(pos[1], -pos[0], compass-np.pi/2)\n relative_transform_mat = np.dot(now_inv_mat , np.array(self.prev_matrix)) #4 * 4\n transformed_prev_lidar_xyz = np.concatenate([self.prev_lidar[:, :3], np.ones((self.prev_lidar.shape[0], 1))], axis=1) # N * 4\n transformed_prev_lidar_xyz = np.einsum(\"ij,kj->ki\", relative_transform_mat, transformed_prev_lidar_xyz)\n transformed_prev_lidar_xyz = np.concatenate([transformed_prev_lidar_xyz[:, :3], self.prev_lidar[:, 3][:, np.newaxis]], axis=1)\n saved_lidar = np.concatenate([transformed_prev_lidar_xyz, now_lidar], axis=0).copy()\n saved_lidar[:, 2] += 2.5\n else:\n saved_lidar = now_lidar.copy()\n saved_lidar[:, 2] += 2.5\n\n\n result[\"lidar\"] = saved_lidar.astype(np.float32)\n self.prev_lidar = now_lidar\n self.prev_matrix = obtain_transform_matrix(pos[1], -pos[0], compass-np.pi/2)\n return result\n \n def offset_then_rotate(self, target_2d_world_coor, ref_2d_wolrd_coor, ref_yaw):\n final_coor = target_2d_world_coor - ref_2d_wolrd_coor\n R = np.array([\n [np.cos(ref_yaw), -np.sin(ref_yaw)],\n [np.sin(ref_yaw), np.cos(ref_yaw)]\n ])\n return np.einsum(\"ij,kj->ki\", R.T, final_coor)\n\n @torch.no_grad()\n def run_step(self, input_data, timestamp):\n if not self.initialized:\n self._init()\n tick_data = self.tick(input_data)\n ################# Preprocess ################\n results = {}\n ego_theta = tick_data[\"theta\"] if not np.isnan(tick_data[\"theta\"]) else 0\n ego_theta = ego_theta - np.pi/2\n results[\"input_theta\"] = ego_theta\n\n results[\"input_x\"] = tick_data[\"y\"]\n results[\"input_y\"] = -tick_data[\"x\"]\n ego_xy = np.stack([results[\"input_x\"], results[\"input_y\"]], axis=-1)\n results[\"speed\"] = tick_data[\"speed\"]\n results[\"can_bus\"] = np.zeros(18)\n results[\"can_bus\"][0] = results[\"input_x\"] #Gloabal\n results[\"can_bus\"][1] = results[\"input_y\"] #Global\n accel = np.array(tick_data[\"acceleration\"])\n accel[:2] = self.offset_then_rotate(np.array(accel[:2])[np.newaxis, :], np.array([0, 0]), ego_theta).squeeze(0)\n results[\"can_bus\"][7:10] = accel\n results[\"can_bus\"][10:13] = tick_data[\"angular_velocity\"]\n results[\"can_bus\"][13] = tick_data[\"speed\"]\n results[\"can_bus\"][-2] = ego_theta\n results[\"can_bus\"][-1] = ego_theta / np.pi * 180\n \n results['target_point'] = self.offset_then_rotate(np.array([[tick_data[\"y_target\"], -tick_data[\"x_target\"]]]), ego_xy, ego_theta).squeeze(0)\n\n command = tick_data['next_command']\n if command < 0:\n command = 4\n command -= 1\n results['target_command_raw'] = torch.tensor(command).long()\n assert command in [0, 1, 2, 3, 4, 5]\n cmd_one_hot = [0] * 6\n cmd_one_hot[command] = 1\n results['target_command'] = torch.tensor(cmd_one_hot)\n\n ## Inference mode, all empty\n results['waypoints'] = np.zeros(4)\n results[\"action\"] = np.zeros(3)\n results[\"action_mu\"] = np.zeros(2)\n results[\"action_sigma\"] = np.zeros(2)\n results['future_action_mu'] = np.zeros((self.pred_len, 2))\n results['future_action_sigma'] = np.zeros((self.pred_len, 2)) \n results['future_action'] = np.zeros((self.pred_len, 3)) \n results[\"value\"] = 0\n results[\"feature\"] = np.zeros(1)\n results[\"future_feature\"] = np.zeros((self.pred_len, 1))\n \n results[\"img\"] = [tick_data[camera_name] for camera_name in self.cfg[\"camera_list\"]]\n\n points = tick_data[\"lidar\"]\n points = self.points_class(\n points, points_dim=points.shape[-1], attribute_dims=None)\n results[\"points\"] = points\n results = self.test_pipeline(results)\n\n if len(self.data_queue) >= self.data_queue_len:\n self.data_queue.popleft()\n self.data_queue.append(results)\n\n if self.step < self.data_queue_len:\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n self.update_gps_buffer(control, tick_data['theta'], tick_data['speed'])\n return control\n \n ## Preprocess\n selected_index_lis = []\n for selected_index in self.cfg[\"cfg\"][\"history_query_index_lis\"][:-1]:\n selected_index = selected_index * 10 ## train in 2Hz while Simulator is in 20 Hz\n selected_index_lis.append(selected_index-1)\n selected_index_lis.append(-1)\n input_data_queue = [copy.deepcopy(self.data_queue[selected_index]) for selected_index in selected_index_lis]\n input_data_union = ds_module.union2one(self.seq_test_pipeline, input_data_queue)\n input_data_batch = mm_collate_to_batch_form([input_data_union], samples_per_gpu=1)\n input_data_batch[\"img\"] = input_data_batch[\"img\"].data[0]\n if len(selected_index_lis) == 1:\n input_data_batch[\"img\"] = input_data_batch[\"img\"].squeeze(0)\n input_data_batch[\"points\"] = input_data_batch[\"points\"].data[0]\n input_data_batch[\"img_metas\"] = input_data_batch[\"img_metas\"].data[0]\n for _ in input_data_batch:\n if torch.is_tensor(input_data_batch[_]):\n input_data_batch[_] = input_data_batch[_].to(self.device)\n\n with torch.no_grad():\n pred = self.model.forward_inference(input_data_batch)\n gt_velocity = torch.FloatTensor([tick_data['speed']]).to(self.device, dtype=torch.float32)\n steer_ctrl, throttle_ctrl, brake_ctrl, metadata = self.model.process_action(pred, tick_data['next_command'], gt_velocity, results['target_point'])\n \n steer_traj, throttle_traj, brake_traj, metadata_traj = self.model.control_pid(pred['pred_wp'][:, -1, :, :], gt_velocity, results['target_point'])\n \n if brake_traj < 0.05: brake_traj = 0.0\n if throttle_traj > brake_traj: brake_traj = 0.0\n\n overall_pred_is_accel = ((throttle_traj>0) or (throttle_ctrl>0) or (brake_traj<0.3) or (brake_ctrl<0.3))\n overall_pred_is_brake = ((brake_traj>0.1) or (brake_ctrl>0.1))\n\n control = carla.VehicleControl()\n control.steer = steer_ctrl\n is_turn = False\n if abs(control.steer) > 0.07: ## In turning\n is_turn = True\n speed_threshold = 2.5 ## Reduce stuck during turning\n else:\n speed_threshold = 3.0 ## Recude red light infraction/collision\n\n if overall_pred_is_brake:\n control.brake=1.0\n control.throttle=0.0\n else:\n control.brake=0.0\n control.throttle=0.75\n\n is_stuck = False\n # By transfuser, crawl\n if(self.stuck_detector > self.stuck_threshold):\n print(\"Detected agent being stuck.\", \"Frame:\", self.step // 10)\n is_stuck = True\n if overall_pred_is_accel:\n control.brake = 0.0\n control.throttle = 0.75\n else:\n control.brake = 1.0\n control.throttle = 0.0\n \n if(float(gt_velocity) < 0.5): # just an arbitrary low number to threshhold when the car is stopped\n self.stuck_detector += 1\n elif(float(gt_velocity) > 0.5):\n self.stuck_detector = 0\n \n if float(tick_data['speed']) > speed_threshold:\n max_throttle = 0.05\n else:\n max_throttle = 0.75\n control.throttle = np.clip(control.throttle, a_min=0.0, a_max=max_throttle)\n\n self.pid_metadata = {}\n self.pid_metadata['steer_ctrl'] = float(steer_ctrl)\n self.pid_metadata['steer_traj'] = float(steer_traj)\n self.pid_metadata['throttle_ctrl'] = float(throttle_ctrl)\n self.pid_metadata['throttle_traj'] = float(throttle_traj)\n self.pid_metadata['brake_ctrl'] = float(brake_ctrl)\n self.pid_metadata['brake_traj'] = float(brake_traj)\n self.pid_metadata[\"is_stuck\"] = is_stuck\n self.pid_metadata[\"stuck_detector\"] = self.stuck_detector\n self.pid_metadata['steer'] = control.steer\n self.pid_metadata[\"brake\"] = control.brake\n self.pid_metadata[\"throttle\"] = control.throttle\n self.pid_metadata[\"speed\"] = float(tick_data['speed'])\n\n if SAVE_PATH is not None and self.step % 10 == 0:\n self.save(tick_data)\n \n self.update_gps_buffer(control, tick_data['theta'], tick_data['speed']) \n return control\n\n def save(self, tick_data):\n frame = self.step // 10\n Image.fromarray(tick_data['rgb_front']).save(self.save_path / 'rgb_front' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_left']).save(self.save_path / 'rgb_left' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_right']).save(self.save_path / 'rgb_right' / ('%04d.png' % frame))\n #Image.fromarray(tick_data['rgb_back']).save(self.save_path / 'rgb_back' / ('%04d.png' % frame))\n Image.fromarray(tick_data['topdown']).save(self.save_path / 'topdown' / ('%04d.png' % frame))\n outfile = open(self.save_path / 'meta' / ('%04d.json' % frame), 'w')\n json.dump(self.pid_metadata, outfile, indent=4)\n outfile.close()\n #np.save(self.save_path / 'lidar' / ('%04d.npy' % frame), tick_data[\"lidar\"].astype(np.float32), allow_pickle=True)\n\n def destroy(self):\n del self.model\n torch.cuda.empty_cache()\n\n def update_gps_buffer(self, control, theta, speed):\n yaw = np.array([(theta - np.pi/2.0)])\n speed = np.array([speed])\n action = np.array(np.stack([control.steer, control.throttle, control.brake], axis=-1))\n\n #Update gps locations\n for i in range(len(self.gps_buffer)):\n loc =self.gps_buffer[i]\n loc_temp = np.array([loc[1], -loc[0]]) #Bicycle model uses a different coordinate system\n next_loc_tmp, _, _ = self.ego_model.forward(loc_temp, yaw, speed, action)\n next_loc = np.array([-next_loc_tmp[1], next_loc_tmp[0]])\n self.gps_buffer[i] = next_loc\n return None" }, { "path": "open_loop_training/__init__.py", "content": "" }, { "path": "open_loop_training/code/__init__.py", "content": "from .datasets.pipelines import *\nfrom .datasets import CarlaDataset\nfrom .model_code.backbones import *\nfrom .model_code.dense_heads import *\nfrom .encoder_decoder_framework import *\n\n" }, { "path": "open_loop_training/code/apis/__init__.py", "content": "from .train import custom_train_model\nfrom .mmdet_train import custom_train_detector\n__all__ = ['custom_train_model', 'custom_train_detector']\n" }, { "path": "open_loop_training/code/apis/mmdet_train.py", "content": "# ---------------------------------------------\n# Copyright (c) OpenMMLab. All rights reserved.\n# ---------------------------------------------\n# Modified by Zhiqi Li\n# ---------------------------------------------\nimport random\nimport warnings\n\nimport numpy as np\nimport torch\nimport torch.distributed as dist\nfrom mmcv.parallel import MMDataParallel, MMDistributedDataParallel\nfrom mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,\n Fp16OptimizerHook, OptimizerHook, build_optimizer,\n build_runner, get_dist_info)\nfrom mmcv.utils import build_from_cfg\n\nfrom mmdet.core import EvalHook\n\nfrom mmdet.datasets import (build_dataset,\n replace_ImageToTensor)\nfrom mmdet.utils import get_root_logger\nimport time\nimport os.path as osp\nfrom ..datasets.builder import build_dataloader\nfrom ..core.evaluation.eval_hooks import CustomDistEvalHook, CustomEvalHook\nfrom ..datasets import custom_build_dataset\ndef custom_train_detector(model,\n dataset,\n cfg,\n distributed=False,\n validate=False,\n timestamp=None,\n eval_model=None,\n meta=None):\n logger = get_root_logger(cfg.log_level)\n\n # prepare data loaders\n \n dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]\n #assert len(dataset)==1s\n if 'imgs_per_gpu' in cfg.data:\n logger.warning('\"imgs_per_gpu\" is deprecated in MMDet V2.0. '\n 'Please use \"samples_per_gpu\" instead')\n if 'samples_per_gpu' in cfg.data:\n logger.warning(\n f'Got \"imgs_per_gpu\"={cfg.data.imgs_per_gpu} and '\n f'\"samples_per_gpu\"={cfg.data.samples_per_gpu}, \"imgs_per_gpu\"'\n f'={cfg.data.imgs_per_gpu} is used in this experiments')\n else:\n logger.warning(\n 'Automatically set \"samples_per_gpu\"=\"imgs_per_gpu\"='\n f'{cfg.data.imgs_per_gpu} in this experiments')\n cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu\n\n data_loaders = [\n build_dataloader(\n ds,\n cfg.data.samples_per_gpu,\n cfg.data.workers_per_gpu,\n # cfg.gpus will be ignored if distributed\n len(cfg.gpu_ids),\n dist=distributed,\n seed=cfg.seed,\n shuffler_sampler=cfg.data.shuffler_sampler, # dict(type='DistributedGroupSampler'),\n nonshuffler_sampler=cfg.data.nonshuffler_sampler, # dict(type='DistributedSampler'),\n ) for ds in dataset\n ]\n\n # put model on gpus\n if distributed:\n find_unused_parameters = True#cfg.get('find_unused_parameters', False)\n # Sets the `find_unused_parameters` parameter in\n # torch.nn.parallel.DistributedDataParallel\n model = MMDistributedDataParallel(\n model.cuda(),\n device_ids=[torch.cuda.current_device()],\n broadcast_buffers=False,\n find_unused_parameters=find_unused_parameters)\n if eval_model is not None:\n eval_model = MMDistributedDataParallel(\n eval_model.cuda(),\n device_ids=[torch.cuda.current_device()],\n broadcast_buffers=False,\n find_unused_parameters=find_unused_parameters)\n if hasattr(cfg, 'SyncBN') and cfg.SyncBN:\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)\n else:\n model = MMDataParallel(\n model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)\n if eval_model is not None:\n eval_model = MMDataParallel(\n eval_model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)\n\n\n # build runner\n optimizer = build_optimizer(model, cfg.optimizer)\n\n if 'runner' not in cfg:\n cfg.runner = {\n 'type': 'EpochBasedRunner',\n 'max_epochs': cfg.total_epochs\n }\n warnings.warn(\n 'config is now expected to have a `runner` section, '\n 'please set `runner` in your config.', UserWarning)\n else:\n if 'total_epochs' in cfg:\n assert cfg.total_epochs == cfg.runner.max_epochs\n if eval_model is not None:\n runner = build_runner(\n cfg.runner,\n default_args=dict(\n model=model,\n eval_model=eval_model,\n optimizer=optimizer,\n work_dir=cfg.work_dir,\n logger=logger,\n meta=meta))\n else:\n runner = build_runner(\n cfg.runner,\n default_args=dict(\n model=model,\n optimizer=optimizer,\n work_dir=cfg.work_dir,\n logger=logger,\n meta=meta))\n\n # an ugly workaround to make .log and .log.json filenames the same\n runner.timestamp = timestamp\n\n # fp16 setting\n fp16_cfg = cfg.get('fp16', None)\n if fp16_cfg is not None:\n optimizer_config = Fp16OptimizerHook(\n **cfg.optimizer_config, **fp16_cfg, distributed=distributed)\n elif distributed and 'type' not in cfg.optimizer_config:\n optimizer_config = OptimizerHook(**cfg.optimizer_config)\n else:\n optimizer_config = cfg.optimizer_config\n\n # register hooks\n runner.register_training_hooks(cfg.lr_config, optimizer_config,\n cfg.checkpoint_config, cfg.log_config,\n cfg.get('momentum_config', None))\n \n # register profiler hook\n #trace_config = dict(type='tb_trace', dir_name='work_dir')\n #profiler_config = dict(on_trace_ready=trace_config)\n #runner.register_profiler_hook(profiler_config)\n \n if distributed:\n if isinstance(runner, EpochBasedRunner):\n runner.register_hook(DistSamplerSeedHook())\n\n # register eval hooks\n if validate:\n # Support batch_size > 1 in validation\n # val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)\n # if val_samples_per_gpu > 1:\n # assert False\n # # Replace 'ImageToTensor' to 'DefaultFormatBundle'\n # cfg.data.val.pipeline = replace_ImageToTensor(\n # cfg.data.val.pipeline)\n val_dataset = custom_build_dataset(cfg.data.val, dict(test_mode=True))\n\n val_dataloader = build_dataloader(\n val_dataset,\n samples_per_gpu=cfg.data.samples_per_gpu,\n workers_per_gpu=cfg.data.workers_per_gpu,\n dist=distributed,\n shuffle=False,\n shuffler_sampler=cfg.data.shuffler_sampler, # dict(type='DistributedGroupSampler'),\n nonshuffler_sampler=cfg.data.nonshuffler_sampler, # dict(type='DistributedSampler'),\n )\n eval_cfg = cfg.get('evaluation', {})\n eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'\n eval_cfg['jsonfile_prefix'] = osp.join('val', cfg.work_dir, time.ctime().replace(' ','_').replace(':','_'))\n eval_hook = CustomDistEvalHook if distributed else CustomEvalHook\n runner.register_hook(eval_hook(val_dataloader, **eval_cfg))\n\n # user-defined hooks\n if cfg.get('custom_hooks', None):\n custom_hooks = cfg.custom_hooks\n assert isinstance(custom_hooks, list), \\\n f'custom_hooks expect list type, but got {type(custom_hooks)}'\n for hook_cfg in cfg.custom_hooks:\n assert isinstance(hook_cfg, dict), \\\n 'Each item in custom_hooks expects dict type, but got ' \\\n f'{type(hook_cfg)}'\n hook_cfg = hook_cfg.copy()\n priority = hook_cfg.pop('priority', 'NORMAL')\n hook = build_from_cfg(hook_cfg, HOOKS)\n runner.register_hook(hook, priority=priority)\n\n if cfg.resume_from:\n runner.resume(cfg.resume_from)\n elif cfg.load_from:\n runner.load_checkpoint(cfg.load_from)\n runner.run(data_loaders, cfg.workflow)\n\n" }, { "path": "open_loop_training/code/apis/train.py", "content": "# ---------------------------------------------\n# Copyright (c) OpenMMLab. All rights reserved.\n# ---------------------------------------------\n# Modified by Zhiqi Li\n# ---------------------------------------------\n\nfrom .mmdet_train import custom_train_detector\nfrom mmseg.apis import train_segmentor\nfrom mmdet.apis import train_detector\n\ndef custom_train_model(model,\n dataset,\n cfg,\n distributed=False,\n validate=False,\n timestamp=None,\n eval_model=None,\n meta=None):\n \"\"\"A function wrapper for launching model training according to cfg.\n\n Because we need different eval_hook in runner. Should be deprecated in the\n future.\n \"\"\"\n if cfg.model.type in ['EncoderDecoder3D']:\n assert False\n else:\n custom_train_detector(\n model,\n dataset,\n cfg,\n distributed=distributed,\n validate=validate,\n timestamp=timestamp,\n eval_model=eval_model,\n meta=meta)\n\n\ndef train_model(model,\n dataset,\n cfg,\n distributed=False,\n validate=False,\n timestamp=None,\n meta=None):\n \"\"\"A function wrapper for launching model training according to cfg.\n\n Because we need different eval_hook in runner. Should be deprecated in the\n future.\n \"\"\"\n if cfg.model.type in ['EncoderDecoder3D']:\n train_segmentor(\n model,\n dataset,\n cfg,\n distributed=distributed,\n validate=validate,\n timestamp=timestamp,\n meta=meta)\n else:\n train_detector(\n model,\n dataset,\n cfg,\n distributed=distributed,\n validate=validate,\n timestamp=timestamp,\n meta=meta)\n" }, { "path": "open_loop_training/code/core/evaluation/__init__.py", "content": "from .eval_hooks import CustomDistEvalHook, CustomEvalHook\nfrom .epoch_hook import MySetEpochInfoHook" }, { "path": "open_loop_training/code/core/evaluation/epoch_hook.py", "content": "from mmcv.parallel import is_module_wrapper\nfrom mmcv.runner import HOOKS, Hook\n\n\n@HOOKS.register_module()\nclass MySetEpochInfoHook(Hook):\n \"\"\"Set runner's epoch information to the model.\"\"\"\n def before_train_epoch(self, runner):\n epoch = runner.epoch\n model = runner.model\n if is_module_wrapper(model):\n model = model.module\n model.set_epoch(epoch)" }, { "path": "open_loop_training/code/core/evaluation/eval_hooks.py", "content": "\n# Note: Considering that MMCV's EvalHook updated its interface in V1.3.16,\n# in order to avoid strong version dependency, we did not directly\n# inherit EvalHook but BaseDistEvalHook.\n\nimport bisect\nimport os.path as osp\nimport re\n\nimport mmcv\nimport torch.distributed as dist\nfrom mmcv.runner import DistEvalHook as BaseDistEvalHook\nfrom mmcv.runner import EvalHook as BaseEvalHook\nfrom torch.nn.modules.batchnorm import _BatchNorm\nfrom mmdet.core.evaluation.eval_hooks import DistEvalHook, EvalHook\nimport torch\n\ndef _calc_dynamic_intervals(start_interval, dynamic_interval_list):\n assert mmcv.is_list_of(dynamic_interval_list, tuple)\n\n dynamic_milestones = [0]\n dynamic_milestones.extend(\n [dynamic_interval[0] for dynamic_interval in dynamic_interval_list])\n dynamic_intervals = [start_interval]\n dynamic_intervals.extend(\n [dynamic_interval[1] for dynamic_interval in dynamic_interval_list])\n return dynamic_milestones, dynamic_intervals\n\n\nclass CustomEvalHook(BaseEvalHook):\n def __init__(self, *args, dynamic_intervals=None, **kwargs):\n super(CustomEvalHook, self).__init__(*args, **kwargs)\n self.use_dynamic_intervals = dynamic_intervals is not None\n\n if self.use_dynamic_intervals:\n self.dynamic_milestones, self.dynamic_intervals = \\\n _calc_dynamic_intervals(self.interval, dynamic_intervals)\n def _decide_interval(self, runner):\n if self.use_dynamic_intervals:\n progress = runner.epoch if self.by_epoch else runner.iter\n step = bisect.bisect(self.dynamic_milestones, (progress + 1))\n # Dynamically modify the evaluation interval\n self.interval = self.dynamic_intervals[step - 1]\n\n def before_train_epoch(self, runner):\n \"\"\"Evaluate the model only at the start of training by epoch.\"\"\"\n self._decide_interval(runner)\n super().before_train_epoch(runner)\n\n def before_train_iter(self, runner):\n self._decide_interval(runner)\n super().before_train_iter(runner)\n\n def _do_evaluate(self, runner):\n \"\"\"perform evaluation and save ckpt.\"\"\"\n # Synchronization of BatchNorm's buffer (running_mean\n # and running_var) is not supported in the DDP of pytorch,\n # which may cause the inconsistent performance of models in\n # different ranks, so we broadcast BatchNorm's buffers\n # of rank 0 to other ranks to avoid this.\n if not self._should_evaluate(runner):\n return\n results = {}\n calculated_num = 0.0\n dataset = self.dataloader.dataset\n prog_bar = mmcv.ProgressBar(len(dataset))\n runner.model.eval()\n for i, data in enumerate(self.dataloader):\n with torch.no_grad():\n result = runner.model(return_loss=False, **data)\n batch_size = result[\"num_samples\"]\n for key in result[\"log_vars\"]:\n if key not in results:\n results[key] = 0.0\n results[key] += result[\"log_vars\"][key] * batch_size\n calculated_num += batch_size\n\n for key in results.keys():\n results[key] /= calculated_num\n print('\\n')\n runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)\n for name, val in results.items():\n runner.log_buffer.output[name] = val\n runner.log_buffer.ready = True\n\n if self.save_best:\n self._save_ckpt(runner, results)\n\n\n\nclass CustomDistEvalHook(BaseDistEvalHook):\n\n def __init__(self, *args, dynamic_intervals=None, **kwargs):\n super(CustomDistEvalHook, self).__init__(*args, **kwargs)\n self.use_dynamic_intervals = dynamic_intervals is not None\n if self.use_dynamic_intervals:\n self.dynamic_milestones, self.dynamic_intervals = \\\n _calc_dynamic_intervals(self.interval, dynamic_intervals)\n\n def _decide_interval(self, runner):\n if self.use_dynamic_intervals:\n progress = runner.epoch if self.by_epoch else runner.iter\n step = bisect.bisect(self.dynamic_milestones, (progress + 1))\n # Dynamically modify the evaluation interval\n self.interval = self.dynamic_intervals[step - 1]\n\n def before_train_epoch(self, runner):\n \"\"\"Evaluate the model only at the start of training by epoch.\"\"\"\n self._decide_interval(runner)\n super().before_train_epoch(runner)\n\n def before_train_iter(self, runner):\n self._decide_interval(runner)\n super().before_train_iter(runner)\n\n def _do_evaluate(self, runner):\n \"\"\"perform evaluation and save ckpt.\"\"\"\n # Synchronization of BatchNorm's buffer (running_mean\n # and running_var) is not supported in the DDP of pytorch,\n # which may cause the inconsistent performance of models in\n # different ranks, so we broadcast BatchNorm's buffers\n # of rank 0 to other ranks to avoid this.\n if self.broadcast_bn_buffer:\n model = runner.model\n for name, module in model.named_modules():\n if isinstance(module,\n _BatchNorm) and module.track_running_stats:\n dist.broadcast(module.running_var, 0)\n dist.broadcast(module.running_mean, 0)\n\n if not self._should_evaluate(runner):\n return\n\n tmpdir = self.tmpdir\n if tmpdir is None:\n tmpdir = osp.join(runner.work_dir, '.eval_hook')\n from .eval_tool import custom_multi_gpu_test\n with torch.no_grad():\n results = custom_multi_gpu_test(\n runner.model,\n self.dataloader,\n tmpdir=tmpdir,\n gpu_collect=self.gpu_collect)\n if runner.rank == 0:\n print('\\n')\n runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)\n for name, val in results.items():\n runner.log_buffer.output[name] = val\n runner.log_buffer.ready = True\n\n if self.save_best:\n self._save_ckpt(runner, results)\n \n" }, { "path": "open_loop_training/code/core/evaluation/eval_tool.py", "content": "# ---------------------------------------------\r\n# Copyright (c) OpenMMLab. All rights reserved.\r\n# ---------------------------------------------\r\n# Modified by Zhiqi Li\r\n# ---------------------------------------------\r\nimport os.path as osp\r\nimport pickle\r\nimport shutil\r\nimport tempfile\r\nimport time\r\n\r\nimport mmcv\r\nimport torch\r\nimport torch.distributed as dist\r\nfrom mmcv.image import tensor2imgs\r\nfrom mmcv.runner import get_dist_info\r\n\r\nfrom mmdet.core import encode_mask_results\r\n\r\n\r\nimport mmcv\r\nimport numpy as np\r\nimport pycocotools.mask as mask_util\r\n\r\n# ---------------------------------------------\r\n# Copyright (c) OpenMMLab. All rights reserved.\r\n# ---------------------------------------------\r\n# Modified by Zhiqi Li\r\n# Modified then by Xiaosong Jia \r\n# ---------------------------------------------\r\nimport os.path as osp\r\nimport pickle\r\nimport shutil\r\nimport tempfile\r\nimport time\r\nfrom unittest import result\r\n\r\nimport mmcv\r\nimport torch\r\nimport torch.distributed as dist\r\nfrom mmcv.image import tensor2imgs\r\nfrom mmcv.runner import get_dist_info\r\n\r\nfrom mmdet.core import encode_mask_results\r\n\r\n\r\nimport mmcv\r\nimport numpy as np\r\nimport pycocotools.mask as mask_util\r\n\r\ndef custom_encode_mask_results(mask_results):\r\n \"\"\"Encode bitmap mask to RLE code. Semantic Masks only\r\n Args:\r\n mask_results (list | tuple[list]): bitmap mask results.\r\n In mask scoring rcnn, mask_results is a tuple of (segm_results,\r\n segm_cls_score).\r\n Returns:\r\n list | tuple: RLE encoded mask.\r\n \"\"\"\r\n cls_segms = mask_results\r\n num_classes = len(cls_segms)\r\n encoded_mask_results = []\r\n for i in range(len(cls_segms)):\r\n encoded_mask_results.append(\r\n mask_util.encode(\r\n np.array(\r\n cls_segms[i][:, :, np.newaxis], order='F',\r\n dtype='uint8'))[0]) # encoded with RLE\r\n return [encoded_mask_results]\r\n\r\ndef custom_multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):\r\n \"\"\"Test model with multiple gpus.\r\n This method tests model with multiple gpus and collects the results\r\n under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'\r\n it encodes results to gpu tensors and use gpu communication for results\r\n collection. On cpu mode it saves the results on different gpus to 'tmpdir'\r\n and collects them by the rank 0 worker.\r\n Args:\r\n model (nn.Module): Model to be tested.\r\n data_loader (nn.Dataloader): Pytorch data loader.\r\n tmpdir (str): Path of directory to save the temporary results from\r\n different gpus under cpu mode.\r\n gpu_collect (bool): Option to use either gpu or cpu to collect results.\r\n Returns:\r\n list: The prediction results.\r\n \"\"\"\r\n model.eval()\r\n\r\n results = {}\r\n calculated_num = 0.0\r\n dataset = data_loader.dataset\r\n rank, world_size = get_dist_info()\r\n # if rank == 0:\r\n # prog_bar = mmcv.ProgressBar(len(dataset))\r\n time.sleep(2) # This line can prevent deadlock problem in some cases.\r\n for i, data in enumerate(data_loader):\r\n with torch.no_grad():\r\n result = model(return_loss=False, **data)\r\n batch_size = result[\"num_samples\"]\r\n for key in result[\"log_vars\"]:\r\n if key not in results:\r\n results[key] = 0.0\r\n results[key] += result[\"log_vars\"][key] * batch_size\r\n calculated_num += batch_size\r\n # if rank == 0:\r\n # for _ in range(batch_size * world_size):\r\n # prog_bar.update()\r\n for key in results.keys():\r\n results[key] = (results[key]/calculated_num, calculated_num)\r\n # collect results from all ranks\r\n if gpu_collect:\r\n results = collect_results_gpu(results, len(dataset))\r\n else:\r\n results = collect_results_cpu(results, len(dataset), tmpdir)\r\n tmpdir = tmpdir+'_mask' if tmpdir is not None else None\r\n return results\r\n\r\n\r\ndef collect_results_cpu(result_part, size, tmpdir=None):\r\n rank, world_size = get_dist_info()\r\n # create a tmp dir if it is not specified\r\n if tmpdir is None:\r\n MAX_LEN = 512\r\n # 32 is whitespace\r\n dir_tensor = torch.full((MAX_LEN, ),\r\n 32,\r\n dtype=torch.uint8,\r\n device='cuda')\r\n if rank == 0:\r\n mmcv.mkdir_or_exist('.dist_test')\r\n tmpdir = tempfile.mkdtemp(dir='.dist_test')\r\n tmpdir = torch.tensor(\r\n bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')\r\n dir_tensor[:len(tmpdir)] = tmpdir\r\n dist.broadcast(dir_tensor, 0)\r\n tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()\r\n else:\r\n mmcv.mkdir_or_exist(tmpdir)\r\n # dump the part result to the dir\r\n mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))\r\n dist.barrier()\r\n # collect all parts\r\n if rank != 0:\r\n return None\r\n else:\r\n # load results of all parts from tmp dir\r\n results = {}\r\n results_cnts = {}\r\n num_sample = 0\r\n for i in range(world_size):\r\n part_file = osp.join(tmpdir, f'part_{i}.pkl')\r\n part_file = mmcv.load(part_file)\r\n for key in part_file:\r\n if key not in results:\r\n results[key] = 0.0\r\n results_cnts[key] = 0.0\r\n results[key] += part_file[key][0] * part_file[key][1]\r\n results_cnts[key] += part_file[key][1]\r\n for key in results:\r\n results[key] /= results_cnts[key]\r\n shutil.rmtree(tmpdir)\r\n return results\r\n\r\n\r\ndef collect_results_gpu(result_part, size):\r\n collect_results_cpu(result_part, size)" }, { "path": "open_loop_training/code/datasets/__init__.py", "content": "from .carla_dataset import CarlaDataset\nfrom .builder import custom_build_dataset\n\n__all__ = [\n \"CarlaDataset\"\n]\n" }, { "path": "open_loop_training/code/datasets/base_dataset.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport warnings\nimport os.path as osp\nfrom abc import ABCMeta, abstractmethod\nfrom os import PathLike\nfrom typing import List\n\nimport mmcv\nimport numpy as np\nfrom torch.utils.data import Dataset\n\n\nfrom mmdet3d.datasets.pipelines import Compose\n\ndef expanduser(path):\n if isinstance(path, (str, PathLike)):\n return osp.expanduser(path)\n else:\n return path\n\n\nclass BaseDataset(Dataset):\n \"\"\"Base dataset.\n\n Args:\n data_prefix (str): the prefix of data path\n pipeline (list): a list of dict, where each element represents\n a operation defined in `mmcls.datasets.pipelines`\n ann_file (str | None): the annotation file. When ann_file is str,\n the subclass is expected to read from the ann_file. When ann_file\n is None, the subclass is expected to read according to data_prefix\n test_mode (bool): in train mode or test mode\n \"\"\"\n\n\n def __init__(self,\n ann_file,\n pipeline,\n data_prefix='',\n test_mode=False):\n super(BaseDataset, self).__init__()\n self.data_prefix = expanduser(data_prefix)\n self.pipeline = Compose(pipeline)\n self.ann_file = expanduser(ann_file)\n self.test_mode = test_mode\n self.data_infos = []\n\n @abstractmethod\n def load_annotations(self):\n return mmcv.load(self.ann_file)\n\n\n def pre_pipeline(self, results):\n \"\"\"Prepare results dict for pipeline.\"\"\"\n results['img_prefix'] = self.img_prefix\n\n\n\n def __len__(self):\n return len(self.data_infos)\n\n def __getitem__(self, idx):\n results = copy.deepcopy(self.data_infos[idx])\n self.pre_pipeline(results)\n return self.pipeline(results)\n\n\n def evaluate(self,\n results,\n metric='accuracy',\n metric_options=None,\n jsonfile_prefix=None,\n indices=None,\n logger=None):\n pass\n\n" }, { "path": "open_loop_training/code/datasets/builder.py", "content": "\n# Copyright (c) OpenMMLab. All rights reserved.\nimport copy\nimport platform\nimport random\nfrom functools import partial\n\nimport numpy as np\nfrom mmcv.parallel import collate\nfrom mmcv.runner import get_dist_info\nfrom mmcv.utils import Registry, build_from_cfg\nfrom torch.utils.data import DataLoader\n\nfrom mmdet.datasets.samplers import GroupSampler\nfrom ..datasets.samplers.group_sampler import DistributedGroupSampler\nfrom ..datasets.samplers.distributed_sampler import DistributedSampler\nfrom ..datasets.samplers.sampler import build_sampler\n\ndef build_dataloader(dataset,\n samples_per_gpu,\n workers_per_gpu,\n num_gpus=1,\n dist=True,\n shuffle=True,\n seed=None,\n shuffler_sampler=None,\n nonshuffler_sampler=None,\n **kwargs):\n \"\"\"Build PyTorch DataLoader.\n In distributed training, each GPU/process has a dataloader.\n In non-distributed training, there is only one dataloader for all GPUs.\n Args:\n dataset (Dataset): A PyTorch dataset.\n samples_per_gpu (int): Number of training samples on each GPU, i.e.,\n batch size of each GPU.\n workers_per_gpu (int): How many subprocesses to use for data loading\n for each GPU.\n num_gpus (int): Number of GPUs. Only used in non-distributed training.\n dist (bool): Distributed training/test or not. Default: True.\n shuffle (bool): Whether to shuffle the data at every epoch.\n Default: True.\n kwargs: any keyword argument to be used to initialize DataLoader\n Returns:\n DataLoader: A PyTorch dataloader.\n \"\"\"\n rank, world_size = get_dist_info()\n if dist:\n # DistributedGroupSampler will definitely shuffle the data to satisfy\n # that images on each GPU are in the same group\n if shuffle:\n sampler = build_sampler(shuffler_sampler if shuffler_sampler is not None else dict(type='DistributedGroupSampler'),\n dict(\n dataset=dataset,\n samples_per_gpu=samples_per_gpu,\n num_replicas=world_size,\n rank=rank,\n seed=seed)\n )\n\n else:\n sampler = build_sampler(nonshuffler_sampler if nonshuffler_sampler is not None else dict(type='DistributedSampler'),\n dict(\n dataset=dataset,\n num_replicas=world_size,\n rank=rank,\n shuffle=shuffle,\n seed=seed)\n )\n\n batch_size = samples_per_gpu\n num_workers = workers_per_gpu\n else:\n # assert False, 'not support in bevformer'\n print('WARNING!!!!, Only can be used for obtain inference speed!!!!')\n sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None\n batch_size = num_gpus * samples_per_gpu\n num_workers = num_gpus * workers_per_gpu\n\n init_fn = partial(\n worker_init_fn, num_workers=num_workers, rank=rank,\n seed=seed) if seed is not None else None\n\n data_loader = DataLoader(\n dataset,\n batch_size=batch_size,\n sampler=sampler,\n num_workers=num_workers,\n collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),\n pin_memory=False,\n worker_init_fn=init_fn,\n drop_last = True,\n **kwargs)\n\n return data_loader\n\n\ndef worker_init_fn(worker_id, num_workers, rank, seed):\n # The seed of each worker equals to\n # num_worker * rank + worker_id + user_seed\n worker_seed = num_workers * rank + worker_id + seed\n np.random.seed(worker_seed)\n random.seed(worker_seed)\n\n\n# Copyright (c) OpenMMLab. All rights reserved.\nimport platform\nfrom mmcv.utils import Registry, build_from_cfg\n\nfrom mmdet.datasets import DATASETS\nfrom mmdet.datasets.builder import _concat_dataset\n\nif platform.system() != 'Windows':\n # https://github.com/pytorch/pytorch/issues/973\n import resource\n rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)\n base_soft_limit = rlimit[0]\n hard_limit = rlimit[1]\n soft_limit = min(max(4096, base_soft_limit), hard_limit)\n resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))\n\nOBJECTSAMPLERS = Registry('Object sampler')\n\n\ndef custom_build_dataset(cfg, default_args=None):\n from mmdet3d.datasets.dataset_wrappers import CBGSDataset\n from mmdet.datasets.dataset_wrappers import (ClassBalancedDataset,\n ConcatDataset, RepeatDataset)\n if isinstance(cfg, (list, tuple)):\n dataset = ConcatDataset([custom_build_dataset(c, default_args) for c in cfg])\n elif cfg['type'] == 'ConcatDataset':\n dataset = ConcatDataset(\n [custom_build_dataset(c, default_args) for c in cfg['datasets']],\n cfg.get('separate_eval', True))\n elif cfg['type'] == 'RepeatDataset':\n dataset = RepeatDataset(\n custom_build_dataset(cfg['dataset'], default_args), cfg['times'])\n elif cfg['type'] == 'ClassBalancedDataset':\n dataset = ClassBalancedDataset(\n custom_build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])\n elif cfg['type'] == 'CBGSDataset':\n dataset = CBGSDataset(custom_build_dataset(cfg['dataset'], default_args))\n elif isinstance(cfg.get('ann_file'), (list, tuple)):\n dataset = _concat_dataset(cfg, default_args)\n else:\n dataset = build_from_cfg(cfg, DATASETS, default_args)\n\n return dataset\n" }, { "path": "open_loop_training/code/datasets/carla_dataset.py", "content": "import os\nfrom unittest import result\nimport cv2\nimport copy\nimport mmcv\nimport torch\nimport random\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nfrom torchvision import transforms as T\n\nfrom nuscenes.eval.common.utils import Quaternion\nimport pickle\nfrom mmcv.parallel import DataContainer as DC\nfrom mmdet.datasets import DATASETS, CustomDataset\nfrom mmdet3d.datasets.pipelines import Compose\n\nfrom .base_dataset import BaseDataset\nimport json\nimport math\nimport io\n\n@DATASETS.register_module()\nclass CarlaDataset(BaseDataset):\n r\"\"\"Carla Dataset.\n \"\"\"\n def __init__(self, \n cfg,\n used_town,\n pipeline, \n is_local,\n full_queue_pipeline,\n test_mode=False,\n **kwargs):\n super(CarlaDataset).__init__()\n self.test_mode = test_mode\n self.pred_len = cfg[\"pred_len\"]\n ## Pipeline for single frame\n self.pipeline = Compose(pipeline)\n ## Pipeline for a sequence of history frames\n self.full_queue_pipeline = Compose(full_queue_pipeline)\n self.resize_to21x21 = T.Resize(size=(21, 21))\n self.is_local = bool(cfg['is_local']) ## Ignore this, we train out model on a cluster with ceph\n self.is_full = False ## 189K or 2M dataset\n if \"is_full\" in cfg:\n self.is_full = bool(cfg[\"is_full\"]) ## 2M frames dataset setting\n dataset_size_fname = \"dataset_metadata.pkl\"\n\n with open(\"../dataset/\"+dataset_size_fname, \"rb\") as f:\n route_length_dict = pickle.load(f)\n\n self.history_query_index_lis = cfg[\"history_query_index_lis\"]\n route_start_index = -self.history_query_index_lis[0]\n self.data_infos = [] #tuple: (route_folder, current_timestep)\n \n ### Prepare for dataloader to shuffle data\n town_sampel_cnt_dict = {}\n for single_used_town in used_town:\n if single_used_town not in route_length_dict: ## No data collected\n print(single_used_town, \"Not Found\")\n continue\n now_town = single_used_town[:6] ## town_name without index\n if now_town not in town_sampel_cnt_dict:\n town_sampel_cnt_dict[now_town] = 0\n is_val_town = (\"02\" in now_town or \"05\" in now_town)\n ##When we only use the 189K dataset\n if (not self.is_full or is_val_town) and town_sampel_cnt_dict[now_town] > cfg[\"max_sample_per_town\"][now_town]:\n continue\n for route in route_length_dict[single_used_town]:\n ##When we only use the 189K dataset\n if (not self.is_full or is_val_town) and town_sampel_cnt_dict[now_town] > cfg[\"max_sample_per_town\"][now_town]:\n break\n ##Not use the first few frames\n if route_length_dict[single_used_town][route] < route_start_index+1+self.pred_len:\n continue\n if self.is_local:\n route_path = route[:3] + \"dataset/\"+route[3:]\n else:\n route_path = route\n new_data_info = [(route_path, current_time_step) for current_time_step in range(route_start_index, route_length_dict[single_used_town][route]-self.pred_len)]\n self.data_infos += new_data_info\n town_sampel_cnt_dict[now_town] += len(new_data_info)\n print(town_sampel_cnt_dict)\n if not self.is_local: ## ignore this line (we train our model on a cluster with ceph)\n ceph_conf = '~/petreloss.conf'\n from petrel_client.client import Client\n self.client = Client(ceph_conf)\n self.flag = np.ones(len(self), dtype=np.uint8)\n self.cfg = cfg\n \n def load_json(self, fname):\n if self.is_local:\n with open(fname, \"r\") as f:\n return json.load(f)\n else:\n return json.loads(self.client.get(fname))\n\n def load_npy(self, fname):\n if self.is_local:\n with open(fname, \"rb\") as f:\n return np.load(fname, allow_pickle=True)\n else:\n return np.load(io.BytesIO(self.client.get(fname)), allow_pickle=True)\n\n def offset_then_rotate(self, target_2d_world_coor, ref_2d_wolrd_coor, ref_yaw):\n final_coor = target_2d_world_coor - ref_2d_wolrd_coor\n R = np.array([\n [np.cos(ref_yaw), -np.sin(ref_yaw)],\n [np.sin(ref_yaw), np.cos(ref_yaw)]\n ])\n return np.einsum(\"ij,kj->ki\", R.T, final_coor)\n\n ## Preprocess for single frame\n def get_data_info(self, data_folder, route_index, is_current):\n results = {}\n results[\"scene_token\"] = data_folder\n results[\"frame_idx\"] = route_index\n measurements = self.load_json(os.path.join(data_folder, \"measurements\", f\"{str(route_index).zfill(4)}.json\"))\n ego_theta = measurements[\"theta\"] if not np.isnan(measurements[\"theta\"]) else 0# fix for theta=nan in some measurements\n ego_theta = ego_theta - np.pi/2 #Follow https://github.com/dotchen/LAV/blob/23e2f1be3b1b43593761bc7ea7beabda1086b253/team_code/lav_agent.py\n results[\"input_theta\"] = ego_theta\n results[\"input_x\"] = measurements[\"y\"] ## All coordinates are in the ego coordinate system (go front=vertically up in BEV similar to Roach)\n results[\"input_y\"] = -measurements[\"x\"] ## All coordinates are in the ego coordinate system (go front=vertically up in BEV similar to Roach)\n ego_xy = np.stack([results[\"input_x\"], results[\"input_y\"]], axis=-1)\n \n if is_current:\n future_measurements_lis = []\n for future_index in range(1, self.pred_len+1):\n future_measurements_lis.append(self.load_json(os.path.join(data_folder, \"measurements\", f\"{str(route_index+future_index).zfill(4)}.json\")))\n future_x = np.array([_[\"y\"] for _ in future_measurements_lis])\n future_y = -np.array([_[\"x\"] for _ in future_measurements_lis])\n results['waypoints'] = self.offset_then_rotate(np.stack([future_x, future_y], axis=-1), ego_xy, ego_theta)\n results[\"future_speed\"] = [_[\"speed\"] for _ in future_measurements_lis]\n\n results[\"speed\"] = measurements[\"speed\"]\n results[\"can_bus\"] = np.zeros(18)\n results[\"can_bus\"][0] = results[\"input_x\"] ## Gloabal Coordinate\n results[\"can_bus\"][1] = results[\"input_y\"] ## Global Coordinate\n accel = np.array(measurements[\"acceleration\"])\n accel[:2] = self.offset_then_rotate(np.array(accel[:2])[np.newaxis, :], np.array([0, 0]), ego_theta).squeeze(0)\n results[\"can_bus\"][7:10] = accel\n results[\"can_bus\"][10:13] = measurements[\"angular_velocity\"]\n results[\"can_bus\"][13] = measurements[\"speed\"]\n results[\"can_bus\"][-2] = ego_theta\n results[\"can_bus\"][-1] = ego_theta / np.pi * 180\n \n x_target = measurements[\"y_target\"] ## All coordinates are in the ego coordinate system (go front=vertically up in BEV similar to Roach)\n y_target = -measurements[\"x_target\"] ## All coordinates are in the ego coordinate system (go front=vertically up in BEV similar to Roach)\n results['target_point'] = self.offset_then_rotate(np.array([[x_target, y_target]]), ego_xy, ego_theta).squeeze(0)\n results['target_point_aim'] = results[\"target_point\"]\n # VOID = -1\n # LEFT = 1\n # RIGHT = 2\n # STRAIGHT = 3\n # LANEFOLLOW = 4\n # CHANGELANELEFT = 5\n # CHANGELANERIGHT = 6\n command = measurements[\"target_command\"]\n if command < 0:\n command = 4\n command -= 1\n results['target_command_raw'] = torch.tensor(command).long()\n assert command in [0, 1, 2, 3, 4, 5]\n cmd_one_hot = [0] * 6\n cmd_one_hot[command] = 1\n results['target_command'] = torch.tensor(cmd_one_hot)\n\n results[\"pts_filename\"] = os.path.join(data_folder, \"lidar\", f\"{str(route_index).zfill(4)}.npy\")\n if is_current:\n ##Distill Feature\n current_supervision = self.load_npy(os.path.join(data_folder, \"supervision\", f\"{str(route_index).zfill(4)}.npy\")).item()\n ## [throttle, steer, brake]\n results[\"action\"] = current_supervision[\"action\"]\n results[\"action_mu\"] = current_supervision[\"action_mu\"]\n results[\"action_sigma\"] = current_supervision[\"action_sigma\"]\n ## Brake by rules\n if current_supervision['only_ap_brake']:\n results['action_mu'][0] = 0.8\n results['action_sigma'][0] = 5.5\n\n ## Label\n future_supervision_lis = []\n for future_index in range(1, self.pred_len+1):\n future_supervision_lis.append(self.load_npy(os.path.join(data_folder, \"supervision\", f\"{str(route_index+future_index).zfill(4)}.npy\")).item())\n\n future_only_ap_brake = [_[\"only_ap_brake\"] for _ in future_supervision_lis]\n\n results['future_action_mu'] = [_[\"action_mu\"] for _ in future_supervision_lis] ## pred_len x 2\n results['future_action_sigma'] =[_[\"action_sigma\"] for _ in future_supervision_lis] ## pred_len x 2\n results['future_action'] = np.stack([_[\"action\"] for _ in future_supervision_lis], axis=0) ## pred_len x (control.throttle, control.steer, control.brake)\n # use the average value of roach braking action when the brake is only performed by the rule-based detector\n for future_index in range(self.pred_len):\n if future_only_ap_brake[future_index]:\n results['future_action_mu'][future_index][0] = 0.8\n results['future_action_sigma'][future_index][0] = 5.5\n results[\"value\"] = current_supervision[\"value\"]\n results[\"feature\"] = current_supervision[\"features\"]\n results[\"grid_feature\"] = current_supervision[\"cnn_features\"]\n results[\"future_feature\"] = [_[\"features\"] for _ in future_supervision_lis]\n results[\"future_value\"] = [_[\"value\"] for _ in future_supervision_lis]\n results[\"future_grid_feature\"] = [_[\"cnn_features\"] for _ in future_supervision_lis]\n return results\n\n def prepare_train_data(self, index):\n \"\"\"Returns the item at index idx. \"\"\"\n data_queue = []\n # temporal aug # random choose 3 frames from last 4 frames\n #prev_indexs_list = list(range(index-self.queue_length, index))\n #random.shuffle(prev_indexs_list)\n #prev_indexs_list = sorted(prev_indexs_list[1:], reverse=True)\n prev_indexs_list = self.history_query_index_lis\n\n data_folder, route_index = self.data_infos[index]\n input_dict = self.get_data_info(data_folder, route_index, is_current=True)\n if input_dict is None:\n return None\n\n example = self.pipeline(input_dict)\n data_queue.insert(0, example)\n # Load prev frames, not load the current\n for i in prev_indexs_list[:-1][::-1]:\n input_dict = self.get_data_info(data_folder, route_index + i, is_current=False)\n input_dict[\"is_local\"] = self.is_local\n example = self.pipeline(input_dict)\n data_queue.insert(0, copy.deepcopy(example))\n return union2one(self.full_queue_pipeline, data_queue)\n def __getitem__(self, idx):\n while True:\n data = self.prepare_train_data(idx)\n if data is None:\n idx = self._rand_another(idx)\n continue\n return data\n\n def _rand_another(self, idx):\n \"\"\"Randomly get another item with the same flag.\n\n Returns:\n int: Another index of item with the same flag.\n \"\"\"\n pool = np.where(self.flag == self.flag[idx])[0]\n return np.random.choice(pool)\n\n def prepare_test_data(self, index):\n input_dict = self.get_data_info(index)\n example = self.pipeline(input_dict)\n return example\n\n\n\ndef get_ego_shift(delta_x, delta_y, ego_angle):\n # obtain rotation angle and shift with ego motion\n translation_length = np.sqrt(delta_x ** 2 + delta_y ** 2)\n translation_angle = np.arctan2(delta_y, delta_x) / np.pi * 180\n bev_angle = ego_angle - translation_angle\n shift_y = translation_length * np.cos(bev_angle / 180 * np.pi)\n shift_x = translation_length * np.sin(bev_angle / 180 * np.pi) \n return shift_x, shift_y\n\n\n### Multiple frames\ndef union2one(full_queue_pipeline, queue):\n queue = full_queue_pipeline(queue)\n imgs_list = torch.stack([each['img'] for each in queue])\n points_size = [each['points'].data.shape[0] for each in queue]\n metas_map = []\n prev_pos = None\n prev_angle = None\n for i, each in enumerate(queue):\n meta = copy.deepcopy(each['img_metas'].data)\n meta['points_size'] = points_size\n if i == 0: ## Current Frame\n meta['prev_bev'] = False\n prev_pos = copy.deepcopy(meta['can_bus'][:3])\n prev_angle = copy.deepcopy(meta['can_bus'][-1])\n meta['can_bus'][:3] = 0\n meta['can_bus'][-1] = 0\n else:\n meta['prev_bev'] = True\n tmp_pos = copy.deepcopy(meta['can_bus'][:3])\n tmp_angle = copy.deepcopy(meta['can_bus'][-1])\n meta['can_bus'][:3] -= prev_pos\n meta['can_bus'][-1] -= prev_angle\n prev_pos = copy.deepcopy(tmp_pos)\n prev_angle = copy.deepcopy(tmp_angle)\n metas_map.append(meta)\n\n # sweep2key transformation\n metas_map[-1]['curr2key'] = torch.eye(4)\n metas_map[-1]['currlidar2keycam'] = metas_map[-1]['lidar2cam']\n key_x, key_y = queue[-1]['img_metas'].data['can_bus'][:2]\n key_yaw = queue[-1]['img_metas'].data['can_bus'][-2]\n for i in range(len(queue)-2, -1, -1):\n curr_x = queue[i]['img_metas'].data['can_bus'][0]\n curr_y = queue[i]['img_metas'].data['can_bus'][1]\n curr2key_x, curr2key_y = get_ego_shift(\n key_x - curr_x,\n key_y - curr_y,\n key_yaw / np.pi * 180\n )\n curr_yaw = queue[i]['img_metas'].data['can_bus'][-2]\n curr2key_angle = key_yaw - curr_yaw \n # get transmation mats\n R = torch.eye(4)\n R[:2,:2] = torch.Tensor([[np.cos(curr2key_angle), np.sin(curr2key_angle)],\n [-np.sin(curr2key_angle), np.cos(curr2key_angle)]])\n T = torch.eye(4)\n T[0,3], T[1,3] = curr2key_x, curr2key_y\n curr2key = R @ T\n metas_map[i]['curr2key'] = curr2key\n metas_map[i]['currlidar2keycam'] = metas_map[i]['lidar2cam'] @ curr2key\n\n # dense-fusion\n points = queue[-1]['points'].data \n points = torch.cat([points,torch.zeros(points.shape[0],1)], dim=1)\n points[:,4] = 0\n points_list = [points]\n for i in range(len(queue)-2, -1, -1):\n points_sweep = copy.deepcopy(queue[i]['points'].data)\n points_sweep = torch.cat([points_sweep,torch.zeros(points_sweep.shape[0],1)], dim=1)\n curr2key = metas_map[i]['curr2key']\n points_sweep[:, :4] = (curr2key@points_sweep[:, :4].T).T\n timestamp = i-(len(queue)-1)\n points_sweep[:,4] = timestamp\n points_list.append(points_sweep)\n points = torch.cat(points_list).unsqueeze(0)\n queue[-1]['points'] = DC(points, cpu_only=False, stack=True)\n \n queue[-1]['img'] = DC(imgs_list,\n cpu_only=False, stack=True)\n queue[-1]['img_metas'] = DC(metas_map, cpu_only=True)\n queue = queue[-1]\n return queue\n" }, { "path": "open_loop_training/code/datasets/pipelines/__init__.py", "content": "from .transform import (InitMultiImage, ImageTransformMulti, IDAImageTransform)\nfrom .loading import LoadMultiImages, LoadPoints, LoadDepth\nfrom .formating import CarlaFormatBundle,CarlaCollect\n__all__ = [\n 'LoadPoints',\n 'ImageTransformMulti',\n 'InitMultiImage',\n 'LoadMultiImages',\n 'CarlaFormatBundle',\n 'CarlaCollect',\n 'LoadDepth',\n 'IDAImageTransform',\n]" }, { "path": "open_loop_training/code/datasets/pipelines/formating.py", "content": "\r\n# Copyright (c) OpenMMLab. All rights reserved.\r\nimport torch\r\nimport numpy as np\r\nfrom mmcv.parallel import DataContainer as DC\r\n\r\n\r\nfrom mmdet3d.core.points import BasePoints\r\nfrom mmdet.datasets.builder import PIPELINES\r\nfrom mmdet.datasets.pipelines import to_tensor\r\nfrom torch import Tensor\r\n\r\n\r\n@PIPELINES.register_module()\r\nclass CarlaFormatBundle(object):\r\n def __init__(self, ):\r\n return\r\n def __call__(self, results):\r\n if 'points' in results:\r\n if isinstance(results['points'], BasePoints):\r\n results['points'] = DC(results['points'].tensor)\r\n else:\r\n results['points'] = DC(torch.Tensor(results['points']))\r\n if 'img' in results:\r\n if isinstance(results['img'], list):\r\n results['img'] = np.stack(results['img'], axis=0)\r\n return results\r\n def __repr__(self):\r\n return self.__class__.__name__\r\n\r\n\r\n\r\n@PIPELINES.register_module()\r\nclass CarlaCollect(object):\r\n def __init__(self,\r\n keys,\r\n meta_keys=('can_bus', 'ori_shape', 'intrin_mats', 'lidar2img',\r\n 'depth2img', 'cam2img', 'pad_shape',\r\n 'scale_factor', 'flip', 'pcd_horizontal_flip',\r\n 'pcd_vertical_flip', 'frame_idx', 'img_filename',\r\n 'img_norm_cfg', 'bda_mat', 'sample_idx',\r\n 'ida_mats', 'sensor2ego_mats', 'pts_filename',\r\n 'scene_token')):\r\n self.keys = keys\r\n self.meta_keys = meta_keys\r\n\r\n def __call__(self, results):\r\n \"\"\"Call function to collect keys in results. The keys in ``meta_keys``\r\n will be converted to :obj:`mmcv.DataContainer`.\r\n\r\n Args:\r\n results (dict): Result dict contains the data to collect.\r\n\r\n Returns:\r\n dict: The result dict contains the following keys\r\n - keys in ``self.keys``\r\n - ``img_metas``\r\n \"\"\"\r\n data = {}\r\n img_metas = {}\r\n for key in self.meta_keys:\r\n if key in results:\r\n img_metas[key] = results[key]\r\n data['img_metas'] = DC(img_metas, cpu_only=True)\r\n for key in self.keys:\r\n if key in results:\r\n data[key] = results[key]\r\n return data\r\n\r\n def __repr__(self):\r\n \"\"\"str: Return a string that describes the module.\"\"\"\r\n return self.__class__.__name__ + \\\r\n f'(keys={self.keys}, meta_keys={self.meta_keys})'\r\n\r\n" }, { "path": "open_loop_training/code/datasets/pipelines/loading.py", "content": "# Copyright (c) OpenMMLab. All rights reserved.\nimport os\nfrom types import new_class\nimport cv2\nimport time\nimport copy\nimport torch\nimport numpy as np\n\nimport os.path as osp\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nfrom mmdet.datasets.builder import PIPELINES\n\nfrom torchvision import transforms as T\n\nimport mmcv\nfrom mmdet.datasets.builder import PIPELINES\nfrom mmcv.parallel import DataContainer as DC\nfrom mmdet3d.datasets.pipelines.loading import LoadPointsFromFile\nimport io\nfrom scipy.ndimage import label as sep_mask\n\n\n\n@PIPELINES.register_module()\nclass LoadMultiImages(object):\n def __init__(self,\n is_local=False,\n camera_names = ['rgb_front', 'rgb_left', 'rgb_right', 'rgb_back'],\n ceph_conf=\"~/petreloss.conf\",\n ):\n self.is_local = is_local\n self.camera_names = camera_names\n if not self.is_local:\n from petrel_client.client import Client\n self.client = Client(ceph_conf)\n self.file_client_args=dict(backend='disk')\n self.file_client = mmcv.FileClient(**self.file_client_args)\n def load_img(self, filename):\n if self.is_local:\n return np.array(Image.open(filename))\n else:\n return np.frombuffer(memoryview(self.client.get(filename)), np.uint8).reshape(900, 1600, 3)\n def __call__(self, results):\n filenames = [os.path.join(results[\"scene_token\"], name, str(results[\"frame_idx\"]).zfill(4)+\".png\") for name in self.camera_names]\n results['img'] = [self.load_img(filename) for filename in filenames]\n results[\"img_filename\"] = filenames\n return results\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(is_local={self.is_local}, '\n return repr_str\n\n\n\n@PIPELINES.register_module()\nclass LoadPoints(LoadPointsFromFile):\n def __init__(self,\n is_local,\n ceph_conf='~/petreloss.conf',\n **kwargs,\n ):\n super().__init__(**kwargs)\n self.is_local = is_local\n if not self.is_local:\n from petrel_client.client import Client\n self.client = Client(ceph_conf)\n def _load_points(self, pts_filename):\n if not self.is_local:\n points = np.load(io.BytesIO(self.client.get(pts_filename)), allow_pickle=True)\n else:\n points = np.load(pts_filename, allow_pickle=True)\n return points\n\n@PIPELINES.register_module()\nclass LoadDepth(LoadMultiImages):\n def __init__(self,\n is_local=False, # carla's camera use unreal-coordination, which must be corrected\n camera_names = ['rgb_front', 'rgb_left', 'rgb_right', 'rgb_back'],\n ceph_conf=\"~/petreloss.conf\",\n ):\n super().__init__(is_local, camera_names, ceph_conf)\n def __call__(self, results):\n filenames = [os.path.join(results[\"scene_token\"], name.replace('rgb','depth'), str(results[\"frame_idx\"]).zfill(4)+\".png\") for name in self.camera_names]\n depths = []\n for filename in filenames:\n rgb = self.load_img(filename).astype(np.float32)\n r,g,b = rgb[...,0], rgb[...,1], rgb[...,2]\n depth = (r + g*256 + b*256*256)/(256**3 - 1) * 1000\n depths.append(depth)\n results['depth'] = depths\n return results\n\n## Segment Traffic Light\ndef red_green_yellow(rgb_image):\n hsv = cv2.cvtColor(rgb_image[:, None, :], cv2.COLOR_RGB2HSV)\n avg_saturation = int(hsv[:,:,1].mean()) # Sum the brightness values \n sat_low = int(avg_saturation * 1.1)#1.3)\n val_low = 140\n # Green\n lower_green = np.array([70,sat_low,val_low])\n upper_green = np.array([100,255,255])\n sum_green = cv2.inRange(hsv, lower_green, upper_green).astype(np.bool8).sum()\n # Red\n lower_red = np.array([150,sat_low,val_low])\n upper_red = np.array([180,255,255])\n sum_red = cv2.inRange(hsv, lower_red, upper_red).astype(np.bool8).sum()\n if sum_red < 3 and sum_green < 3:\n return 0 #not sure or yellow\n if sum_red >= sum_green:\n return 1# Red\n return 2 # Green\n\n\n@PIPELINES.register_module()\nclass LoadSeg(LoadMultiImages):\n def __init__(self,\n is_local=False, # carla's camera use unreal-coordination, which must be corrected\n camera_names = ['rgb_front', 'rgb_left', 'rgb_right', 'rgb_back'],\n seg_label_idxs=[0,1,2,3,4,5,6],\n ceph_conf=\"~/petreloss.conf\",\n ):\n super().__init__(is_local, camera_names, ceph_conf)\n self.seg_label_idxs = seg_label_idxs\n \n def load_img(self, filename):\n if self.is_local:\n return np.array(Image.open(filename))\n else:\n return np.frombuffer(memoryview(self.client.get(filename)), np.uint8).reshape(900, 1600,)\n def __call__(self, results):\n filenames = [os.path.join(results[\"scene_token\"], name.replace('rgb','seg'), str(results[\"frame_idx\"]).zfill(4)+\".png\") for name in self.camera_names]\n segs = []\n for f_index, filename in enumerate(filenames):\n src = self.load_img(filename).astype(np.float32)\n seg = np.zeros_like(src)\n for idx, label in enumerate(self.seg_label_idxs):\n ## Mannually Segment the traffic light into red, green, yellow\n if label == 18:\n now_img = results['img'][f_index]\n tl_part = src==label\n #tl_pixel_index = np.where(tl_part)\n tl_mask, num_tl = sep_mask(tl_part, structure=[[1,1,1],[1,1,1],[1,1,1]])\n a_flattened = tl_mask.ravel()\n sidx = np.argsort(a_flattened)\n afs = a_flattened[sidx]\n cut_idx = np.r_[0,np.flatnonzero(afs[1:] != afs[:-1])+1, a_flattened.size]\n row, col = np.unravel_index(sidx, tl_mask.shape)\n row_indices = [row[i:j] for i,j in zip(cut_idx[:-1],cut_idx[1:])][1:]\n col_indices = [col[i:j] for i,j in zip(cut_idx[:-1],cut_idx[1:])][1:]\n for tl_index in range(num_tl):\n if len(row_indices[tl_index]) < 20:\n continue\n now_tl = now_img[row_indices[tl_index], col_indices[tl_index], :]\n light_type=red_green_yellow(now_tl)\n seg[row_indices[tl_index], col_indices[tl_index]] = idx + light_type\n else:\n seg[np.where(src==label)] = idx\n segs.append(seg)\n results['seg'] = segs\n return results\n\n" }, { "path": "open_loop_training/code/datasets/pipelines/transform.py", "content": "from email.mime import image\nimport os\nimport cv2\nimport copy\nimport numpy as np\nfrom numpy import random\nimport matplotlib.pyplot as plt\nfrom imgaug import augmenters as iaa\nfrom torchvision import transforms as T\nimport torch\nimport mmcv\nfrom mmdet.datasets.builder import PIPELINES\nfrom mmcv.parallel import DataContainer as DC\nfrom mmdet.datasets.pipelines import to_tensor\n\n## Precompute the following matrice to speed\nLIDAR2UNREAL = {\n \"rgb_front\": np.array([[1.0, 0.0, -0.0, -1.5], [0.0, 1.0, 0.0, 0.0], [-0.0, 0.0, 1.0, -2.5], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_back\":np.array([[-1.0, -0.0, 0.0, -1.6], [0.0, -1.0, 0.0, -0.0], [0.0, 0.0, 1.0, -2.5], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_left\":np.array([[0.0, -1.0, 0.0, -0.3], [1.0, 0.0, -0.0, -0.0], [0.0, 0.0, 1.0, -2.5], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_right\":np.array([[0.0, 1.0, -0.0, -0.3], [-1.0, -0.0, 0.0, 0.0], [0.0, 0.0, 1.0, -2.5], [0.0, 0.0, 0.0, 1.0]])\n} \n\nunreal2cam = np.array([[0,1,0,0], [0,0,-1,0], [1,0,0,0], [0,0,0,1]])\nLIDAR2CAM = {\n \"rgb_front\": np.array([ [0.0, 1.0, 0.0, 0.0] , [0.0, 0.0, -1.0, 2.5] , [1.0, 0.0, 0.0, -1.5] , [0.0, 0.0, 0.0, 1.0] ]),\n \"rgb_back\": np.array([ [0.0, -1.0, 0.0, 0.0] , [0.0, 0.0, -1.0, 2.5] , [-1.0, 0.0, 0.0, -1.6] , [0.0, 0.0, 0.0, 1.0] ]),\n \"rgb_left\": np.array([ [1.0, 0.0, 0.0, 0.0] , [0.0, 0.0, -1.0, 2.5] , [0.0, -1.0, 0.0, -0.3] , [0.0, 0.0, 0.0, 1.0] ]),\n \"rgb_right\": np.array([ [-1.0, 0.0, 0.0, 0.0] , [0.0, 0.0, -1.0, 2.5] , [0.0, 1.0, 0.0, -0.3] , [0.0, 0.0, 0.0, 1.0] ]),\n}\n\n\nUNDISTORT_LIDAR2IMG = {\n \"rgb_front\":np.array([[788.25758876, 304.14395142, 0.0, -1182.38638314], [449.78972161, 0.0, -221.49429321, -120.94884939000008], [1.0, 0.0, 0.0, -1.5], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_left\":np.array([[304.14395142, -788.25758876, 0.0, -236.47727662799997], [0.0, -449.78972161, -221.49429321, 418.79881654199994], [0.0, -1.0, 0.0, -0.3], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_right\":np.array([[-304.14395142, 788.25758876, 0.0, -236.47727662799997], [0.0, 449.78972161, -221.49429321, 418.79881654199994], [0.0, 1.0, 0.0, -0.3], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_back\":np.array([[-788.25758876, -304.14395142, 0.0, -1261.2121420160001], [-449.78972161, 0.0, -221.49429321, -165.9278215510001], [-1.0, 0.0, 0.0, -1.6], [0.0, 0.0, 0.0, 1.0]])\n}\n\nLIDAR2IMG = {\n \"rgb_front\":np.array([[800.0, 214.35935394, 0.0, -1200.0], [450.0, 0.0, -214.35935394, -139.10161515000004], [1.0, 0.0, 0.0, -1.5], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_left\":np.array([[214.35935394, -800.0, 0.0, -240.0], [0.0, -450.0, -214.35935394, 400.89838484999996], [0.0, -1.0, 0.0, -0.3], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_right\":np.array([[-214.35935394, 800.0, 0.0, -240.0], [0.0, 450.0, -214.35935394, 400.89838484999996], [0.0, 1.0, 0.0, -0.3], [0.0, 0.0, 0.0, 1.0]]),\n \"rgb_back\":np.array([[-800.0, -214.35935394, 0.0, -1280.0], [-450.0, 0.0, -214.35935394, -184.10161515000007], [-1.0, 0.0, 0.0, -1.6], [0.0, 0.0, 0.0, 1.0]])\n}\n\nmtx = np.array([[214.35935394, 0, 800,],[0, 214.35935394, 450],[0, 0, 1]])\ndist = np.array([[ 0.00888296, -0.00130899, 0.00012061, -0.00338673, 0.00028834]])\nnewcameramtx = np.array([[304.14395142, 0, 788.25758876,],\n[ 0, 221.49429321, 449.78972161,],\n[ 0, 0, 1, ],])\nCAM_INTRINSIC = {\n \"rgb_front\": mtx,\n \"rgb_back\": mtx,\n \"rgb_left\": mtx,\n \"rgb_right\": mtx,\n}\nCAM_DIST= {\n \"rgb_front\": dist,\n \"rgb_back\": dist,\n \"rgb_left\": dist,\n \"rgb_right\": dist,\n}\n\n@PIPELINES.register_module(force=True)\nclass InitMultiImage(object):\n \"\"\"Random scale the image\n Args:\n scales\n \"\"\"\n def __init__(self, cfg):\n self.size = cfg[\"img_size\"]\n self.camera_names = cfg['camera_names']\n self.resize_func = T.Resize(size=self.size)\n assert len(self.size)==2\n self.undistort = cfg[\"undistort\"]\n self.unreal_coord = cfg[\"unreal_coord\"]\n self.use_depth = cfg['use_depth']\n ## Undistort mat\n mapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (1600, 900), 5)\n mapx = (torch.from_numpy(mapx) - 800) / 800\n mapy= (torch.from_numpy(mapy) - 450) / 450\n self.num_cams = cfg[\"num_cams\"]\n self.temporal_len = cfg[\"queue_length\"]\n ## Precompute the mapping matrix to speed up\n self.map_grid = torch.stack([mapx, mapy], dim=-1).unsqueeze(0).repeat(self.temporal_len*self.num_cams, 1, 1, 1)\n if self.use_depth:\n self.map_grid_depth = torch.stack([mapx, mapy], dim=-1).unsqueeze(0).repeat(self.num_cams, 1, 1, 1)\n\n ##Initialize mats for all cameras\n self.cam_intrinsic = np.array([newcameramtx.copy() for _ in range(self.num_cams)] if self.undistort else [mtx.copy() for _ in range(self.num_cams)])\n self.lidar2cam = np.array([LIDAR2CAM[name] for name in self.camera_names])\n self.lidar2img = np.array([UNDISTORT_LIDAR2IMG[name] for name in self.camera_names] if self.undistort else [LIDAR2IMG[name] for name in self.camera_names])\n\n def __call__(self, queue):\n \"\"\"Call function to pad images, masks, semantic segmentation maps.\n Args:\n results (dict): Result dict from loading pipeline.\n Returns:\n dict: Updated result dict.\n \"\"\"\n #loading from queue\n imgs_list = np.stack([each['img'].data for each in queue], axis=0)\n temporal_len, num_cams, h, w, c = imgs_list.shape\n y_size, x_size = self.size\n y_scale, x_scale = y_size/h, x_size/w\n\n ## undistort\n imgs_list = torch.from_numpy(imgs_list).to(dtype=torch.get_default_dtype()).view(-1, h, w, c).permute(0, 3, 1, 2)\n if self.undistort:\n imgs_list = torch.nn.functional.grid_sample(imgs_list, self.map_grid, align_corners=False)\n imgs_list = self.resize_func(imgs_list).view(temporal_len, num_cams, c, y_size, x_size)\n\n ## depth label \n if self.use_depth:\n depth = torch.from_numpy(np.stack(queue[-1]['depth'])).to(dtype=torch.get_default_dtype()).view(-1,1,h,w)\n if self.undistort:\n depth = torch.nn.functional.grid_sample(depth, self.map_grid_depth, align_corners=False)\n queue[-1]['depth'] = self.resize_func(depth)[:,0,...]\n ## camera mat\n scale_factor = np.eye(4)\n scale_factor[0, 0] *= x_scale\n scale_factor[1, 1] *= y_scale\n for i in range(len(queue)):\n queue[i][\"img_metas\"].data['cam_intrinsic'] = self.cam_intrinsic \n queue[i][\"img_metas\"].data['lidar2cam'] = self.lidar2cam\n lidar2img = [scale_factor @ l2i for l2i in self.lidar2img]\n queue[i][\"img_metas\"].data['lidar2img'] = lidar2img\n queue[i][\"img\"] = imgs_list[i]\n queue[i][\"img_metas\"].data['img_shape'] = [imgs_list.shape[-2:]] * self.num_cams\n queue[i][\"img_metas\"].data['ori_shape'] = [imgs_list.shape[-2:]] * self.num_cams\n queue[i][\"img_metas\"].data['ida_mats'] = np.array([[[1,0,0,0], [0,1,0,0], [0,0,1,0], [0,0,0,1]]] * self.num_cams)\n return queue\n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(size={self.size}, '\n return repr_str\n\n\n\n@PIPELINES.register_module()\nclass ImageTransformMulti(object):\n def __init__(self, aug, batch_size):\n self.transform = T.Compose([T.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])])\n self.aug = aug\n self._batch_read_number = 0\n self._batch_size = batch_size\n \n def __call__(self, queue):\n if self.aug:\n imgs_list = np.stack([each['img'].numpy().astype(np.uint8) for each in queue], axis=0).transpose((0, 1, 3, 4, 2))\n temporal_len, num_cams, H, W, C = imgs_list.shape\n #temporal length x num_camera x H x W x C\n now_augmentor = augmenter(self._batch_read_number/self._batch_size).to_deterministic()\n ##Same ImgAug for all history frames and camera of one single sample\n new_imgs_list = np.stack([now_augmentor.augment_image(imgs_list[i][j]) for i in range(temporal_len) for j in range(num_cams)], axis=0).reshape(-1, H, W, C)\n new_imgs_list = self.transform(torch.from_numpy(new_imgs_list).permute(0, 3, 1, 2).to(dtype=torch.get_default_dtype()).div(255)).view(temporal_len, num_cams, C, H, W)\n for temporal_index in range(imgs_list.shape[0]):\n queue[temporal_index][\"img\"] = to_tensor(new_imgs_list[temporal_index])\n self._batch_read_number += 1\n else:\n for temporal_index in range(len(queue)):\n queue[temporal_index][\"img\"] = self.transform(queue[temporal_index][\"img\"].to(dtype=torch.get_default_dtype()).div(255))\n return queue\n \n def __repr__(self):\n repr_str = self.__class__.__name__\n repr_str += f'(aug={self.aug}, '\n return repr_str\n\ndef augmenter(image_iteration):\n iteration = image_iteration\n frequency_factor = min(0.05 + float(iteration)/600000.0, 1.0)\n color_factor = min(float(iteration)/3000000.0, 1.0)\n dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 600000) ** 1.863486)\n\n blur_factor = min(0.5 + (0.5*iteration/300000.0), 1.0)\n\n add_factor = 10 + 10*iteration/300000.0\n\n multiply_factor_pos = 1 + (2.5*iteration/600000.0)\n multiply_factor_neg = 1 - (0.91 * iteration / 1500000.0)\n\n contrast_factor_pos = 1 + (0.5*iteration/1500000.0)\n contrast_factor_neg = 1 - (0.5 * iteration / 1500000.0)\n\n augmenter = iaa.Sequential([\n\n iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),\n # blur images with a sigma between 0 and 1.5\n iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, dropout_factor),\n per_channel=color_factor)),\n # add gaussian noise to images\n iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(\n 0.08, 0.2), per_channel=color_factor)),\n # randomly remove up to X% of the pixels\n iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),\n # randomly remove up to X% of the pixels\n iaa.Sometimes(frequency_factor,\n iaa.Add((-add_factor, add_factor), per_channel=color_factor)),\n # change brightness of images (by -X to Y of original value)\n iaa.Sometimes(frequency_factor,\n iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),\n # change brightness of images (X-Y% of original value)\n # iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),\n # per_channel=color_factor)),\n iaa.Sometimes(frequency_factor, iaa.contrast.LinearContrast((contrast_factor_neg, contrast_factor_pos),\n per_channel=color_factor)),\n # improve or worsen the contrast\n iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))), # put grayscale\n\n ],\n random_order=True # do all of the above in random order\n )\n\n return augmenter\n\n\n\n### From BEVDepth: https://github.com/Megvii-BaseDetection/BEVDepth\n@PIPELINES.register_module()\nclass IDAImageTransform(object):\n def __init__(self, cfg, ida_aug_conf, is_train=False):\n self.ida_aug_conf = ida_aug_conf\n self.is_train = is_train\n self.size = cfg[\"img_size\"]\n self.camera_names = cfg['camera_names']\n self.resize_func = T.Resize(size=self.size)\n assert len(self.size)==2\n self.undistort = cfg[\"undistort\"]\n self.unreal_coord = cfg[\"unreal_coord\"]\n self.use_depth = False if 'use_depth' not in cfg else cfg['use_depth']\n self.use_seg = False if 'use_seg' not in cfg else cfg['use_seg']\n ## Undistort mat\n mapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (1600, 900), 5)\n mapx = (torch.from_numpy(mapx) - 800) / 800\n mapy= (torch.from_numpy(mapy) - 450) / 450\n self.num_cams = cfg[\"num_cams\"]\n self.temporal_len = cfg[\"queue_length\"]\n self.map_grid = torch.stack([mapx, mapy], dim=-1).unsqueeze(0).repeat(self.temporal_len*self.num_cams, 1, 1, 1)\n if self.use_depth or self.use_seg:\n self.map_grid_depth = torch.stack([mapx, mapy], dim=-1).unsqueeze(0).repeat(self.num_cams, 1, 1, 1)\n ##Initialize mats for all cameras\n self.cam_intrinsic = torch.from_numpy((np.stack([newcameramtx.copy() for _ in range(self.num_cams)], axis=0) if self.undistort else np.stack([mtx.copy() for _ in range(self.num_cams)], axis=0)).astype(np.float32))\n self.lidar2cam = torch.from_numpy(np.stack([LIDAR2CAM[name] for name in self.camera_names], axis=0).astype(np.float32))\n self.lidar2img = torch.from_numpy((np.stack([UNDISTORT_LIDAR2IMG[name] for name in self.camera_names], axis=0) if self.undistort else np.stack([LIDAR2IMG[name] for name in self.camera_names], axis=0)).astype(np.float32))\n\n def sample_ida_augmentation(self):\n \"\"\"Generate ida augmentation values based on ida_config.\"\"\"\n H, W = self.ida_aug_conf['H'], self.ida_aug_conf['W']\n fH, fW = self.ida_aug_conf['final_dim']\n if self.is_train:\n resize = np.random.uniform(*self.ida_aug_conf['resize_lim'])\n resize_dims = (int(W * resize), int(H * resize))\n newW, newH = resize_dims\n crop_h = int(\n (1 - np.random.uniform(*self.ida_aug_conf['bot_pct_lim'])) *\n newH) - fH\n crop_w = int(np.random.uniform(0, max(0, newW - fW)))\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\n flip = False\n if self.ida_aug_conf['rand_flip'] and np.random.choice([0, 1]):\n flip = True\n else:\n resize = max(fH / H, fW / W)\n resize_dims = (int(W * resize), int(H * resize))\n newW, newH = resize_dims\n crop_h = int(\n (1 - np.mean(self.ida_aug_conf['bot_pct_lim'])) * newH) - fH\n crop_w = int(max(0, newW - fW) / 2)\n crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)\n flip = False\n return resize, resize_dims, crop, flip\n\n def __call__(self, queue):\n #loading from queue\n imgs_list = np.stack([each['img'].data for each in queue], axis=0)\n T, N, h, w, c = imgs_list.shape\n ## undistort\n imgs_list = torch.from_numpy(imgs_list).to(dtype=torch.get_default_dtype()).view(-1, h, w, c).permute(0, 3, 1, 2)\n if self.undistort:\n imgs_list = torch.nn.functional.grid_sample(imgs_list, self.map_grid, align_corners=False)\n imgs_list = imgs_list.view(T,N,c,h,w)\n ## depth label \n if self.use_depth:\n depth = torch.from_numpy(np.stack(queue[-1]['depth'])).to(dtype=torch.get_default_dtype()).view(-1,1,h,w)\n if self.undistort:\n depth = torch.nn.functional.grid_sample(depth, self.map_grid_depth, align_corners=False)[:,0]\n if self.use_seg:\n seg = torch.from_numpy(np.stack(queue[-1]['seg'])).to(dtype=torch.get_default_dtype()).view(-1,1,h,w)\n if self.undistort:\n seg = torch.nn.functional.grid_sample(seg, self.map_grid_depth, align_corners=False)[:,0]\n\n img_list_aug = []\n ida_list = []\n depth_list = []\n seg_list = []\n for cam_id in range(N):\n resize, resize_dims, crop, flip = self.sample_ida_augmentation()\n if self.use_depth:\n depth_aug = depth_transform(\n depth[cam_id], resize_dims,\n crop, flip)\n depth_list.append(depth_aug)\n if self.use_seg:\n seg_aug = depth_transform(\n seg[cam_id], resize_dims,\n crop, flip)\n seg_list.append(seg_aug)\n imgs, idas = [], []\n for frame_id in range(T):\n img_aug, ida_mat = img_transform(\n imgs_list[frame_id][cam_id],\n resize=resize,\n resize_dims=resize_dims,\n crop=crop,\n flip=flip,\n rotate=0,\n )\n imgs.append(img_aug)\n idas.append(ida_mat)\n imgs, idas = torch.stack(imgs), torch.stack(idas)\n img_list_aug.append(imgs)\n ida_list.append(idas)\n \n img_list_aug = torch.stack(img_list_aug).permute(1,0,2,3,4) \n ida_list = torch.stack(ida_list).permute(1,0,2,3)\n if self.use_depth:\n queue[-1]['depth'] = torch.stack(depth_list)\n if self.use_seg:\n queue[-1]['seg'] = torch.stack(seg_list)\n ## camera mat\n for i in range(len(queue)): # Save origin values, and they will be transformed in fpn_lss.py finally\n queue[i][\"img_metas\"].data['cam_intrinsic'] = self.cam_intrinsic \n queue[i][\"img_metas\"].data['lidar2cam'] = self.lidar2cam\n queue[i][\"img_metas\"].data['lidar2img'] = self.lidar2img\n queue[i][\"img\"] = img_list_aug[i]\n queue[i][\"img_metas\"].data['img_shape'] = [imgs_list.shape[-2:]] * self.num_cams\n queue[i][\"img_metas\"].data['ori_shape'] = [imgs_list.shape[-2:]] * self.num_cams\n queue[i][\"img_metas\"].data['ida_mats'] = ida_list[i]\n return queue\n\n \n \n\ndef img_transform(img, resize, resize_dims, crop, flip, rotate=0):\n ida_rot = torch.eye(2)\n ida_tran = torch.zeros(2)\n\n (w,h) = resize_dims\n resize_func = T.Resize((h,w))\n # adjust image\n img = resize_func(img)\n x0,y0,x1,y1 = crop\n \n img = img[..., y0:y1, x0:x1]\n if flip:\n img = torch.flip(img, dims=[-1])\n\n # post-homography transformation\n ida_rot *= resize\n ida_tran -= torch.Tensor(crop[:2])\n if flip:\n A = torch.Tensor([[-1, 0], [0, 1]])\n b = torch.Tensor([crop[2] - crop[0], 0])\n ida_rot = A.matmul(ida_rot)\n ida_tran = A.matmul(ida_tran) + b\n A = get_rot(rotate / 180 * np.pi)\n b = torch.Tensor([crop[2] - crop[0], crop[3] - crop[1]]) / 2\n b = A.matmul(-b) + b\n ida_rot = A.matmul(ida_rot)\n ida_tran = A.matmul(ida_tran) + b\n ida_mat = ida_rot.new_zeros(4, 4)\n ida_mat[3, 3] = 1\n ida_mat[2, 2] = 1\n ida_mat[:2, :2] = ida_rot\n ida_mat[:2, 3] = ida_tran\n return img, ida_mat\n\ndef get_rot(h):\n return torch.Tensor([\n [np.cos(h), np.sin(h)],\n [-np.sin(h), np.cos(h)],\n ])\n\ndef depth_transform(depth, resize_dims, crop, flip):\n (w,h) = resize_dims\n resize_func = T.Resize((h,w))\n # adjust image\n depth = resize_func(depth.unsqueeze(0))\n x0,y0,x1,y1 = crop\n depth = depth[..., y0:y1, x0:x1]\n if flip:\n depth = torch.flip(depth, dims=[-1])\n\n return depth[0]" }, { "path": "open_loop_training/code/datasets/samplers/__init__.py", "content": "from .group_sampler import DistributedGroupSampler\nfrom .distributed_sampler import DistributedSampler\nfrom .sampler import SAMPLER, build_sampler\n\n" }, { "path": "open_loop_training/code/datasets/samplers/distributed_sampler.py", "content": "import math\n\nimport torch\nfrom torch.utils.data import DistributedSampler as _DistributedSampler\nfrom .sampler import SAMPLER\n\n\n@SAMPLER.register_module()\nclass DistributedSampler(_DistributedSampler):\n\n def __init__(self,\n dataset=None,\n num_replicas=None,\n rank=None,\n shuffle=True,\n seed=0):\n super().__init__(\n dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)\n # for the compatibility from PyTorch 1.3+\n self.seed = seed if seed is not None else 0\n\n def __iter__(self):\n # deterministically shuffle based on epoch\n if self.shuffle:\n assert False\n else:\n indices = torch.arange(len(self.dataset)).tolist()\n\n # add extra samples to make it evenly divisible\n # in case that indices is shorter than half of total_size\n indices = (indices *\n math.ceil(self.total_size / len(indices)))[:self.total_size]\n assert len(indices) == self.total_size\n\n # subsample\n per_replicas = self.total_size//self.num_replicas\n # indices = indices[self.rank:self.total_size:self.num_replicas]\n indices = indices[self.rank*per_replicas:(self.rank+1)*per_replicas]\n assert len(indices) == self.num_samples\n\n return iter(indices)\n" }, { "path": "open_loop_training/code/datasets/samplers/group_sampler.py", "content": "\n# Copyright (c) OpenMMLab. All rights reserved.\nimport math\n\nimport numpy as np\nimport torch\nfrom mmcv.runner import get_dist_info\nfrom torch.utils.data import Sampler\nfrom .sampler import SAMPLER\nimport random\n\n\n@SAMPLER.register_module()\nclass DistributedGroupSampler(Sampler):\n \"\"\"Sampler that restricts data loading to a subset of the dataset.\n It is especially useful in conjunction with\n :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each\n process can pass a DistributedSampler instance as a DataLoader sampler,\n and load a subset of the original dataset that is exclusive to it.\n .. note::\n Dataset is assumed to be of constant size.\n Arguments:\n dataset: Dataset used for sampling.\n num_replicas (optional): Number of processes participating in\n distributed training.\n rank (optional): Rank of the current process within num_replicas.\n seed (int, optional): random seed used to shuffle the sampler if\n ``shuffle=True``. This number should be identical across all\n processes in the distributed group. Default: 0.\n \"\"\"\n\n def __init__(self,\n dataset,\n samples_per_gpu=1,\n num_replicas=None,\n rank=None,\n seed=0):\n _rank, _num_replicas = get_dist_info()\n if num_replicas is None:\n num_replicas = _num_replicas\n if rank is None:\n rank = _rank\n self.dataset = dataset\n self.samples_per_gpu = samples_per_gpu\n self.num_replicas = num_replicas\n self.rank = rank\n self.epoch = 0\n self.seed = seed if seed is not None else 0\n\n assert hasattr(self.dataset, 'flag')\n self.flag = self.dataset.flag\n self.group_sizes = np.bincount(self.flag)\n\n self.num_samples = 0\n for i, j in enumerate(self.group_sizes):\n self.num_samples += int(\n math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu /\n self.num_replicas)) * self.samples_per_gpu\n self.total_size = self.num_samples * self.num_replicas\n\n def __iter__(self):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(self.epoch + self.seed)\n\n indices = []\n for i, size in enumerate(self.group_sizes):\n if size > 0:\n indice = np.where(self.flag == i)[0]\n assert len(indice) == size\n indice = indice[list(\n torch.randperm(int(size), generator=g).numpy())].tolist()\n extra = int(\n math.ceil(\n size * 1.0 / self.samples_per_gpu / self.num_replicas)\n ) * self.samples_per_gpu * self.num_replicas - len(indice)\n # pad indice\n tmp = indice.copy()\n for _ in range(extra // size):\n indice.extend(tmp)\n indice.extend(tmp[:extra % size])\n indices.extend(indice)\n\n assert len(indices) == self.total_size\n\n indices = [\n indices[j] for i in list(\n torch.randperm(\n len(indices) // self.samples_per_gpu, generator=g))\n for j in range(i * self.samples_per_gpu, (i + 1) *\n self.samples_per_gpu)\n ]\n\n # subsample\n offset = self.num_samples * self.rank\n indices = indices[offset:offset + self.num_samples]\n assert len(indices) == self.num_samples\n\n return iter(indices)\n\n def __len__(self):\n return self.num_samples\n\n def set_epoch(self, epoch):\n self.epoch = epoch\n\n" }, { "path": "open_loop_training/code/datasets/samplers/sampler.py", "content": "from mmcv.utils.registry import Registry, build_from_cfg\r\n\r\nSAMPLER = Registry('sampler')\r\n\r\n\r\ndef build_sampler(cfg, default_args):\r\n return build_from_cfg(cfg, SAMPLER, default_args)\r\n" }, { "path": "open_loop_training/code/encoder_decoder_framework.py", "content": "from collections import deque, OrderedDict\nfrom distutils.command.config import config\nimport cv2\nimport numpy as np\nimport matplotlib\nmatplotlib.use('AGG')\nimport matplotlib.pyplot as plt\n\nimport torch \nfrom torch import nn\nimport torch.nn.functional as F\nimport torch.distributed as dist\nfrom torchvision import transforms as T\n\nfrom mmcv.runner import BaseModule\nfrom mmdet.models import DETECTORS\nimport mmcls.models\nfrom mmdet3d.models import builder\nfrom mmcv.runner import force_fp32, auto_fp16\nfrom .utils import PIDController, FocalLoss, set_dropout_zero, init_weights, SEBasicBlock\n\n\n@DETECTORS.register_module()\nclass EncoderDecoder(BaseModule):\n def __init__(self, \n img_encoder,\n decoder,\n lidar_encoder=None,\n num_cams = 4,\n use_depth=False,\n use_seg=False,\n downsample_factor=16,\n seg_downsample_factor=2,\n train_cfg=None,\n test_cfg=None,\n ):\n super().__init__()\n self.config = train_cfg\n self.fp16_enabled = False\n\n self.num_cams = num_cams\n self.use_depth = False if 'use_depth' not in train_cfg else train_cfg['use_depth']\n self.use_seg = False if 'use_seg' not in train_cfg else train_cfg['use_seg']\n self.downsample_factor = downsample_factor\n self.seg_downsample_factor = seg_downsample_factor\n \n self.turn_controller = PIDController(K_P=self.config['turn_KP'], K_I=self.config['turn_KI'], K_D=self.config['turn_KD'], n=self.config['turn_n'])\n self.speed_controller = PIDController(K_P=self.config['speed_KP'], K_I=self.config['speed_KI'], K_D=self.config['speed_KD'], n=self.config['speed_n'])\n\n ## Encoder\n self.img_encoder = builder.build_backbone(img_encoder)\n self.depth_resize = None\n self.dbound = self.img_encoder.d_bound\n self.depth_channels = int((self.dbound[1] - self.dbound[0]) / self.dbound[2])\n self.seg_loss_func = FocalLoss()\n self.lidar_encoder = builder.build_backbone(lidar_encoder)\n self.act = nn.ReLU\n self.build_fusion_and_flatten_network_for_BEV()\n self.measurements_encoder = nn.Sequential(\n nn.Linear(1+2+6, 128),\n self.act(),\n nn.Linear(128, 128),\n self.act(),\n )\n nn.init.xavier_normal_(self.measurements_encoder[0].weight)\n nn.init.constant_(self.measurements_encoder[0].bias, 0)\n nn.init.xavier_normal_(self.measurements_encoder[2].weight)\n nn.init.constant_(self.measurements_encoder[2].bias, 0)\n \n\n ## Decoder\n self.decoder= builder.build_head(decoder)\n self.max_epoch = 60\n if \"total_epochs\" in self.config:\n self.max_epoch = self.config[\"total_epochs\"]\n \n def set_epoch(self, epoch):\n self.epoch = epoch\n \n @auto_fp16()\n def build_fusion_and_flatten_network_for_BEV(self):\n ### BEV Fusion Network \n self.norm = nn.BatchNorm2d\n self.inplace_act = self.act()\n self.conv_cam = nn.Sequential(\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\n self.norm(256),\n self.act(),\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\n self.norm(256),\n )\n self.conv_cam.apply(init_weights)\n\n self.conv_lidar = nn.Sequential(\n nn.Conv2d(256*2, 256, kernel_size=3, padding=1, bias=False, stride=2),\n self.norm(256),\n self.act(),\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False, stride=2),\n self.norm(256),\n self.act(),\n )\n self.conv_lidar.apply(init_weights)\n self.conv_fusion = nn.Sequential(\n nn.Conv2d(256*2, 256, kernel_size=3, padding=1, bias=False),\n self.norm(256),\n self.act(),\n nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\n self.norm(256),\n )\n self.conv_fusion.apply(init_weights)\n\n ### Transform Raw BEV feature to 1D feature map\n ## The dimension of middle BEV feature map follows Roach to enable feature distillation\n self._256_to_32 = nn.Conv2d(256, 32, kernel_size=3, padding=1)\n self._256_to_32.apply(init_weights)\n self.MLP21 = SEBasicBlock(32, self.act) ## MLP for the 21x21 BEV feature map\n self.MLP10 = SEBasicBlock(64, self.act) ## MLP for the 10x10 BEV feature map\n self.MLP4 = SEBasicBlock(128, self.act) ## MLP for the 4x4 BEV feature map\n self.MLP2 = SEBasicBlock(256, self.act) ## MLP for the 2x2 BEV feature map\n self.MLP21.apply(init_weights)\n self.MLP10.apply(init_weights)\n self.MLP4.apply(init_weights)\n self.MLP2.apply(init_weights)\n self.conv21_10 = nn.Conv2d(32, 64, kernel_size=3, stride=2)\n self.conv10_4 = nn.Conv2d(64, 128, kernel_size=3, stride=2)\n self.conv4_2 = nn.Conv2d(128, 256, kernel_size=3, stride=1)\n self.conv21_10.apply(init_weights)\n self.conv10_4.apply(init_weights)\n self.conv4_2.apply(init_weights)\n \n self.output_fc = nn.Sequential(\n nn.Linear(1024, 512),\n self.act(),\n nn.BatchNorm1d(512),\n nn.Linear(512, 256),\n self.act()\n )\n self.output_fc.apply(init_weights)\n\n def train_step(self, data, optimizer):\n losses = self.forward_train(data)\n loss, log_vars = self._parse_losses(losses)\n outputs = dict(\n loss=loss, log_vars=log_vars, num_samples=data['img'].shape[0])\n return outputs\n \n @force_fp32()\n def forward_train(self, batch):\n target_point = batch['target_point'].to(dtype=torch.float32)\n command = batch['target_command']\n speed = batch['speed'].to(dtype=torch.float32).view(-1,1) / 12.\n state = [speed, target_point, command, batch['target_command_raw']]\n # extract all modal features\n points = batch['points'] if 'points' in batch else None\n cam_feat, lidar_feat, measurement_feat = self.extract_sensor_feat(\n img=batch['img'], \n state=state, \n img_metas=batch['img_metas'],\n points=points)\n \n flattend_BEV_feat, fusion_feats, mid_BEV_feature, mid_lidar_feat = self.get_fusion_feat(cam_feat, lidar_feat)\n \n teacher_forcing_input = {}\n for teacher_key in [\"waypoints\", \"action_sigma\", \"action_mu\", \"future_action_sigma\", \"future_action_mu\"]:\n teacher_forcing_input[teacher_key] = batch[teacher_key]\n\n ## Decoder\n pred = self.decoder(flattend_BEV_feat, fusion_feats, measurement_feat, target_point, self, teacher_forcing_input, [cam_feat[\"lidar2img\"], cam_feat[\"ida_mat\"], cam_feat[\"fpn_feats\"], mid_lidar_feat])\n loss_dict = self.decoder.loss(batch, pred, mid_BEV_feature)\n\n ## seg supervision\n if self.use_seg:\n seg_gt = self.get_downsampled_gt_seg(batch['seg'])\n seg_pred = cam_feat['seg']\n seg_loss = self.seg_loss_func(seg_pred, seg_gt)\n loss_dict['seg_loss'] = seg_loss * 10\n \n ## depth supervision\n if self.use_depth:\n depth_gt = batch['depth']\n depth_gt = self.get_downsampled_gt_depth(depth_gt)\n depth_pred = cam_feat['depth'].permute(0, 2, 3, 1).contiguous().view(\n -1, self.depth_channels)\n fg_mask = torch.max(depth_gt, dim=1).values > 0.0\n depth_loss = F.binary_cross_entropy_with_logits(\n depth_pred[fg_mask],\n depth_gt[fg_mask],\n reduction='none',\n ).sum() / max(1.0, fg_mask.sum())\n loss_dict['depth_loss'] = depth_loss\n return loss_dict\n \n ## For close-loop evaluation\n def forward_inference(self, batch):\n self.epoch = 10000\n target_point = batch['target_point'].to(dtype=torch.float32)\n command = batch['target_command']\n speed = batch['speed'].to(dtype=torch.float32).view(-1,1) / 12.\n state = [speed, target_point, command, batch['target_command_raw']]\n # extract all modal features\n points = batch['points'] if 'points' in batch else None\n cam_feat, lidar_feat, measurement_feat = self.extract_sensor_feat(\n img=batch['img'], \n state=state, \n img_metas=batch['img_metas'],\n points=points)\n flattend_BEV_feat, fusion_feats, mid_feature, mid_lidar_feat = self.get_fusion_feat(cam_feat, lidar_feat)\n teacher_forcing_data = None\n pred = self.decoder(flattend_BEV_feat, fusion_feats, measurement_feat, target_point, self, teacher_forcing_data, [cam_feat[\"lidar2img\"], cam_feat[\"ida_mat\"], cam_feat[\"fpn_feats\"], mid_lidar_feat])\n return pred\n\n @auto_fp16()\n def get_fusion_feat(self, cam_feat, lidar_feat,):\n lidar_feat_with_high_resolution = lidar_feat[0].clone()\n #fusion\n cam_feats_reduce = self.inplace_act(self.conv_cam(cam_feat['bev']) + cam_feat['bev']) # Residual Connection\n if self.lidar_encoder is not None:\n pts_feats_reduce = self.conv_lidar(lidar_feat[0])\n bev_feats = self.inplace_act(self.conv_fusion(torch.cat([cam_feats_reduce, pts_feats_reduce], dim=1)) + cam_feats_reduce + pts_feats_reduce) # Residual Connection Bx256x21x21\n else:\n bev_feats = cam_feats_reduce # Bx256x21x21\n \n mid_feature = [None, None] #Currently no feature for 8x94x94 16x45x45\n feats32_21_21 = self.MLP21(self.inplace_act(self._256_to_32(bev_feats)))\n \n bev_feats = feats32_21_21\n mid_feature.append(feats32_21_21)\n feats64_10_10 = self.MLP10(self.inplace_act(self.conv21_10(feats32_21_21)))\n mid_feature.append(feats64_10_10)\n feats128_4_4 = self.MLP4(self.inplace_act(self.conv10_4(feats64_10_10)))\n mid_feature.append(feats128_4_4)\n feats256_2_2 = self.MLP2(self.inplace_act(self.conv4_2((feats128_4_4))))\n mid_feature.append(feats256_2_2)\n flattend_BEV_feat = self.output_fc(feats256_2_2.flatten(start_dim=1)) # Bx256\n return flattend_BEV_feat, bev_feats, mid_feature, lidar_feat_with_high_resolution\n\n @auto_fp16()\n def extract_sensor_feat(self, img, state, img_metas, points, is_train=True):\n # extract camera feature\n cam_feat = self.img_encoder(img=img, img_metas=img_metas, is_return_depth=self.use_depth and is_train)\n cam_feat['bev'] = torch.rot90(torch.flip(cam_feat['bev'],dims=[2]), 1, dims=[2,3]) ### Match with Roach BEV\n state = torch.cat(state[:3], dim=-1)\n measurement_feat = self.measurements_encoder(state)\n lidar_feat = self.lidar_encoder(points[:, -1, ...])\n for i in range(len(lidar_feat)):\n lidar_feat[i] = torch.rot90(torch.flip(lidar_feat[i],dims=[2]), 1, dims=[2,3]) ### Match with Roach BEV\n ## Visualization of BEV feature\n if False:\n self.plot(cam_feat['bev'], lidar_feat[0], img_metas[0])\n return cam_feat, lidar_feat, measurement_feat\n\n def plot(self, cam_feats, fusion_feats, img_metas):\n def get_plot(arr):\n arr2 = arr.cpu().detach().numpy()\n return np.max(arr2,axis=0)\n for i in [0]:\n plt.clf()\n plt.subplot(2,1,1)\n plt.imshow(get_plot(cam_feats[i]))#[::-1])\n plt.title('cam feature')\n plt.subplot(2,1,2)\n plt.imshow(get_plot(fusion_feats[i]))#[::-1])\n plt.title('lidar feature')\n plt.suptitle('x=[-8.0, 30.4], y=[-19.2, 19.2]')\n plt.savefig('cam_fusion_'+str(i)+'.png')\n\n @force_fp32()\n def process_action(self, pred, command, speed, target_point):\n action = self._get_action_beta(pred['mu_branches'][:, -1, :].view(1,2), pred['sigma_branches'][:, -1, :].view(1,2))\n acc, steer = action.cpu().numpy()[0].astype(np.float64)\n if acc >= 0.0:\n throttle = acc\n brake = 0.0\n else:\n throttle = 0.0\n brake = np.abs(acc)\n throttle = np.clip(throttle, 0, 1)\n steer = np.clip(steer, -1, 1)\n brake = np.clip(brake, 0, 1)\n metadata = {\n 'speed': float(speed.cpu().numpy().astype(np.float64)),\n 'steer': float(steer),\n 'throttle': float(throttle),\n 'brake': float(brake),\n 'command': command,\n 'target_point': target_point,\n }\n return steer, throttle, brake, metadata\n\n @force_fp32()\n def _get_action_beta(self, alpha, beta):\n x = torch.zeros_like(alpha)\n x[:, 1] += 0.5\n mask1 = (alpha > 1) & (beta > 1)\n x[mask1] = (alpha[mask1]-1)/(alpha[mask1]+beta[mask1]-2)\n mask2 = (alpha <= 1) & (beta > 1)\n x[mask2] = 0.0\n mask3 = (alpha > 1) & (beta <= 1)\n x[mask3] = 1.0\n # mean\n mask4 = (alpha <= 1) & (beta <= 1)\n x[mask4] = alpha[mask4]/torch.clamp((alpha[mask4]+beta[mask4]), min=1e-5)\n x = x * 2 - 1\n return x\n \n ## Copy from TCP\n ## Modified by Xiaosong Jia\n @force_fp32()\n def control_pid(self, waypoints, velocity, target, stuck_desired_speed=-1):\n ''' Predicts vehicle control with a PID controller.\n Args:\n waypoints (tensor): output of self.plan()\n velocity (tensor): speedometer input\n '''\n assert(waypoints.size(0)==1)\n waypoints = waypoints[0].data.cpu().numpy()\n saved_waypoints = waypoints.copy()\n saved_target = target.copy()\n waypoints = waypoints[:, ::-1]\n target = target[::-1]\n\n # iterate over vectors between predicted waypoints\n num_pairs = len(waypoints) - 1\n best_norm = 1e5\n desired_speed = 0\n aim = waypoints[0]\n for i in range(num_pairs):\n # magnitude of vectors, used for speed\n desired_speed += np.linalg.norm(\n waypoints[i+1] - waypoints[i]) * 2.0 / num_pairs\n\n # norm of vector midpoints, used for steering\n norm = np.linalg.norm((waypoints[i+1] + waypoints[i]) / 2.0)\n if abs(self.config['aim_dist']-best_norm) > abs(self.config['aim_dist']-norm):\n aim = waypoints[i]\n best_norm = norm\n desired_speed = desired_speed.astype(np.float64)\n if stuck_desired_speed > 0:\n desired_speed = stuck_desired_speed\n aim_last = waypoints[-1] - waypoints[-2]\n\n angle = np.degrees(np.pi / 2 - np.arctan2(aim[1], aim[0])) / 90\n angle_last = np.degrees(np.pi / 2 - np.arctan2(aim_last[1], aim_last[0])) / 90\n angle_target = np.degrees(np.pi / 2 - np.arctan2(target[1], target[0])) / 90\n\n # choice of point to aim for steering, removing outlier predictions\n # use target point if it has a smaller angle or if error is large\n # predicted point otherwise\n # (reduces noise in eg. straight roads, helps with sudden turn commands)\n use_target_to_aim = np.abs(angle_target) < np.abs(angle)\n #angle_thresh \n use_target_to_aim = use_target_to_aim or (np.abs(angle_target-angle_last) > self.config['angle_thresh'] and target[1] < self.config['dist_thresh'])\n if use_target_to_aim:\n angle_final = angle_target\n else:\n angle_final = angle\n angle_final = angle_final.astype(np.float64)\n speed = velocity[0].data.cpu().numpy()\n if (speed < 0.01):\n angle_final = 0.0 # When we don't move we don't want the angle error to accumulate in the integral\n\n steer = self.turn_controller.step(angle_final)\n steer = np.clip(steer, -1.0, 1.0)\n\n brake = desired_speed < self.config['brake_speed'] or (speed / desired_speed) > self.config['brake_ratio']\n\n delta = np.clip(desired_speed - speed, 0.0, self.config['clip_delta'])\n throttle = self.speed_controller.step(delta)\n throttle = np.clip(throttle, 0.0, 1.0)#self.config['max_throttle'])\n throttle = throttle if not brake else 0.0\n\n metadata = {\n 'speed': float(speed.astype(np.float64)),\n 'steer': float(steer),\n 'throttle': float(throttle),\n 'brake': float(brake),\n 'wp_4': tuple(saved_waypoints[3].astype(np.float64)),\n 'wp_3': tuple(saved_waypoints[2].astype(np.float64)),\n 'wp_2': tuple(saved_waypoints[1].astype(np.float64)),\n 'wp_1': tuple(saved_waypoints[0].astype(np.float64)),\n 'aim': tuple(aim.astype(np.float64)),\n 'target': tuple(saved_target.astype(np.float64)),\n 'desired_speed': float(desired_speed),\n 'angle': float(angle.astype(np.float64)),\n 'angle_last': float(angle_last.astype(np.float64)),\n 'angle_target': float(angle_target.astype(np.float64)),\n 'angle_final': float(angle_final),\n 'delta': float(delta.astype(np.float64)),\n }\n return steer, throttle, brake, metadata\n\n @force_fp32()\n def forward(self, is_eval=True, **kwargs):\n \"\"\"Calls either forward_train or forward_test depending on whether\n return_loss=True.\n Note this setting will change the expected inputs. When\n `return_loss=True`, img and img_metas are single-nested (i.e.\n torch.Tensor and list[dict]), and when `resturn_loss=False`, img and\n img_metas should be double nested (i.e. list[torch.Tensor],\n list[list[dict]]), with the outer list indicating test time\n augmentations.\n \"\"\"\n losses = self.forward_train(kwargs)\n loss, log_vars = self._parse_losses(losses)\n outputs = dict(\n loss=loss, log_vars=log_vars, num_samples=kwargs['img'].shape[0])\n return outputs\n \n @force_fp32()\n def _parse_losses(self, losses):\n \"\"\"Parse the raw outputs (losses) of the network.\n\n Args:\n losses (dict): Raw output of the network, which usually contain\n losses and other necessary infomation.\n\n Returns:\n tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \\\n which may be a weighted sum of all losses, log_vars contains \\\n all the variables to be sent to the logger.\n \"\"\"\n log_vars = OrderedDict()\n for loss_name, loss_value in losses.items():\n if isinstance(loss_value, torch.Tensor):\n log_vars[loss_name] = loss_value.mean()\n elif isinstance(loss_value, list):\n log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)\n else:\n raise TypeError(\n f'{loss_name} is not a tensor or list of tensors')\n loss = sum(_value for _key, _value in log_vars.items()\n if 'loss' in _key)\n log_vars['loss'] = loss\n for loss_name, loss_value in log_vars.items():\n # reduce loss when distributed training\n if dist.is_available() and dist.is_initialized():\n loss_value = loss_value.data.clone()\n dist.all_reduce(loss_value.div_(dist.get_world_size()))\n log_vars[loss_name] = loss_value.item()\n return loss, log_vars\n \n @force_fp32()\n def get_downsampled_gt_depth(self, gt_depths, min_pooling=True):\n \"\"\"\n Input:\n gt_depths: [B, N, H, W]\n Output:\n gt_depths: [B*N*h*w, d]\n \"\"\"\n B, N, H, W = gt_depths.shape\n if min_pooling:\n gt_depths = gt_depths.view(\n B * N,\n H // self.downsample_factor,\n self.downsample_factor,\n W // self.downsample_factor,\n self.downsample_factor,\n 1,\n )\n gt_depths = gt_depths.permute(0, 1, 3, 5, 2, 4).contiguous()\n gt_depths = gt_depths.view(\n -1, self.downsample_factor * self.downsample_factor)\n gt_depths_tmp = torch.where(gt_depths == 0.0,\n 1e5 * torch.ones_like(gt_depths),\n gt_depths)\n gt_depths = torch.min(gt_depths_tmp, dim=-1).values\n gt_depths = gt_depths.view(B * N, H // self.downsample_factor,\n W // self.downsample_factor)\n else: # just use nearest sampling\n if self.depth_resize is None:\n self.depth_resize = T.Resize((H//self.downsample_factor,W//self.downsample_factor), interpolation=T.InterpolationMode.NEAREST)\n gt_depths = gt_depths.view(B*N, H, W)\n gt_depths = self.depth_resize(gt_depths)\n gt_depths = (gt_depths -\n (self.dbound[0] - self.dbound[2])) / self.dbound[2]\n gt_depths = torch.where(\n (gt_depths < self.depth_channels + 1) & (gt_depths >= 0.0),\n gt_depths, torch.zeros_like(gt_depths))\n gt_depths = F.one_hot(gt_depths.long(),\n num_classes=self.depth_channels + 1).view(\n -1, self.depth_channels + 1)[:, 1:]\n return gt_depths.float()\n\n @force_fp32()\n def get_downsampled_gt_seg(self, gt_seg):\n B, N, H, W = gt_seg.shape\n if self.depth_resize is None:\n self.depth_resize = T.Resize((H//self.seg_downsample_factor,W//self.seg_downsample_factor), interpolation=T.InterpolationMode.NEAREST)\n gt_seg = gt_seg.view(B*N, H, W)\n gt_seg = self.depth_resize(gt_seg)\n return gt_seg.long()\n\n\n" }, { "path": "open_loop_training/code/model_code/backbones/__init__.py", "content": "\nfrom .lidarnet import LidarNet, SparseEncoder_fp32\nfrom .lss import LSS, PAFPN_fp32\n__all__ = ['LidarNet', 'LSS', \"SparseEncoder_fp32\", \"PAFPN_fp32\"]" }, { "path": "open_loop_training/code/model_code/backbones/lidarnet.py", "content": "import copy\r\nimport numpy as np\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.distributions import Beta\r\n\r\nfrom mmcv.runner import BaseModule\r\nfrom mmcv.runner import force_fp32, auto_fp16\r\nfrom mmdet.models import BACKBONES\r\nfrom mmdet.core import (multi_apply, multi_apply, reduce_mean)\r\nfrom mmdet3d.models.detectors.mvx_two_stage import MVXTwoStageDetector\r\nfrom mmdet3d.models.builder import MIDDLE_ENCODERS\r\nfrom mmdet3d.models.middle_encoders.sparse_encoder import SparseEncoder\r\nfrom mmdet3d.ops.spconv import IS_SPCONV2_AVAILABLE\r\nif IS_SPCONV2_AVAILABLE:\r\n from spconv.pytorch import SparseConvTensor\r\nelse:\r\n from mmcv.ops import SparseConvTensor\r\n\r\n\r\n#To avoid strange FP16 inf norm bug\r\n@MIDDLE_ENCODERS.register_module()\r\nclass SparseEncoder_fp32(SparseEncoder):\r\n \r\n @force_fp32()\r\n def forward(self, voxel_features, coors, batch_size):\r\n \"\"\"Forward of SparseEncoder.\r\n\r\n Args:\r\n voxel_features (torch.Tensor): Voxel features in shape (N, C).\r\n coors (torch.Tensor): Coordinates in shape (N, 4),\r\n the columns in the order of (batch_idx, z_idx, y_idx, x_idx).\r\n batch_size (int): Batch size.\r\n\r\n Returns:\r\n dict: Backbone features.\r\n \"\"\"\r\n coors = coors.int()\r\n input_sp_tensor = SparseConvTensor(voxel_features, coors,\r\n self.sparse_shape, batch_size)\r\n x = self.conv_input(input_sp_tensor)\r\n\r\n encode_features = []\r\n for encoder_layer in self.encoder_layers:\r\n x = encoder_layer(x)\r\n encode_features.append(x)\r\n\r\n # for detection head\r\n # [200, 176, 5] -> [200, 176, 2]\r\n out = self.conv_out(encode_features[-1])\r\n spatial_features = out.dense()\r\n\r\n N, C, D, H, W = spatial_features.shape\r\n spatial_features = spatial_features.view(N, C * D, H, W)\r\n\r\n return spatial_features\r\n\r\n\r\n@BACKBONES.register_module()\r\nclass LidarNet(MVXTwoStageDetector):\r\n def __init__(self,\r\n bev_h=None,\r\n bev_w=None,\r\n pts_voxel_layer=None,\r\n pts_voxel_encoder=None,\r\n pts_middle_encoder=None,\r\n pts_fusion_layer=None,\r\n pts_backbone=None,\r\n pts_neck=None,\r\n pts_bbox_head=None,\r\n train_cfg=None,\r\n test_cfg=None,\r\n ):\r\n self.bev_h = bev_h\r\n self.bev_w = bev_w\r\n super(LidarNet,\r\n self).__init__(pts_voxel_layer, pts_voxel_encoder,\r\n pts_middle_encoder, pts_fusion_layer,\r\n None, pts_backbone, None, pts_neck,\r\n pts_bbox_head, None, None,\r\n train_cfg, test_cfg, None)\r\n self.fp16_enabled = False\r\n \r\n @auto_fp16()\r\n def forward(self, pts):\r\n voxels, num_points, coors = self.voxelize(pts)\r\n voxel_features = self.pts_voxel_encoder(voxels, num_points, coors)\r\n batch_size = coors[-1, 0] + 1\r\n x = self.pts_middle_encoder(voxel_features, coors, batch_size) \r\n if self.with_pts_backbone:\r\n x = self.pts_backbone(x)\r\n if self.with_pts_neck:\r\n x = self.pts_neck(x)\r\n return x\r\n\r\n\r\n" }, { "path": "open_loop_training/code/model_code/backbones/lss.py", "content": "## Copyright (c) Megvii Inc. All rights reserved.\n## From BEVDepth\n## Modified by Xiaosong Jia\nimport torch\nimport torch.nn.functional as F\nfrom mmcv.runner import BaseModule\nfrom mmdet.models import BACKBONES\nfrom mmcv.cnn import build_conv_layer\nfrom mmdet3d.models import build_neck\nfrom mmdet.models import build_backbone\nfrom mmdet.models.backbones.resnet import BasicBlock\nfrom torch import nn\nimport numpy as np\nfrom ops.voxel_pooling import voxel_pooling\nfrom mmcv.runner import force_fp32, auto_fp16\n\n__all__ = ['LSS']\n\n\nclass _ASPPModule(nn.Module):\n def __init__(self, inplanes, planes, kernel_size, padding, dilation,\n BatchNorm):\n super(_ASPPModule, self).__init__()\n self.atrous_conv = nn.Conv2d(inplanes,\n planes,\n kernel_size=kernel_size,\n stride=1,\n padding=padding,\n dilation=dilation,\n bias=False)\n self.bn = BatchNorm(planes)\n self.relu = nn.ReLU()\n self._init_weight()\n\n def forward(self, x):\n x = self.atrous_conv(x)\n x = self.bn(x)\n return self.relu(x)\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\nclass ASPP(nn.Module):\n def __init__(self, inplanes, mid_channels=256, BatchNorm=nn.BatchNorm2d):\n super(ASPP, self).__init__()\n dilations = [1, 6, 12, 18]\n\n self.aspp1 = _ASPPModule(inplanes,\n mid_channels,\n 1,\n padding=0,\n dilation=dilations[0],\n BatchNorm=BatchNorm)\n self.aspp2 = _ASPPModule(inplanes,\n mid_channels,\n 3,\n padding=dilations[1],\n dilation=dilations[1],\n BatchNorm=BatchNorm)\n self.aspp3 = _ASPPModule(inplanes,\n mid_channels,\n 3,\n padding=dilations[2],\n dilation=dilations[2],\n BatchNorm=BatchNorm)\n self.aspp4 = _ASPPModule(inplanes,\n mid_channels,\n 3,\n padding=dilations[3],\n dilation=dilations[3],\n BatchNorm=BatchNorm)\n\n self.global_avg_pool = nn.Sequential(\n nn.AdaptiveAvgPool2d((1, 1)),\n nn.Conv2d(inplanes, mid_channels, 1, stride=1, bias=False),\n BatchNorm(mid_channels),\n nn.ReLU(),\n )\n self.conv1 = nn.Conv2d(int(mid_channels * 5),\n mid_channels,\n 1,\n bias=False)\n self.bn1 = BatchNorm(mid_channels)\n self.relu = nn.ReLU()\n self.dropout = nn.Dropout(0.5)\n self._init_weight()\n\n def forward(self, x):\n x1 = self.aspp1(x)\n x2 = self.aspp2(x)\n x3 = self.aspp3(x)\n x4 = self.aspp4(x)\n x5 = self.global_avg_pool(x)\n x5 = F.interpolate(x5,\n size=x4.size()[2:],\n mode='bilinear',\n align_corners=True)\n x = torch.cat((x1, x2, x3, x4, x5), dim=1)\n\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n\n return self.dropout(x)\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\nclass Mlp(nn.Module):\n def __init__(self,\n in_features,\n hidden_features=None,\n out_features=None,\n act_layer=nn.ReLU,\n drop=0.0):\n super().__init__()\n out_features = out_features or in_features\n hidden_features = hidden_features or in_features\n self.fc1 = nn.Linear(in_features, hidden_features)\n self.act = act_layer()\n self.drop1 = nn.Dropout(drop)\n self.fc2 = nn.Linear(hidden_features, out_features)\n self.drop2 = nn.Dropout(drop)\n\n def forward(self, x):\n x = self.fc1(x)\n x = self.act(x)\n x = self.drop1(x)\n x = self.fc2(x)\n x = self.drop2(x)\n return x\n\n\nclass SELayer(nn.Module):\n def __init__(self, channels, act_layer=nn.ReLU, gate_layer=nn.Sigmoid):\n super().__init__()\n self.conv_reduce = nn.Conv2d(channels, channels, 1, bias=True)\n self.act1 = act_layer()\n self.conv_expand = nn.Conv2d(channels, channels, 1, bias=True)\n self.gate = gate_layer()\n\n def forward(self, x, x_se):\n x_se = self.conv_reduce(x_se)\n x_se = self.act1(x_se)\n x_se = self.conv_expand(x_se)\n return x * self.gate(x_se)\n\n\nclass DepthNet(nn.Module):\n def __init__(self, in_channels, mid_channels, context_channels,\n depth_channels):\n super(DepthNet, self).__init__()\n self.reduce_conv = nn.Sequential(\n nn.Conv2d(in_channels,\n mid_channels,\n kernel_size=3,\n stride=1,\n padding=1),\n nn.BatchNorm2d(mid_channels),\n nn.ReLU(inplace=True),\n )\n self.context_conv = nn.Conv2d(mid_channels,\n context_channels,\n kernel_size=1,\n stride=1,\n padding=0)\n self.bn = nn.BatchNorm1d(22)\n self.depth_mlp = Mlp(22, mid_channels, mid_channels)\n self.depth_se = SELayer(mid_channels) # NOTE: add camera-aware\n self.context_mlp = Mlp(22, mid_channels, mid_channels)\n self.context_se = SELayer(mid_channels) # NOTE: add camera-aware\n self.depth_conv = nn.Sequential(\n BasicBlock(mid_channels, mid_channels),\n BasicBlock(mid_channels, mid_channels),\n BasicBlock(mid_channels, mid_channels),\n ASPP(mid_channels, mid_channels),\n build_conv_layer(cfg=dict(\n type='DCN',\n in_channels=mid_channels,\n out_channels=mid_channels,\n kernel_size=3,\n padding=1,\n groups=4,\n im2col_step=128,\n )),\n nn.Conv2d(mid_channels,\n depth_channels,\n kernel_size=1,\n stride=1,\n padding=0),\n )\n\n def forward(self, x, mats_dict): # mats_dict B x T x N\n intrins = mats_dict['intrin_mats'][:, -1:, ..., :3, :3]\n batch_size = intrins.shape[0]\n num_cams = intrins.shape[2]\n ida = mats_dict['ida_mats'][:, -1:, ...]\n sensor2ego = mats_dict['sensor2ego_mats'][:, -1:, ..., :3, :]\n mlp_input = torch.cat(\n [\n torch.stack(\n [\n intrins[:, 0:1, ..., 0, 0],\n intrins[:, 0:1, ..., 1, 1],\n intrins[:, 0:1, ..., 0, 2],\n intrins[:, 0:1, ..., 1, 2],\n ida[:, 0:1, ..., 0, 0],\n ida[:, 0:1, ..., 0, 1],\n ida[:, 0:1, ..., 0, 3],\n ida[:, 0:1, ..., 1, 0],\n ida[:, 0:1, ..., 1, 1],\n ida[:, 0:1, ..., 1, 3],\n ],\n dim=-1,\n ),\n sensor2ego.view(batch_size, 1, num_cams, -1),\n ],\n -1,\n )\n mlp_input = self.bn(mlp_input.reshape(-1, mlp_input.shape[-1]))\n x = self.reduce_conv(x)\n context_se = self.context_mlp(mlp_input)[..., None, None]\n context = self.context_se(x, context_se)\n context = self.context_conv(context)\n depth_se = self.depth_mlp(mlp_input)[..., None, None]\n depth = self.depth_se(x, depth_se)\n depth = self.depth_conv(depth)\n return torch.cat([depth, context], dim=1)\n\n\nclass UnetLayer(nn.Module):\n def __init__(self, in_channels, middle_channels, out_channels):\n super(UnetLayer, self).__init__()\n self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)\n self.conv_relu = nn.Sequential(\n nn.Conv2d(middle_channels, out_channels, kernel_size=3, padding=1),\n nn.ReLU(inplace=True),\n )\n self.fp16_enabled = False\n \n @force_fp32()\n def forward(self, x1, x2):\n x1 = self.up(x1)\n x1 = torch.cat((x1, x2), dim=1)\n x1 = self.conv_relu(x1)\n return x1\n\nclass UNet(nn.Module):\n def __init__(self, n_class, fpn_in_channels):\n super().__init__()\n self.unet_layer4 = UnetLayer(fpn_in_channels[3], 256+fpn_in_channels[2], 256)\n self.unet_layer3 = UnetLayer(256, 256+fpn_in_channels[1], 256)\n self.unet_layer2 = UnetLayer(256, 128+fpn_in_channels[0], 128)\n self.unet_layer0 = nn.Sequential(\n nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True),\n nn.Conv2d(128, 64, kernel_size=3, padding=1, bias=False),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, padding=1, bias=False)\n )\n self.conv_last = nn.Conv2d(64, n_class, 1)\n self.fp16_enabled = False\n \n def forward(self, input):\n e1, e2, e3, e4 = input # input resnet's feature directly\n d4 = self.unet_layer4(e4, e3) \n d3 = self.unet_layer3(d4, e2) \n d2 = self.unet_layer2(d3, e1) \n d0 = self.unet_layer0(d2) \n out = self.conv_last(d0) # 1/2 origin shape\n return out\n\nfrom mmdet.models.necks.pafpn import PAFPN \nfrom mmdet.models import NECKS \n@NECKS.register_module()\nclass PAFPN_fp32(PAFPN):\n @force_fp32()\n def forward(self, inputs):\n \"\"\"Forward function.\"\"\"\n assert len(inputs) == len(self.in_channels)\n\n # build laterals\n laterals = [\n lateral_conv(inputs[i + self.start_level])\n for i, lateral_conv in enumerate(self.lateral_convs)\n ]\n\n # build top-down path\n used_backbone_levels = len(laterals)\n for i in range(used_backbone_levels - 1, 0, -1):\n prev_shape = laterals[i - 1].shape[2:]\n # fix runtime error of \"+=\" inplace operation in PyTorch 1.10\n laterals[i - 1] = laterals[i - 1] + F.interpolate(\n laterals[i], size=prev_shape, mode='nearest')\n\n # build outputs\n # part 1: from original levels\n inter_outs = [\n self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)\n ]\n\n # part 2: add bottom-up path\n for i in range(0, used_backbone_levels - 1):\n inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i])\n\n outs = []\n outs.append(inter_outs[0])\n outs.extend([\n self.pafpn_convs[i - 1](inter_outs[i])\n for i in range(1, used_backbone_levels)\n ])\n\n # part 3: add extra levels\n if self.num_outs > len(outs):\n # use max pool to get more levels on top of outputs\n # (e.g., Faster R-CNN, Mask R-CNN)\n if not self.add_extra_convs:\n for i in range(self.num_outs - used_backbone_levels):\n outs.append(F.max_pool2d(outs[-1], 1, stride=2))\n # add conv layers on top of original feature maps (RetinaNet)\n else:\n if self.add_extra_convs == 'on_input':\n orig = inputs[self.backbone_end_level - 1]\n outs.append(self.fpn_convs[used_backbone_levels](orig))\n elif self.add_extra_convs == 'on_lateral':\n outs.append(self.fpn_convs[used_backbone_levels](\n laterals[-1]))\n elif self.add_extra_convs == 'on_output':\n outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))\n else:\n raise NotImplementedError\n for i in range(used_backbone_levels + 1, self.num_outs):\n if self.relu_before_extra_convs:\n outs.append(self.fpn_convs[i](F.relu(outs[-1])))\n else:\n outs.append(self.fpn_convs[i](outs[-1]))\n return tuple(outs)\n\n\n@BACKBONES.register_module()\nclass LSS(BaseModule):\n def __init__(self, x_bound, y_bound, z_bound, d_bound, final_dim,\n downsample_factor, output_channels, img_backbone_conf,\n img_neck_conf, depth_net_conf, seg_net_conf=None, queue_len=1, fpn_in_channels=[64, 128, 256, 512]):\n \"\"\"Modified from `https://github.com/nv-tlabs/lift-splat-shoot`.\n Args:\n x_bound (list): Boundaries for x.\n y_bound (list): Boundaries for y.\n z_bound (list): Boundaries for z.\n d_bound (list): Boundaries for d.\n final_dim (list): Dimension for input images.\n downsample_factor (int): Downsample factor between feature map\n and input image.\n output_channels (int): Number of channels for the output\n feature map.\n img_backbone_conf (dict): Config for image backbone.\n img_neck_conf (dict): Config for image neck.\n depth_net_conf (dict): Config for depth net.\n \"\"\"\n\n super(LSS, self).__init__()\n self.downsample_factor = downsample_factor\n self.fp16_enabled = False\n self.d_bound = d_bound\n self.final_dim = final_dim\n self.output_channels = output_channels\n self.queue_len = queue_len\n if self.queue_len!=1:\n self.bev_multiframe_merge = nn.Conv2d(256*queue_len,\n 256,\n kernel_size=3,\n stride=1,\n padding=1,\n bias=False)\n self.register_buffer(\n 'voxel_size',\n torch.Tensor([row[2] for row in [x_bound, y_bound, z_bound]]))\n self.register_buffer(\n 'voxel_coord',\n torch.Tensor([\n row[0] + row[2] / 2.0 for row in [x_bound, y_bound, z_bound]\n ]))\n self.register_buffer(\n 'voxel_num',\n torch.LongTensor([(row[1] - row[0]) / row[2]\n for row in [x_bound, y_bound, z_bound]]))\n self.register_buffer('frustum', self.create_frustum())\n self.depth_channels, _, _, _ = self.frustum.shape\n\n self.img_backbone = build_backbone(img_backbone_conf)\n self.img_neck = build_neck(img_neck_conf)\n self.neck_conv = nn.Conv2d(in_channels=img_neck_conf[\"out_channels\"], out_channels=depth_net_conf[\"in_channels\"], kernel_size=1)\n self.depth_net = self._configure_depth_net(depth_net_conf)\n\n\n self.seg_net = self._configure_seg_net(seg_net_conf, fpn_in_channels)\n \n self.seg_res_to_image_feature =nn.Sequential(\n nn.Conv2d(seg_net_conf['out_channels'], 64, 1),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n\n nn.Conv2d(64, 16, 1),\n nn.BatchNorm2d(16),\n nn.ReLU(),\n\n nn.Conv2d(16, 32, 3, padding=1, stride=2),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n\n\n nn.Conv2d(32, 32, 1),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n \n nn.Conv2d(32, 64, 3, padding=1, stride=2),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n\n nn.Conv2d(64, 64, 1),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n\n nn.Conv2d(64, 128, 3, padding=1, stride=2),\n nn.BatchNorm2d(128),\n nn.ReLU(),\n )\n self.merge_seg_and_image = nn.Conv2d(256+128, 256, 3, padding=1)\n self.img_neck.init_weights()\n self.img_backbone.init_weights()\n\n def _configure_depth_net(self, depth_net_conf):\n return DepthNet(\n depth_net_conf['in_channels'],\n depth_net_conf['mid_channels'],\n self.output_channels,\n self.depth_channels,\n )\n\n def _configure_seg_net(self, seg_net_conf, fpn_in_channels):\n return UNet(seg_net_conf['out_channels'], fpn_in_channels)\n\n def create_frustum(self):\n \"\"\"Generate frustum\"\"\"\n # make grid in image plane\n ogfH, ogfW = self.final_dim\n fH, fW = ogfH // self.downsample_factor, ogfW // self.downsample_factor\n d_coords = torch.arange(*self.d_bound,\n dtype=torch.float).view(-1, 1,\n 1).expand(-1, fH, fW)\n D, _, _ = d_coords.shape\n x_coords = torch.linspace(0, ogfW - 1, fW, dtype=torch.float).view(\n 1, 1, fW).expand(D, fH, fW)\n y_coords = torch.linspace(0, ogfH - 1, fH,\n dtype=torch.float).view(1, fH,\n 1).expand(D, fH, fW)\n paddings = torch.ones_like(d_coords)\n # D x H x W x 3\n frustum = torch.stack((x_coords, y_coords, d_coords, paddings), -1)\n return frustum\n\n @force_fp32()\n def get_geometry(self, sensor2ego_mat, intrin_mat, ida_mat, bda_mat):\n \"\"\"Transfer points from camera coord to ego coord.\n\n Args:\n rots(Tensor): Rotation matrix from camera to ego.\n trans(Tensor): Translation matrix from camera to ego.\n intrins(Tensor): Intrinsic matrix.\n post_rots_ida(Tensor): Rotation matrix for ida.\n post_trans_ida(Tensor): Translation matrix for ida\n post_rot_bda(Tensor): Rotation matrix for bda.\n\n Returns:\n Tensors: points ego coord.\n \"\"\"\n batch_size, num_cams, _, _ = sensor2ego_mat.shape\n\n # undo post-transformation\n # B x N x D x H x W x 3\n points = self.frustum\n ida_mat = ida_mat.view(batch_size, num_cams, 1, 1, 1, 4, 4)\n ## ida_mat = aug_img2img\n ## img2aug_img\n points = ida_mat.inverse().matmul(points.unsqueeze(-1)) # B,N,D,H,W,4,1\n # cam_to_ego\n points = torch.cat(\n (points[:, :, :, :, :, :2] * points[:, :, :, :, :, 2:3], # x/y * z \n points[:, :, :, :, :, 2:]), 5)\n \n combine = sensor2ego_mat.matmul(torch.inverse(intrin_mat))\n # cam2lidar @ img2cam --> img2lidar\n points = combine.view(batch_size, num_cams, 1, 1, 1, 4,\n 4).matmul(points)\n if bda_mat is not None:\n bda_mat = bda_mat.unsqueeze(1).repeat(1, num_cams, 1, 1).view(\n batch_size, num_cams, 1, 1, 1, 4, 4)\n points = (bda_mat @ points).squeeze(-1)\n else:\n points = points.squeeze(-1)\n return points[..., :3]\n\n @auto_fp16()\n def get_cam_feats(self, imgs):\n \"\"\"Get feature maps from images.\"\"\"\n batch_size, num_sweeps, num_cams, num_channels, imH, imW = imgs.shape\n imgs = imgs.flatten().view(batch_size * num_sweeps * num_cams,\n num_channels, imH, imW)\n fpn_feats = self.img_backbone(imgs)\n fpn_feats = self.img_neck(fpn_feats)\n\n img_feats = self.neck_conv_fp32(fpn_feats[2])\n img_feats = img_feats.reshape(batch_size, num_sweeps, num_cams,\n img_feats.shape[1], img_feats.shape[2],\n img_feats.shape[3])\n return img_feats, fpn_feats\n\n @force_fp32()\n def neck_conv_fp32(self, input):\n return self.neck_conv(input)\n\n @force_fp32()\n def _forward_depth_net(self, feat, mats_dict):\n return self.depth_net(feat, mats_dict)\n \n @auto_fp16()\n def _forward_voxel_net(self, img_feat_with_depth):\n return img_feat_with_depth\n\n @auto_fp16()\n def _forward_single_sweep(self,\n sweep_index,\n sweep_imgs,\n mats_dict,\n is_return_depth=False):\n \"\"\"Forward function for single sweep.\n\n Args:\n sweep_index (int): Index of sweeps.\n sweep_imgs (Tensor): Input images.\n mats_dict (dict):\n sensor2ego_mats(Tensor): Transformation matrix from\n camera to ego with shape of (B, num_sweeps,\n num_cameras, 4, 4).\n intrin_mats(Tensor): Intrinsic matrix with shape\n of (B, num_sweeps, num_cameras, 4, 4).\n ida_mats(Tensor): Transformation matrix for ida with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n sensor2sensor_mats(Tensor): Transformation matrix\n from key frame camera to sweep frame camera with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n is_return_depth (bool, optional): Whether to return depth.\n Default: False.\n\n Returns:\n Tensor: BEV feature map.\n \"\"\"\n batch_size, num_sweeps, num_cams, num_channels, img_height, \\\n img_width = sweep_imgs.shape\n\n img_feats, fpn_feats = self.get_cam_feats(sweep_imgs)\n source_features = img_feats[:, 0, ...]\n source_features = source_features.reshape(batch_size * num_cams,\n source_features.shape[2],\n source_features.shape[3],\n source_features.shape[4])\n depth_feature = self._forward_depth_net(\n source_features,\n mats_dict,\n )\n depth = depth_feature[:, :self.depth_channels]\n depth_softmax = depth.softmax(1)\n\n img_feature = depth_feature[:, self.depth_channels:(self.depth_channels + self.output_channels)]\n\n ## Map 2D Segmentation to BEV as well \n seg_output = self.seg_net(fpn_feats) # 1/2 origin shape\n seg_feature = self.seg_res_to_image_feature_forward(seg_output.detach())# 1/16 origin shape\n img_feature = torch.cat((img_feature, seg_feature), dim=1)\n img_feature = self.merge_seg_and_image(img_feature) # keep same dims\n\n img_feat_with_depth = depth_softmax.unsqueeze(1) * img_feature.unsqueeze(2)\n outs = {}\n outs[\"fpn_feats\"] = fpn_feats\n img_feat_with_depth = self._forward_voxel_net(img_feat_with_depth)\n\n img_feat_with_depth = img_feat_with_depth.reshape(\n batch_size,\n num_cams,\n img_feat_with_depth.shape[1],\n img_feat_with_depth.shape[2],\n img_feat_with_depth.shape[3],\n img_feat_with_depth.shape[4],\n )\n geom_xyz = self.get_geometry(\n mats_dict['sensor2ego_mats'][:, sweep_index, ...],\n mats_dict['intrin_mats'][:, sweep_index, ...],\n mats_dict['ida_mats'][:, sweep_index, ...],\n mats_dict.get('bda_mat', None),\n )\n\n img_feat_with_depth = img_feat_with_depth.permute(0, 1, 3, 4, 5, 2)\n\n feature_map = self.voxel_pooling_method(geom_xyz, img_feat_with_depth.contiguous(), self.voxel_num.to(img_feat_with_depth.device))\n \n outs[\"bev\"] = feature_map.contiguous()\n if is_return_depth:\n outs['depth'] = depth\n outs['seg'] = seg_output\n return outs\n \n \n @force_fp32()\n def seg_res_to_image_feature_forward(self, input):\n return self.seg_res_to_image_feature(input)\n\n @force_fp32()\n def voxel_pooling_method(self, geom_xyz, img_feat_with_depth, voxel_num):\n geom_xyz = ((geom_xyz - (self.voxel_coord - self.voxel_size / 2.0)) /\n self.voxel_size).int()\n return voxel_pooling(geom_xyz, img_feat_with_depth, voxel_num)\n\n @auto_fp16()\n def forward(self,\n img,\n img_metas,\n timestamps=None,\n is_return_depth=False):\n \"\"\"Forward function.\n Args:\n sweep_imgs(Tensor): Input images with shape of (B, num_sweeps,\n num_cameras, 3, H, W).\n mats_dict(dict):\n sensor2ego_mats(Tensor): Transformation matrix from\n camera to ego with shape of (B, num_sweeps,\n num_cameras, 4, 4).\n intrin_mats(Tensor): Intrinsic matrix with shape\n of (B, num_sweeps, num_cameras, 4, 4).\n ida_mats(Tensor): Transformation matrix for ida with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n sensor2sensor_mats(Tensor): Transformation matrix\n from key frame camera to sweep frame camera with\n shape of (B, num_sweeps, num_cameras, 4, 4).\n timestamps(Tensor): Timestamp for all images with the shape of(B,\n num_sweeps, num_cameras).\n\n Return:\n Tensor: bev feature map.\n \"\"\"\n if img.dim() == 5:\n img = img.unsqueeze(1) \n\n batch_size, num_sweeps, num_cams, num_channels, img_height, \\\n img_width = img.shape\n\n intrins, ida, sensor2ego = [], [], []\n for b in range(len(img_metas)):\n _intrins, _ida, _sensor2ego = [], [], []\n for sweep_idx in range(len(img_metas[0])):\n mats = img_metas[b][sweep_idx]\n intrinsic_mat = torch.zeros((num_cams,4,4))\n intrinsic_mat[:,:3,:3] = mats['cam_intrinsic']\n intrinsic_mat[:,3,3] = 1\n _intrins.append(intrinsic_mat)\n _ida.append(mats['ida_mats']) # Nx4x4\n _sensor2ego.append(mats['currlidar2keycam'].permute(0,2,1)) # cam2lidar\n intrins.append(torch.stack(_intrins))\n sensor2ego.append(torch.stack(_sensor2ego))\n ida.append(torch.stack(_ida))\n intrins, ida, sensor2ego = torch.stack(intrins), torch.stack(ida), torch.stack(sensor2ego)\n \n old_img_metas = img_metas\n img_metas = {}\n img_metas['intrin_mats'] = intrins.to(img.device)\n img_metas['sensor2ego_mats'] = sensor2ego.to(img.device)\n img_metas['ida_mats'] = ida.to(img.device)\n\n key_frame_res = self._forward_single_sweep(\n -1,\n img[:, -1:, ...], \n img_metas,\n is_return_depth=is_return_depth)\n\n\n outs = {}\n bev_feature_list = [key_frame_res['bev']]\n if self.seg_net is not None:\n outs['seg'] = key_frame_res['seg']\n if is_return_depth:\n outs['depth'] = key_frame_res['depth']\n outs[\"fpn_feats\"] = key_frame_res[\"fpn_feats\"]\n\n current_ida = ida[:, -1, :, :].clone() #B, 4, 4, 4\n current_lidar2img = torch.stack([_[-1][\"lidar2img\"] for _ in old_img_metas], dim=0)\n outs['lidar2img'] = current_lidar2img\n outs['ida_mat'] = current_ida\n\n # multi-frame \n for sweep_index in range(1, num_sweeps):\n with torch.no_grad():\n sweep_frame_res = self._forward_single_sweep(\n -sweep_index,\n img[:, -(sweep_index+1):-sweep_index, ...],\n img_metas,\n is_return_depth=False)\n bev_feature_list.append(sweep_frame_res['bev'])\n\n assert len(bev_feature_list)==self.queue_len, 'LSS.queue_len must be set correctly in config!'\n bev_feature = torch.cat(bev_feature_list, 1)\n if self.queue_len>1:\n bev_feature = self.bev_multiframe_merge(bev_feature)\n outs['bev'] = bev_feature\n return outs\n " }, { "path": "open_loop_training/code/model_code/dense_heads/__init__.py", "content": "from .thinktwice_decoder import ThinkTwiceDecoder\n" }, { "path": "open_loop_training/code/model_code/dense_heads/multi_scale_deformable_attn_function.py", "content": "# ---------------------------------------------\n# Copyright (c) OpenMMLab. All rights reserved.\n# ---------------------------------------------\n# Modified by Zhiqi Li\n# Modified by Xiaosong Jia\n# ---------------------------------------------\nimport torch\nfrom torch.cuda.amp import custom_bwd, custom_fwd\nfrom torch.autograd.function import Function, once_differentiable\nfrom mmcv.utils import ext_loader\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom mmcv.cnn import xavier_init, constant_init\nfrom mmcv.cnn.bricks.registry import (ATTENTION,\n TRANSFORMER_LAYER_SEQUENCE)\nfrom mmcv.cnn.bricks.transformer import TransformerLayerSequence\nimport math\nfrom mmcv.runner.base_module import BaseModule, ModuleList, Sequential\nfrom mmcv.utils import (ConfigDict, build_from_cfg, deprecated_api_warning,\n to_2tuple)\nfrom mmcv.ops.multi_scale_deform_attn import multi_scale_deformable_attn_pytorch\nfrom mmcv.utils import ext_loader\next_module = ext_loader.load_ext(\n '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])\nfrom mmcv.runner import force_fp32, auto_fp16\n\ndef inverse_sigmoid(x, eps=1e-5):\n \"\"\"Inverse function of sigmoid.\n Args:\n x (Tensor): The tensor to do the\n inverse.\n eps (float): EPS avoid numerical\n overflow. Defaults 1e-5.\n Returns:\n Tensor: The x has passed the inverse\n function of sigmoid, has same\n shape with input.\n \"\"\"\n x = x.clamp(min=0, max=1)\n x1 = x.clamp(min=eps)\n x2 = (1 - x).clamp(min=eps)\n return torch.log(x1 / x2)\n\nclass MultiScaleDeformableAttnFunction_fp16(Function):\n\n @staticmethod\n @custom_fwd(cast_inputs=torch.float16)\n def forward(ctx, value, value_spatial_shapes, value_level_start_index,\n sampling_locations, attention_weights, im2col_step):\n \"\"\"GPU version of multi-scale deformable attention.\n\n Args:\n value (Tensor): The value has shape\n (bs, num_keys, mum_heads, embed_dims//num_heads)\n value_spatial_shapes (Tensor): Spatial shape of\n each feature map, has shape (num_levels, 2),\n last dimension 2 represent (h, w)\n sampling_locations (Tensor): The location of sampling points,\n has shape\n (bs ,num_queries, num_heads, num_levels, num_points, 2),\n the last dimension 2 represent (x, y).\n attention_weights (Tensor): The weight of sampling points used\n when calculate the attention, has shape\n (bs ,num_queries, num_heads, num_levels, num_points),\n im2col_step (Tensor): The step used in image to column.\n\n Returns:\n Tensor: has shape (bs, num_queries, embed_dims)\n \"\"\"\n ctx.im2col_step = im2col_step\n output = ext_module.ms_deform_attn_forward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n im2col_step=ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes,\n value_level_start_index, sampling_locations,\n attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n @custom_bwd\n def backward(ctx, grad_output):\n \"\"\"GPU version of backward function.\n\n Args:\n grad_output (Tensor): Gradient\n of output tensor of forward.\n\n Returns:\n Tuple[Tensor]: Gradient\n of input tensors in forward.\n \"\"\"\n value, value_spatial_shapes, value_level_start_index, \\\n sampling_locations, attention_weights = ctx.saved_tensors\n grad_value = torch.zeros_like(value)\n grad_sampling_loc = torch.zeros_like(sampling_locations)\n grad_attn_weight = torch.zeros_like(attention_weights)\n\n ext_module.ms_deform_attn_backward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n grad_output.contiguous(),\n grad_value,\n grad_sampling_loc,\n grad_attn_weight,\n im2col_step=ctx.im2col_step)\n\n return grad_value, None, None, \\\n grad_sampling_loc, grad_attn_weight, None\n\n\nclass MultiScaleDeformableAttnFunction_fp32(Function):\n\n @staticmethod\n @custom_fwd(cast_inputs=torch.float32)\n def forward(ctx, value, value_spatial_shapes, value_level_start_index,\n sampling_locations, attention_weights, im2col_step):\n \"\"\"GPU version of multi-scale deformable attention.\n\n Args:\n value (Tensor): The value has shape\n (bs, num_keys, mum_heads, embed_dims//num_heads)\n value_spatial_shapes (Tensor): Spatial shape of\n each feature map, has shape (num_levels, 2),\n last dimension 2 represent (h, w)\n sampling_locations (Tensor): The location of sampling points,\n has shape\n (bs ,num_queries, num_heads, num_levels, num_points, 2),\n the last dimension 2 represent (x, y).\n attention_weights (Tensor): The weight of sampling points used\n when calculate the attention, has shape\n (bs ,num_queries, num_heads, num_levels, num_points),\n im2col_step (Tensor): The step used in image to column.\n\n Returns:\n Tensor: has shape (bs, num_queries, embed_dims)\n \"\"\"\n\n ctx.im2col_step = im2col_step\n output = ext_module.ms_deform_attn_forward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n im2col_step=ctx.im2col_step)\n ctx.save_for_backward(value, value_spatial_shapes,\n value_level_start_index, sampling_locations,\n attention_weights)\n return output\n\n @staticmethod\n @once_differentiable\n @custom_bwd\n def backward(ctx, grad_output):\n \"\"\"GPU version of backward function.\n\n Args:\n grad_output (Tensor): Gradient\n of output tensor of forward.\n\n Returns:\n Tuple[Tensor]: Gradient\n of input tensors in forward.\n \"\"\"\n value, value_spatial_shapes, value_level_start_index, \\\n sampling_locations, attention_weights = ctx.saved_tensors\n grad_value = torch.zeros_like(value)\n grad_sampling_loc = torch.zeros_like(sampling_locations)\n grad_attn_weight = torch.zeros_like(attention_weights)\n\n ext_module.ms_deform_attn_backward(\n value,\n value_spatial_shapes,\n value_level_start_index,\n sampling_locations,\n attention_weights,\n grad_output.contiguous(),\n grad_value,\n grad_sampling_loc,\n grad_attn_weight,\n im2col_step=ctx.im2col_step)\n\n return grad_value, None, None, \\\n grad_sampling_loc, grad_attn_weight, None\n\n\n\nclass PositionwiseFeedForward(nn.Module):\n ''' A two-feed-forward-layer module '''\n def __init__(self, d_in, d_hid, dropout=0.0):\n super().__init__()\n self.norm = nn.LayerNorm(d_in)\n self.w_1 = nn.Linear(d_in, d_hid)\n self.w_2 = nn.Linear(d_hid, d_in) # position-wise\n self.act = F.gelu\n self.dropout = nn.Dropout(dropout)\n xavier_init(self.w_1, distribution='normal', bias=0.)\n xavier_init(self.w_2, distribution='normal', bias=0.)\n nn.init.constant_(self.norm.weight, 1)\n nn.init.constant_(self.norm.bias, 0)\n def forward(self, x):\n residual = x\n output = self.w_2(self.act(self.w_1(self.norm(x))))\n output = self.dropout(output) + residual\n return output\n\nclass SpatialCrossAttention(BaseModule):\n \"\"\"An attention module used in BEVFormer.\n Args:\n embed_dims (int): The embedding dimension of Attention.\n Default: 256.\n num_cams (int): The number of cameras\n dropout (float): A Dropout layer on `inp_residual`.\n Default: 0..\n init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.\n Default: None.\n deformable_attention: (dict): The config for the deformable attention used in SCA.\n \"\"\"\n\n def __init__(self,\n embed_dims=256,\n num_cams=4,\n dropout=0.0,\n init_cfg=None,\n batch_first=True,\n deformable_attention=dict(\n type='MSDeformableAttention3D',\n embed_dims=256,\n num_levels=4),\n query_dims=1543,\n **kwargs\n ):\n super(SpatialCrossAttention, self).__init__(init_cfg)\n\n self.init_cfg = init_cfg\n self.dropout = nn.Dropout(dropout)\n self.fp16_enabled = False\n self.deformable_attention = MSDeformableAttention3D()\n self.embed_dims = embed_dims\n self.num_cams = num_cams\n self.batch_first = batch_first\n self.query_linear = nn.Sequential(\n nn.LayerNorm(query_dims),\n nn.Linear(query_dims, 512),\n nn.GELU(),\n nn.Linear(512, embed_dims),\n nn.GELU()\n )\n self.dropout = nn.Dropout(dropout)\n self.ffn = PositionwiseFeedForward(256, 1024)\n\n self.output_proj = nn.Sequential(\n nn.LayerNorm(int(self.num_cams*256)),\n nn.Linear(int(self.num_cams*256), 512),\n nn.GELU(),\n nn.Linear(512, embed_dims),\n )\n self.init_weight()\n\n \n def init_weight(self):\n \"\"\"Default initialization for Parameters of Module.\"\"\"\n nn.init.constant_(self.query_linear[0].weight, 1)\n nn.init.constant_(self.query_linear[0].bias, 0)\n xavier_init(self.query_linear[1], distribution='normal', bias=0.)\n xavier_init(self.query_linear[3], distribution='normal', bias=0.)\n xavier_init(self.output_proj, distribution='normal', bias=0.)\n \n @force_fp32(apply_to=('query', 'key', 'value', 'query_pos', 'reference_points_cam'))\n def forward(self,\n query,\n key=None,\n value=None,\n residual=None,\n query_pos=None,\n key_padding_mask=None,\n reference_points=None,\n spatial_shapes=None,\n reference_points_cam=None,\n bev_mask=None,\n level_start_index=None,\n indexes=None,\n flag='encoder',\n **kwargs):\n \"\"\"Forward Function of Detr3DCrossAtten.\n Args:\n query (Tensor): Query of Transformer with shape\n (num_query, bs, embed_dims).\n key (Tensor): The key tensor with shape\n `(num_key, bs, embed_dims)`.\n value (Tensor): The value tensor with shape\n `(num_key, bs, embed_dims)`. (B, N, C, H, W)\n residual (Tensor): The tensor used for addition, with the\n same shape as `x`. Default None. If None, `x` will be used.\n query_pos (Tensor): The positional encoding for `query`.\n Default: None.\n key_pos (Tensor): The positional encoding for `key`. Default\n None.\n reference_points (Tensor): The normalized reference\n points with shape (bs, num_query, 4),\n all elements is range in [0, 1], top-left (0,0),\n bottom-right (1, 1), including padding area.\n or (N, Length_{query}, num_levels, 4), add\n additional two dimensions is (w, h) to\n form reference boxes.\n key_padding_mask (Tensor): ByteTensor for `query`, with\n shape [bs, num_key].\n spatial_shapes (Tensor): Spatial shape of features in\n different level. With shape (num_levels, 2),\n last dimension represent (h, w).\n level_start_index (Tensor): The start index of each level.\n A tensor has shape (num_levels) and can be represented\n as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].\n Returns:\n Tensor: forwarded results with shape [num_query, bs, embed_dims].\n \"\"\"\n query = self.query_linear(query)\n bs, num_cams, max_len, _ = query.size()\n num_cams, l, bs, embed_dims = value.shape\n value = value.permute(2, 0, 1, 3).reshape(\n bs * self.num_cams, l, self.embed_dims)\n\n queries = self.dropout(self.deformable_attention(\n query=query.view(bs*self.num_cams, max_len, self.embed_dims), key=value, value=value, \n reference_points=reference_points.view(bs*self.num_cams, max_len, 2), spatial_shapes=spatial_shapes,\n level_start_index=level_start_index).view(bs, self.num_cams, max_len, self.embed_dims))\n queries = self.ffn(queries)\n \n for j in range(bs):\n for i, index_query_per_img in enumerate(indexes):\n queries[j, i, :len(index_query_per_img)] = 0\n queries[j, i] /= max(len(index_query_per_img), 1.0)\n queries = queries.sum(-2).view(bs, -1)\n queries = self.output_proj(queries)\n return queries\n\nclass MSDeformableAttention3D(BaseModule):\n \"\"\"An attention module used in BEVFormer based on Deformable-Detr.\n `Deformable DETR: Deformable Transformers for End-to-End Object Detection.\n `_.\n Args:\n embed_dims (int): The embedding dimension of Attention.\n Default: 256.\n num_heads (int): Parallel attention heads. Default: 64.\n num_levels (int): The number of feature map used in\n Attention. Default: 4.\n num_points (int): The number of sampling points for\n each query in each head. Default: 4.\n im2col_step (int): The step used in image_to_column.\n Default: 64.\n dropout (float): A Dropout layer on `inp_identity`.\n Default: 0.1.\n batch_first (bool): Key, Query and Value are shape of\n (batch, n, embed_dim)\n or (n, batch, embed_dim). Default to False.\n norm_cfg (dict): Config dict for normalization layer.\n Default: None.\n init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.\n Default: None.\n \"\"\"\n\n def __init__(self,\n embed_dims=256,\n num_heads=8,\n num_levels=4,\n num_points=8,\n im2col_step=64,\n dropout=0.0,\n batch_first=True,\n norm_cfg=None,\n init_cfg=None):\n super().__init__(init_cfg)\n if embed_dims % num_heads != 0:\n raise ValueError(f'embed_dims must be divisible by num_heads, '\n f'but got {embed_dims} and {num_heads}')\n dim_per_head = embed_dims // num_heads\n self.norm_cfg = norm_cfg\n self.batch_first = batch_first\n self.output_proj = None\n self.fp16_enabled = False\n\n self.im2col_step = im2col_step\n self.embed_dims = embed_dims\n self.num_levels = num_levels\n self.num_heads = num_heads\n self.num_points = num_points\n self.sampling_offsets = nn.Linear(\n embed_dims, num_heads * num_levels * num_points * 2)\n self.attention_weights = nn.Linear(embed_dims,\n num_heads * num_levels * num_points)\n self.value_proj = nn.Linear(embed_dims, embed_dims)\n self.init_weights()\n\n def init_weights(self):\n \"\"\"Default initialization for Parameters of Module.\"\"\"\n constant_init(self.sampling_offsets, 0.)\n thetas = torch.arange(\n self.num_heads,\n dtype=torch.float32) * (2.0 * math.pi / self.num_heads)\n grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)\n grid_init = (grid_init /\n grid_init.abs().max(-1, keepdim=True)[0]).view(\n self.num_heads, 1, 1,\n 2).repeat(1, self.num_levels, self.num_points, 1)\n for i in range(self.num_points):\n grid_init[:, :, i, :] *= i + 1\n\n self.sampling_offsets.bias.data = grid_init.view(-1)\n constant_init(self.attention_weights, val=0., bias=0.)\n xavier_init(self.value_proj, distribution='uniform', bias=0.)\n xavier_init(self.output_proj, distribution='uniform', bias=0.)\n self._is_init = True\n\n def forward(self,\n query,\n key=None,\n value=None,\n identity=None,\n query_pos=None,\n key_padding_mask=None,\n reference_points=None,\n spatial_shapes=None,\n level_start_index=None,\n **kwargs):\n \"\"\"Forward Function of MultiScaleDeformAttention.\n Args:\n query (Tensor): Query of Transformer with shape\n ( bs, num_query, embed_dims).\n key (Tensor): The key tensor with shape\n `(bs, num_key, embed_dims)`.\n value (Tensor): The value tensor with shape\n `(bs, num_key, embed_dims)`.\n identity (Tensor): The tensor used for addition, with the\n same shape as `query`. Default None. If None,\n `query` will be used.\n query_pos (Tensor): The positional encoding for `query`.\n Default: None.\n key_pos (Tensor): The positional encoding for `key`. Default\n None.\n reference_points (Tensor): The normalized reference\n points with shape (bs, num_query, num_levels, 2),\n all elements is range in [0, 1], top-left (0,0),\n bottom-right (1, 1), including padding area.\n or (N, Length_{query}, num_levels, 4), add\n additional two dimensions is (w, h) to\n form reference boxes.\n key_padding_mask (Tensor): ByteTensor for `query`, with\n shape [bs, num_key].\n spatial_shapes (Tensor): Spatial shape of features in\n different levels. With shape (num_levels, 2),\n last dimension represents (h, w).\n level_start_index (Tensor): The start index of each level.\n A tensor has shape ``(num_levels, )`` and can be represented\n as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].\n Returns:\n Tensor: forwarded results with shape [num_query, bs, embed_dims].\n \"\"\"\n\n if value is None:\n value = query\n bs, num_query, _ = query.shape\n bs, num_value, _ = value.shape\n assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value\n\n value = self.value_proj(value)\n # if key_padding_mask is not None:\n # value = value.masked_fill(key_padding_mask[..., None], 0.0)\n value = value.view(bs, num_value, self.num_heads, -1)\n sampling_offsets = self.sampling_offsets(query).view(\n bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)\n attention_weights = self.attention_weights(query).view(\n bs, num_query, self.num_heads, self.num_levels * self.num_points)\n\n attention_weights = attention_weights.softmax(-1)\n\n attention_weights = attention_weights.view(bs, num_query,\n self.num_heads,\n self.num_levels,\n self.num_points)\n\n if reference_points.shape[-1] == 2:\n \"\"\"\n For each BEV query, it owns `num_Z_anchors` in 3D space that having different heights.\n After proejcting, each BEV query has `num_Z_anchors` reference points in each 2D image.\n For each referent point, we sample `num_points` sampling points.\n For `num_Z_anchors` reference points, it has overall `num_points * num_Z_anchors` sampling points.\n \"\"\"\n offset_normalizer = torch.stack(\n [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)\n\n bs, num_query, xy = reference_points.shape\n reference_points = reference_points[:, :, None, None, None, :]\n sampling_offsets = sampling_offsets / offset_normalizer[None, None, None, :, None, :]\n bs, num_query, num_heads, num_levels, num_all_points, xy = sampling_offsets.shape\n sampling_locations = reference_points + sampling_offsets\n #bs, num_query, num_heads, num_levels, num_all_points, xy = sampling_locations.shape\n\n\n else:\n raise ValueError(\n f'Last dim of reference_points must be'\n f' 2 or 4, but get {reference_points.shape[-1]} instead.')\n\n # sampling_locations.shape: bs, num_query, num_heads, num_levels, num_all_points, 2\n # attention_weights.shape: bs, num_query, num_heads, num_levels, num_all_points\n if torch.cuda.is_available() and value.is_cuda:\n if value.dtype == torch.float16:\n MultiScaleDeformableAttnFunction = MultiScaleDeformableAttnFunction_fp32\n else:\n MultiScaleDeformableAttnFunction = MultiScaleDeformableAttnFunction_fp32\n output = MultiScaleDeformableAttnFunction.apply(\n value, spatial_shapes, level_start_index, sampling_locations,\n attention_weights, self.im2col_step)\n else:\n output = multi_scale_deformable_attn_pytorch(\n value, spatial_shapes, sampling_locations, attention_weights)\n return output" }, { "path": "open_loop_training/code/model_code/dense_heads/thinktwice_decoder.py", "content": "import copy\r\nfrom collections import OrderedDict\r\nfrom distutils.errors import LibError\r\nfrom .multi_scale_deformable_attn_function import SpatialCrossAttention\r\nfrom .utils import SpatialGRU, sigmoid_focal_loss, init_weights\r\nfrom mmcv.cnn import build_conv_layer\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch.distributions import Beta\r\n\r\nfrom mmdet.core import (multi_apply, multi_apply, reduce_mean)\r\nfrom mmdet.models import HEADS\r\nfrom mmcv.runner import BaseModule\r\n\r\nimport numpy as np\r\nimport mmcv\r\nimport cv2 as cv\r\nfrom mmcv.runner import force_fp32, auto_fp16\r\n\r\ndef inv_softplus(x):\r\n return x + torch.log(-torch.expm1(-x))\r\n\r\n\r\nclass PredictionModule(nn.Module):\r\n def __init__(self, act):\r\n super().__init__()\r\n self.act = act\r\n self.spatial_gru = SpatialGRU(input_size=6, hidden_size=32, act=self.act)\r\n ## Resiudal Update\r\n self.ffn = nn.Sequential(\r\n nn.Conv2d(32, 64, kernel_size=1),\r\n self.act(),\r\n nn.Conv2d(64, 32, kernel_size=3, padding=1),\r\n self.act(),\r\n nn.Conv2d(32, 32, kernel_size=1),\r\n )\r\n \r\n @force_fp32()\r\n def forward(self, current_BEV_feature, current_wp, current_ctrl, future_bev_feat_from_last_layer):\r\n future_bev_feat = self.spatial_gru(torch.cat([current_wp, current_ctrl], dim=2).unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, current_BEV_feature.shape[-2], current_BEV_feature.shape[-1]), state=current_BEV_feature).view(-1, current_BEV_feature.shape[1], current_BEV_feature.shape[2], current_BEV_feature.shape[3])\r\n ## Residual Update\r\n if future_bev_feat_from_last_layer is not None:\r\n current_future_bev_feat = self.ffn(future_bev_feat) + future_bev_feat_from_last_layer.view(-1, current_BEV_feature.shape[1], current_BEV_feature.shape[2], current_BEV_feature.shape[3])\r\n return future_bev_feat\r\n \r\n\r\n\r\n\r\nclass LookModule(nn.Module):\r\n def __init__(self, act):\r\n super().__init__()\r\n self.act = act\r\n self.cam_look_module = SpatialCrossAttention()\r\n self.lidar_look_module_atten = nn.Sequential(\r\n nn.Linear(6+128, 256),\r\n self.act(),\r\n nn.Linear(256, 512),\r\n nn.Sigmoid(),\r\n )\r\n self.lidar_look_module_MLP = nn.Sequential(\r\n nn.Linear(512, 128),\r\n self.act(),\r\n nn.Flatten(start_dim=2),\r\n nn.Linear(9*128, 256),\r\n self.act(),\r\n )\r\n\r\n self.look_feature_MLP = nn.Sequential(\r\n nn.Linear(512*4, 512),\r\n self.act(),\r\n nn.Linear(512, 128),\r\n )\r\n self.point_cloud_range = [-8.0, -19.2, -4.0, 30.4, 19.2, 4.0]\r\n self.fp16_enabled = False\r\n \r\n @force_fp32() \r\n def obtain_lidar_look_features(self, wp, lidar_grid):\r\n bs, T, xy = wp.size()\r\n relative_wp_x = 1.0 - torch.clamp(((wp[..., 0] - self.point_cloud_range[0])/(self.point_cloud_range[3]-self.point_cloud_range[0])).unsqueeze(-1) + torch.Tensor([0.0, -0.1, 0.1]).unsqueeze(0).unsqueeze(0).to(wp.device), min=0.0, max=1.0)\r\n relative_wp_y = torch.clamp(((wp[..., 1] - self.point_cloud_range[1])/(self.point_cloud_range[4]-self.point_cloud_range[1])).unsqueeze(-1) + torch.Tensor([0.0, -0.1, 0.1]).unsqueeze(0).unsqueeze(0).to(wp.device), min=0.0, max=1.0)\r\n relative_wp = torch.stack([relative_wp_x.unsqueeze(-1).repeat(1, 1, 1, 3), relative_wp_y.unsqueeze(-2).repeat(1, 1, 3, 1)], dim=-1).view(bs*T, -1, 1, 2) * 2 - 1\r\n sampled_feat = F.grid_sample(input=lidar_grid.view(bs*T, *lidar_grid.shape[2:]), grid=relative_wp, align_corners=False).view(bs, T, -1, 9).transpose(2, 3)\r\n return sampled_feat\r\n\r\n @force_fp32()\r\n def obtain_cam_ref_points_query(self, reference_points, transform_mat, img_shape, query, mlvl_feats):\r\n reference_points = torch.cat((reference_points, torch.ones_like(reference_points[..., :1])), -1) # reference_points (B, num_queries, 4)\r\n\r\n B, num_query = reference_points.size()[:2]\r\n num_cam = transform_mat[0].size(1)\r\n reference_points = reference_points.view(B, 1, num_query, 4).repeat(1, num_cam, 1, 1).unsqueeze(-1)\r\n lidar2img = transform_mat[0].view(B, num_cam, 1, 4, 4).repeat(1, 1, num_query, 1, 1)\r\n ida_mat = transform_mat[1].view(B, num_cam, 1, 4, 4).repeat(1, 1, num_query, 1, 1)\r\n reference_points_cam = torch.matmul(lidar2img, reference_points).squeeze(-1)\r\n eps = 1e-5\r\n new_reference_points_cam = reference_points_cam.clone()\r\n new_reference_points_cam[..., 0:2] = reference_points_cam[..., 0:2] / torch.maximum(\r\n reference_points_cam[..., 2:3], torch.ones_like(reference_points_cam[..., 2:3])*eps)\r\n reference_points_cam = torch.matmul(ida_mat, new_reference_points_cam.unsqueeze(-1)).squeeze(-1)\r\n mask = (reference_points_cam[..., 2:3] > eps)\r\n\r\n reference_points_cam = reference_points_cam[..., :2]\r\n\r\n reference_points_cam[..., 0] /= img_shape[1]\r\n reference_points_cam[..., 1] /= img_shape[0]#B x Cam x Num_query x 2\r\n mask = (mask & (reference_points_cam[..., 1:2] > 0.0)\r\n & (reference_points_cam[..., 1:2] < 1.0)\r\n & (reference_points_cam[..., 0:1] < 1.0) \r\n & (reference_points_cam[..., 0:1] > 0.0))\r\n mask = mask.view(B, num_cam, num_query)\r\n mask = torch.nan_to_num(mask)\r\n mask = mask.permute(1, 0, 2)\r\n sampled_feats = []\r\n \r\n N = 4\r\n reference_points_cam_lvl = reference_points_cam.view(B*N, num_query, 1, 2) * 2 -1.0\r\n for lvl, feat in enumerate(mlvl_feats):\r\n BN, C, H, W = feat.size()\r\n B = int(BN/N)\r\n #feat = feat.view(B*N, C, H, W)\r\n sampled_feat = F.grid_sample(feat, reference_points_cam_lvl, align_corners=False)\r\n sampled_feat = sampled_feat.view(B, N, C, num_query, 1).permute(0, 2, 3, 1, 4)\r\n sampled_feats.append(sampled_feat)\r\n sampled_feats = torch.stack(sampled_feats, -1)\r\n sampled_feats = sampled_feats.view(B, C, num_query, num_cam, len(mlvl_feats)).permute(0, 2, 3, 1, 4).reshape(B, num_query, num_cam, -1)\r\n \r\n indexes = []\r\n # From bev_mask of each image, to obtain the available queries' index\r\n for i, mask_per_img in enumerate(mask):\r\n tmp_index = []\r\n for j in range(B):\r\n index_query_per_img = mask_per_img[j].nonzero().squeeze(-1)\r\n tmp_index.append(index_query_per_img)\r\n indexes.append(tmp_index)\r\n max_len = max([len(each_sample) for each_cam in indexes for each_sample in each_cam])\r\n bs, num_query, query_emb = query.shape\r\n num_cams = 4\r\n queries_rebatch = query.new_zeros([bs * num_cams, max_len, query_emb+1024])\r\n reference_points_rebatch = reference_points_cam.new_zeros([bs * num_cams, max_len, 2]) \r\n\r\n reference_points_cam = reference_points_cam.permute(1, 0, 2, 3)\r\n for i, reference_points_per_img in enumerate(reference_points_cam):\r\n for j in range(bs):\r\n index_query_per_img = indexes[i][j]\r\n if len(index_query_per_img) != 0:\r\n queries_rebatch[j * num_cams + i, :len(index_query_per_img)] = torch.cat([query[j, index_query_per_img], sampled_feats[j, index_query_per_img, i]], dim=-1)\r\n reference_points_rebatch[j * num_cams + i, :len(index_query_per_img)] = reference_points_per_img[j, index_query_per_img]\r\n return reference_points_rebatch.view(bs, num_cams, max_len, 2), queries_rebatch.view(bs, num_cams, max_len, query_emb+1024), indexes\r\n \r\n\r\n @force_fp32()\r\n def forward(self, current_wp, current_ctrl_softplus, measurement_feat, flattened_feat, coor2img, img_size, mlvl_feats, fpn_feat_flatten, spatial_shapes, level_start_index, lidar_feat_with_high_resolution, temporal_embedding, static_embedding):\r\n # Look Module - Image\r\n ## Avoid empty points situation which could cause bugs in code \r\n static_point = torch.Tensor([[5.0, 0.0], [0.0, -5.0], [0.0, 5.0], [-5.0, 0.0],]).unsqueeze(0).repeat(current_wp.shape[0], 1, 1).to(current_wp.device)\r\n look_wp = torch.cat([current_wp, static_point], dim=1)\r\n ### Add z to 2D BEV coordinate uniformly\r\n look_wp_3d = torch.cat([look_wp.unsqueeze(2).repeat(1, 1, 15, 1), torch.linspace(-4, 10, 15, dtype=float, device=look_wp.device).unsqueeze(0).unsqueeze(0).unsqueeze(-1).repeat(look_wp.shape[0], look_wp.shape[1], 1, 1)], dim=-1).view(look_wp.shape[0], -1, 3).to(look_wp.device, look_wp.dtype) # B x T x z_level x 3 -> B x T*z_level x 3\r\n input_ctrl = torch.cat([current_ctrl_softplus.unsqueeze(2).repeat(1, 1, 15, 1).view(current_ctrl_softplus.shape[0], -1, 4), torch.zeros(current_wp.shape[0], 4*15, 4).to(current_wp.device)], dim=1) ## No control for static points\r\n\r\n ## For deformable Attn\r\n img_query = torch.cat([\r\n input_ctrl, ## predicted control \r\n look_wp_3d, ## predict traj\r\n torch.cat([temporal_embedding.unsqueeze(0).unsqueeze(-2).repeat(current_wp.shape[0], 1, 15, 1).view(current_wp.shape[0], -1, temporal_embedding.shape[-1]), ## Temporal Emeddding\r\n static_embedding.unsqueeze(0).unsqueeze(-2).repeat(current_wp.shape[0], 1, 15, 1).view(current_wp.shape[0], -1, temporal_embedding.shape[-1])], dim=1), ## Static Point Embedding\r\n measurement_feat.unsqueeze(1).repeat(1, look_wp_3d.shape[1], 1), \r\n flattened_feat.unsqueeze(1).repeat(1, look_wp_3d.shape[1], 1)\r\n ], dim=-1) #B x T*z_level * 4 + 3 + 128 + 128 + 256\r\n \r\n ## Transform into the desired format for the deformable attention\r\n ref_points_cam_rebatch, query_rebatch, indexes_rebatch = self.obtain_cam_ref_points_query(look_wp_3d, coor2img, img_size, img_query, mlvl_feats)\r\n img_look_features = self.cam_look_module(query=query_rebatch, value=fpn_feat_flatten, reference_points=ref_points_cam_rebatch, spatial_shapes=spatial_shapes, level_start_index=level_start_index, indexes=indexes_rebatch)\r\n img_look_features = img_look_features.unsqueeze(1).repeat(1, temporal_embedding.shape[0], 1)\r\n\r\n ## Look Module - Lidar\r\n lidar_attn_weight = self.lidar_look_module_atten(torch.cat([current_wp, current_ctrl_softplus, temporal_embedding.unsqueeze(0).repeat(current_wp.shape[0], 1, 1)], dim=-1)) ## Different feature maps for different control signals and time-steps\r\n\r\n temporal_lidar_feat = lidar_attn_weight.unsqueeze(-1).unsqueeze(-1) * lidar_feat_with_high_resolution.unsqueeze(1).float()\r\n ## Retrieve Lidar features\r\n lidar_look_features = self.obtain_lidar_look_features(current_wp, temporal_lidar_feat)\r\n lidar_look_features = self.lidar_look_module_MLP(lidar_look_features)\r\n\r\n look_features = torch.cat([img_look_features, torch.zeros_like(lidar_look_features)], dim=-1)\r\n return look_features\r\n\r\nclass ThinkTwiceDecoderLayer(nn.Module):\r\n def __init__(self, act):\r\n super().__init__()\r\n self.act = act\r\n self.prediction_module = PredictionModule(self.act)\r\n self.look_module = LookModule(self.act)\r\n\r\n self.mlp = nn.Sequential(\r\n nn.LayerNorm(256+128+512+128),\r\n ## flattened prediction BEV feat 256 + look feature 512 + temporal embedding 128 + measurement\r\n nn.Linear(256+128+512+128, 512),\r\n self.act(),\r\n nn.Dropout(0.0),\r\n nn.Linear(512, 512),\r\n self.act(),\r\n )\r\n \r\n self.traj_offset_module = nn.Sequential(\r\n nn.Linear(512+2, 256),\r\n self.act(),\r\n nn.Linear(256, 64),\r\n self.act(),\r\n nn.Linear(64, 2),\r\n )\r\n self.ctrl_offset_module = nn.Sequential(\r\n nn.Linear(512+4, 256),\r\n self.act(),\r\n nn.Linear(256, 64),\r\n self.act(),\r\n nn.Linear(64, 4),\r\n )\r\n \r\n self.BEV_feat_update_module = nn.Sequential(\r\n nn.Conv2d(512*4+32, 128, kernel_size=3, padding=1),\r\n self.act(),\r\n nn.Conv2d(128, 32, kernel_size=3, padding=1),\r\n )\r\n \r\n self.flattened_BEV_feat_update_module = nn.Sequential(\r\n nn.Linear(256+512*4, 512),\r\n self.act(),\r\n nn.Linear(512, 256),\r\n )\r\n self.fp16_enabled = False\r\n\r\n\r\n @force_fp32()\r\n def forward(self, BEV_feat, current_wp, current_ctrl, future_bev_feat_from_last_layer, parent_module, grid2feat, measurement_feat, flattened_BEV_feat, coor2img, img_size, mlvl_feats, fpn_feat_flatten, spatial_shapes, level_start_index, lidar_feat_with_high_resolution, temporal_embedding, static_embedding):\r\n current_ctrl_softplus = F.softplus(current_ctrl)\r\n\r\n ## Prediction Module\r\n future_bev_feat = self.prediction_module(current_BEV_feature=BEV_feat, current_wp=current_wp, current_ctrl=current_ctrl_softplus, future_bev_feat_from_last_layer=future_bev_feat_from_last_layer)\r\n\r\n flattened_future_BEV_feat, _ = grid2feat(future_bev_feat, parent_module)\r\n flattened_future_BEV_feat = flattened_future_BEV_feat.view(BEV_feat.shape[0], 4, 256) ## Pred_len = 4\r\n future_bev_feat = future_bev_feat.view(BEV_feat.shape[0], -1, BEV_feat.shape[1], BEV_feat.shape[2], BEV_feat.shape[3])\r\n\r\n ## Look Module\r\n look_features = self.look_module(\r\n current_wp=current_wp, current_ctrl_softplus=current_ctrl_softplus, measurement_feat=measurement_feat, flattened_feat=flattened_BEV_feat, coor2img=coor2img, img_size=img_size, mlvl_feats=mlvl_feats, fpn_feat_flatten=fpn_feat_flatten, spatial_shapes=spatial_shapes, level_start_index=level_start_index, lidar_feat_with_high_resolution=lidar_feat_with_high_resolution, temporal_embedding=temporal_embedding, static_embedding=static_embedding\r\n )\r\n\r\n all_future_feat = self.mlp(torch.cat([flattened_future_BEV_feat, look_features, temporal_embedding.unsqueeze(0).repeat(flattened_future_BEV_feat.shape[0], 1, 1), measurement_feat.unsqueeze(1).repeat(1, flattened_future_BEV_feat.shape[1], 1)], dim=-1))\r\n\r\n traj_offset =self.traj_offset_module(torch.cat([current_wp, all_future_feat], dim=-1))\r\n ctrl_offset = self.ctrl_offset_module(torch.cat([current_ctrl, all_future_feat], dim=-1))\r\n\r\n ## Residual update of feature maps similar to DETR\r\n updated_BEV_feat = self.BEV_feat_update_module(torch.cat([BEV_feat, all_future_feat.view(BEV_feat.shape[0], -1).unsqueeze(-1).unsqueeze(-1).repeat(1, 1, BEV_feat.shape[2], BEV_feat.shape[3])], dim=1)) + BEV_feat\r\n\r\n updated_flattend_BEV_feat = self.flattened_BEV_feat_update_module(torch.cat([flattened_BEV_feat, all_future_feat.view(flattened_BEV_feat.shape[0], -1)], dim=-1)) + flattened_BEV_feat\r\n return traj_offset, ctrl_offset, future_bev_feat, updated_BEV_feat, updated_flattend_BEV_feat\r\n\r\n@HEADS.register_module()\r\nclass ThinkTwiceDecoder(BaseModule):\r\n def __init__(self,\r\n *args,\r\n config=None,\r\n bev_h=None,\r\n bev_w=None,\r\n BEV_feat_dim=256, ## BEVfFeature dimension\r\n flattened_BEV_feat_dim=256, ## Flattened 1D feature dimension\r\n **kwargs):\r\n\r\n super(ThinkTwiceDecoder, self).__init__(\r\n *args, **kwargs)\r\n \r\n self.config = config\r\n self.bev_h = bev_h\r\n self.bev_w = bev_w\r\n self.BEV_feat_dim = BEV_feat_dim\r\n self.flattened_BEV_feat_dim = flattened_BEV_feat_dim\r\n self.act = nn.ReLU\r\n\r\n #For distil\r\n self.distil_index = [2, 3, 4, 5] ## All layers\r\n self.distil_kl_loss_weight_dict = {2:0.25, 3:1.0/3.0, 4:1.0/4.0, 5:1/11.0}\r\n self.wp_loss_weight = 15.0\r\n self.action_loss_weight = 15.0\r\n \r\n self.build_layers()\r\n self.fp16_enabled = False\r\n\r\n def build_layers(self):\r\n self.build_coarse_output_layer()\r\n self.build_refine_layers()\r\n self.apply(init_weights)\r\n\r\n ## Similar to TCP, give 1D feature map, predict 1D traj and control\r\n def build_coarse_output_layer(self):\r\n self.join_traj = nn.Sequential(\r\n nn.Linear(128+self.flattened_BEV_feat_dim, 512),\r\n self.act(),\r\n nn.Linear(512, 512),\r\n self.act(),\r\n nn.Linear(512, 256),\r\n self.act(),\r\n )\r\n self.output_traj = nn.Sequential(\r\n nn.Linear(256, 512),\r\n self.act(),\r\n nn.Linear(512, 2*self.config.pred_len),\r\n )\r\n self.join_ctrl = nn.Sequential(\r\n nn.Linear(128+self.flattened_BEV_feat_dim, 512),\r\n self.act(),\r\n nn.Linear(512, 512),\r\n self.act(),\r\n nn.Linear(512, 256),\r\n self.act(),\r\n )\r\n self.speed_branch = nn.Sequential(\r\n nn.Linear(self.flattened_BEV_feat_dim, 256),\r\n self.act(),\r\n nn.Linear(256, 256),\r\n self.act(),\r\n nn.Linear(256, 1),\r\n )\r\n\r\n self.value_branch_traj = nn.Sequential(\r\n nn.Linear(256, 256),\r\n self.act(),\r\n nn.Linear(256, 256),\r\n self.act(),\r\n nn.Linear(256, 1),\r\n )\r\n self.value_branch_ctrl = nn.Sequential(\r\n nn.Linear(256, 256),\r\n self.act(),\r\n nn.Linear(256, 256),\r\n self.act(),\r\n nn.Linear(256, 1),\r\n )\r\n # shared branches_neurons\r\n self.dim_out = 2\r\n\r\n self.policy_head = nn.Sequential(\r\n nn.Linear(256, 512),\r\n self.act(),\r\n nn.Linear(512, 512),\r\n self.act(),\r\n )\r\n self.dist_mu = nn.Sequential(\r\n nn.Linear(512, 512), \r\n self.act(),\r\n nn.Linear(512, self.dim_out * self.config.pred_len),\r\n ) ## No softplus here!\r\n self.dist_sigma = nn.Sequential(\r\n nn.Linear(512, 512), \r\n self.act(),\r\n nn.Linear(512, self.dim_out * self.config.pred_len),\r\n ) ## No softplus here!\r\n \r\n\r\n def build_refine_layers(self):\r\n ## Transform all FPN feature to the same dimension to conduct multi-scale deformable attention\r\n self.fpn_linear0 = nn.Conv2d(in_channels=self.config[\"FPN_out_channels\"][0], out_channels=256, kernel_size=1)\r\n self.fpn_linear1 = nn.Conv2d(in_channels=self.config[\"FPN_out_channels\"][1], out_channels=256, kernel_size=1)\r\n self.fpn_linear2 = nn.Conv2d(in_channels=self.config[\"FPN_out_channels\"][2], out_channels=256, kernel_size=1)\r\n self.fpn_linear3 = nn.Conv2d(in_channels=self.config[\"FPN_out_channels\"][3], out_channels=256, kernel_size=1)\r\n self.temporal_embedding = nn.parameter.Parameter(torch.zeros(self.config.pred_len, 128)) ## Embedding for different predicted time-step\r\n self.cams_embeds = nn.parameter.Parameter(torch.zeros(4, 256))\r\n self.static_embedding = nn.parameter.Parameter(torch.zeros(4, 128))\r\n self.level_embeds = nn.parameter.Parameter(torch.zeros(4, 256)) ## For multi-scale attention\r\n nn.init.trunc_normal_(self.temporal_embedding, mean=0, std=0.02)\r\n nn.init.trunc_normal_(self.cams_embeds, mean=0, std=0.02)\r\n nn.init.trunc_normal_(self.static_embedding, mean=0, std=0.02)\r\n nn.init.trunc_normal_(self.temporal_embedding, mean=0, std=0.02)\r\n\r\n self.decoder_layers = nn.ModuleList([ThinkTwiceDecoderLayer(self.act) for _ in range(self.config[\"refine_num\"])])\r\n \r\n @force_fp32()\r\n def transform_fpn_feats(self, mlvl_feats):\r\n spatial_shapes = []\r\n feat_flatten = []\r\n \"\"\" flatten feats of each level \"\"\"\r\n for lvl, feat in enumerate(mlvl_feats):\r\n bs_num_cam, c, h, w = feat.shape\r\n num_cam = 4\r\n bs = int(bs_num_cam/num_cam)\r\n feat = feat.view(bs, num_cam, c, h, w)\r\n spatial_shape = (h, w)\r\n feat = feat.flatten(3).permute(1, 0, 3, 2) \r\n feat = feat + self.cams_embeds[:, None, None, :].to(feat.dtype)\r\n feat = feat + self.level_embeds[None, None, lvl:lvl + 1, :].to(feat.dtype)\r\n spatial_shapes.append(spatial_shape)\r\n feat_flatten.append(feat)\r\n \"\"\" concat flattened features \"\"\"\r\n feat_flatten = torch.cat(feat_flatten, 2) # (cam, bs, sum(h*w), 256)\r\n spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=mlvl_feats[0].device)\r\n level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))\r\n fpn_feat_flatten = feat_flatten.permute(0, 2, 1, 3) # (num_cam, sum(H*W), bs, embed_dims)\r\n return spatial_shapes, level_start_index, fpn_feat_flatten\r\n\r\n ## Convert 32x21x21 BEV feature to 256 dim 1D feature by the shared flatten module\r\n @auto_fp16()\r\n def grid2feat(self, grid_feat, parent_module):\r\n mid_feature = [None, None] #Currently no feature for 8x94x94 16x45x45\r\n mid_feature.append(grid_feat)\r\n feats64_10_10 = parent_module.MLP10(parent_module.inplace_act(parent_module.conv21_10(grid_feat)))\r\n mid_feature.append(feats64_10_10)\r\n feats128_4_4 = parent_module.MLP4(parent_module.inplace_act(parent_module.conv10_4(feats64_10_10)))\r\n mid_feature.append(feats128_4_4)\r\n feats256_2_2 = parent_module.MLP2(parent_module.inplace_act(parent_module.conv4_2((feats128_4_4))))\r\n mid_feature.append(feats256_2_2)\r\n flattened_BEV_feat = parent_module.output_fc(feats256_2_2.flatten(start_dim=1))\r\n return flattened_BEV_feat, mid_feature\r\n \r\n\r\n @force_fp32()\r\n def forward(self, flattend_BEV_feat, BEV_feat, measurement_feat, target_point, parent_module, teacher_forcing_data=None, look_feature_metadata=None):\r\n outs = {}\r\n outs['bev_feature'] = BEV_feat\r\n ## Coarase prediction similar to TCP\r\n ### Predict speed by sensor feature to alleviate copy cat problem\r\n outs['pred_speed'] = self.speed_branch(flattend_BEV_feat)\r\n ### Coarse Traj Branch\r\n j_traj = self.join_traj(torch.cat([flattend_BEV_feat, measurement_feat], 1))\r\n outs['pred_value_traj'] = self.value_branch_traj(j_traj)\r\n outs['pred_features_traj'] = j_traj\r\n pred_wp = self.output_traj(j_traj).view(-1, self.config.pred_len, 2)\r\n pred_wp_lis = [pred_wp]\r\n ### Coarse Ctrl Branch\r\n j_ctrl = self.join_ctrl(torch.cat([flattend_BEV_feat, measurement_feat], dim=-1))\r\n outs['pred_value_ctrl'] = self.value_branch_ctrl(j_ctrl)\r\n outs['pred_features_ctrl'] = j_ctrl\r\n policy = self.policy_head(j_ctrl)\r\n predicted_mu = self.dist_mu(policy).view(-1, self.config.pred_len, self.dim_out)\r\n predicted_sigma = self.dist_sigma(policy).view(-1, self.config.pred_len, self.dim_out)\r\n pred_ctrl_lis = [torch.cat([predicted_mu, predicted_sigma], dim=-1)]\r\n\r\n ## Look Module\r\n ### Prepare features for Look Module - Image\r\n lidar2img = look_feature_metadata[0].to(flattend_BEV_feat.device)\r\n ida_mat = look_feature_metadata[1].to(flattend_BEV_feat.device)\r\n fpn_feats = look_feature_metadata[2]\r\n mlvl_feats = []\r\n mlvl_feats.append(self.fpn_linear0(fpn_feats[0]))\r\n mlvl_feats.append(self.fpn_linear1(fpn_feats[1]))\r\n mlvl_feats.append(self.fpn_linear2(fpn_feats[2]))\r\n mlvl_feats.append(self.fpn_linear3(fpn_feats[3]))\r\n spatial_shapes, level_start_index, fpn_feat_flatten = self.transform_fpn_feats(mlvl_feats)\r\n ### Prepare features for Look Module - Lidar\r\n lidar_feat_with_high_resolution = look_feature_metadata[3]\r\n\r\n\r\n future_bev_feat = None\r\n current_BEV_feat = BEV_feat.clone()\r\n current_flattened_BEV_feat = flattend_BEV_feat.clone()\r\n stored_BEV_feat = []\r\n stored_flattened_BEV_feat = []\r\n stored_future_BEV_feat = []\r\n for refine_layer_index in range(self.config[\"refine_num\"]):\r\n current_wp = pred_wp_lis[-1].detach()\r\n current_ctrl = pred_ctrl_lis[-1].detach()\r\n\r\n traj_offset, ctrl_offset, updated_future_bev_feat, updated_BEV_feat, updated_flattend_BEV_feat = self.decoder_layers[refine_layer_index](BEV_feat=current_BEV_feat, current_wp=current_wp, current_ctrl=current_ctrl, future_bev_feat_from_last_layer=future_bev_feat, parent_module=parent_module, grid2feat=self.grid2feat, measurement_feat=measurement_feat, flattened_BEV_feat=current_flattened_BEV_feat, coor2img=[lidar2img, ida_mat], img_size=self.config[\"img_size\"], mlvl_feats=mlvl_feats, fpn_feat_flatten=fpn_feat_flatten, spatial_shapes=spatial_shapes, level_start_index=level_start_index, lidar_feat_with_high_resolution=lidar_feat_with_high_resolution, temporal_embedding=self.temporal_embedding, static_embedding=self.static_embedding)\r\n\r\n pred_wp_lis.append(traj_offset.float()+current_wp.float())\r\n pred_ctrl_lis.append(ctrl_offset.float()+current_ctrl.float())\r\n\r\n stored_BEV_feat.append(updated_BEV_feat)\r\n current_BEV_feat = updated_BEV_feat\r\n\r\n stored_flattened_BEV_feat.append(updated_flattend_BEV_feat)\r\n current_flattened_BEV_feat = updated_flattend_BEV_feat\r\n\r\n stored_future_BEV_feat.append(updated_future_bev_feat)\r\n future_bev_feat = updated_future_bev_feat\r\n \r\n outs[\"refine_flattned_BEV_feature\"] = torch.stack(stored_flattened_BEV_feat, dim=1)\r\n outs[\"refine_BEV_feature\"] = torch.stack(stored_BEV_feat, dim=1)\r\n outs[\"refine_future_BEV_feature\"] = torch.stack(stored_future_BEV_feat, dim=1).view(current_wp.shape[0], current_wp.shape[1], self.config[\"refine_num\"], 32, 21, 21).transpose(1, 2)\r\n\r\n pred_wp_lis = torch.stack(pred_wp_lis, dim=1) #BxrefinexTx2\r\n pred_ctrl_lis = torch.clamp(F.softplus(torch.stack(pred_ctrl_lis, dim=1).float()), min=1e-3) #BxrefinexTx4\r\n outs['pred_wp'] = pred_wp_lis\r\n outs['mu_branches'] = pred_ctrl_lis[:, :, 0, :2]\r\n outs['sigma_branches'] = pred_ctrl_lis[:, :, 0, 2:]\r\n outs['future_mu'] = pred_ctrl_lis[:, :, 1:, :2]\r\n outs['future_sigma'] = pred_ctrl_lis[:, :, 1:, 2:]\r\n\r\n ## Apply teacher forcing\r\n if teacher_forcing_data is not None:\r\n teacher_pred_traj_offset_lis = []\r\n teacher_pred_ctrl_offset_lis = []\r\n ##GT input should obtain GT future feature\r\n current_wp = teacher_forcing_data[\"waypoints\"].float()\r\n\r\n current_ctrl_softplus = torch.cat([torch.cat([teacher_forcing_data[\"action_mu\"], teacher_forcing_data[\"action_sigma\"]], dim=-1).unsqueeze(1), torch.cat([torch.stack(teacher_forcing_data[\"future_action_mu\"][:-1], dim=1), torch.stack(teacher_forcing_data[\"future_action_sigma\"][:-1], dim=1)], dim=-1)], dim=1).float()\r\n\r\n current_ctrl = inv_softplus(current_ctrl_softplus)\r\n\r\n future_bev_feat = None\r\n current_BEV_feat = BEV_feat.clone()\r\n current_flattened_BEV_feat = flattend_BEV_feat.clone()\r\n stored_BEV_feat = []\r\n stored_flattened_BEV_feat = []\r\n stored_future_BEV_feat = []\r\n for refine_layer_index in range(self.config[\"refine_num\"]):\r\n traj_offset, ctrl_offset, updated_future_bev_feat, updated_BEV_feat, updated_flattend_BEV_feat = self.decoder_layers[refine_layer_index](BEV_feat=current_BEV_feat, current_wp=current_wp, current_ctrl=current_ctrl, future_bev_feat_from_last_layer=future_bev_feat, parent_module=parent_module, grid2feat=self.grid2feat, measurement_feat=measurement_feat, flattened_BEV_feat=current_flattened_BEV_feat, coor2img=[lidar2img, ida_mat], img_size=self.config[\"img_size\"], mlvl_feats=mlvl_feats, fpn_feat_flatten=fpn_feat_flatten, spatial_shapes=spatial_shapes, level_start_index=level_start_index, lidar_feat_with_high_resolution=lidar_feat_with_high_resolution, temporal_embedding=self.temporal_embedding, static_embedding=self.static_embedding)\r\n\r\n teacher_pred_traj_offset_lis.append(traj_offset)\r\n teacher_pred_ctrl_offset_lis.append(ctrl_offset)\r\n\r\n stored_BEV_feat.append(updated_BEV_feat)\r\n current_BEV_feat = updated_BEV_feat\r\n\r\n stored_flattened_BEV_feat.append(updated_flattend_BEV_feat)\r\n current_flattened_BEV_feat = updated_flattend_BEV_feat\r\n\r\n stored_future_BEV_feat.append(updated_future_bev_feat)\r\n future_bev_feat = updated_future_bev_feat\r\n \r\n teacher_pred_traj_offset_lis = torch.stack(teacher_pred_traj_offset_lis, dim=1)\r\n teacher_pred_ctrl_offset_lis = torch.stack(teacher_pred_ctrl_offset_lis, dim=1)\r\n teacher_future_BEV_lis = torch.stack(stored_future_BEV_feat, dim=1)\r\n\r\n outs[\"teacher_pred_wp_offset\"] = teacher_pred_traj_offset_lis\r\n outs[\"teacher_pred_ctrl_offset_lis\"] = teacher_pred_ctrl_offset_lis\r\n outs[\"teacher_future_BEV_feature\"] = teacher_future_BEV_lis\r\n\r\n outs[\"teacher_refine_flattned_BEV_feature\"] = torch.stack(stored_flattened_BEV_feat, dim=1)\r\n outs[\"teacher_refine_BEV_feature\"] = torch.stack(stored_BEV_feat, dim=1)\r\n return outs\r\n\r\n @force_fp32()\r\n def loss(self,\r\n batch,\r\n pred,\r\n mid_BEV_feature, indices_with_gradient=None,\r\n ):\r\n loss_dict = dict()\r\n\r\n gt_speed = batch['speed'].to(dtype=torch.float32).view(-1,1) / 12.\r\n gt_value = batch['value'].view(-1,1)\r\n gt_flattened_BEV_feature = batch['feature']\r\n gt_waypoints = batch['waypoints']\r\n\r\n ## Open Loop Statistics\r\n with torch.no_grad():\r\n pred_action = self._get_action_beta(pred['mu_branches'][:, -1, :], pred['sigma_branches'][:, -1, :])\r\n gt_action = self._get_action_beta(batch['action_mu'], batch['action_sigma'])\r\n l1_action = F.l1_loss(pred_action, gt_action, reduction=\"none\").detach().mean(dim=0)\r\n loss_dict[\"current_throttle_brake_offset\"] = l1_action[0]\r\n loss_dict['current_steer_offset'] = l1_action[1]\r\n wp_offset = F.l1_loss(pred['pred_wp'][:, -1, :, :], gt_waypoints[:, :, :],reduction=\"none\").detach().mean(dim=0)\r\n mean_wp_offset = wp_offset.mean(dim=0) \r\n loss_dict[\"longitudinal_offset\"] = mean_wp_offset[0] \r\n loss_dict[\"lateral_offset\"] = mean_wp_offset[1]\r\n loss_dict[\"longitudinal_offset\"] = mean_wp_offset[0] \r\n loss_dict[\"lateral_offset\"] = mean_wp_offset[1]\r\n\r\n ## Current Action Loss\r\n gt_ctrl_distribution = Beta(batch['action_mu'].unsqueeze(1), batch['action_sigma'].unsqueeze(1))\r\n pred_ctrl_disttribution = Beta(pred['mu_branches'], pred['sigma_branches'])\r\n kl_div = torch.distributions.kl_divergence(gt_ctrl_distribution, pred_ctrl_disttribution)\r\n loss_dict['action_loss'] = kl_div.mean() * self.action_loss_weight\r\n\r\n\r\n loss_dict['speed_loss'] = F.smooth_l1_loss(pred['pred_speed'], gt_speed, reduction=\"mean\")\r\n loss_dict['value_loss'] = (F.smooth_l1_loss(pred['pred_value_traj'], gt_value, reduction=\"mean\") + F.smooth_l1_loss(pred['pred_value_ctrl'], gt_value, reduction=\"none\")) * self.config.value_weight\r\n\r\n loss_dict['flattened_feature_loss'] = (F.smooth_l1_loss(pred['pred_features_traj'], gt_flattened_BEV_feature, reduction=\"mean\") + F.smooth_l1_loss(pred['pred_features_ctrl'], gt_flattened_BEV_feature, reduction=\"mean\")) * self.config.features_weight\r\n\r\n ## Future Action Loss\r\n gt_future_action_mu = torch.stack(batch['future_action_mu'][:-1], axis=1).unsqueeze(1)\r\n gt_future_action_sigma = torch.stack(batch['future_action_sigma'][:-1], axis=1).unsqueeze(1)\r\n gt_future_ctrl_distribution = Beta(gt_future_action_mu, gt_future_action_sigma)\r\n pred_future_ctrl_distribution = Beta(pred['future_mu'], pred['future_sigma'])\r\n future_kl_div = torch.distributions.kl_divergence(gt_future_ctrl_distribution, pred_future_ctrl_distribution)\r\n loss_dict['future_action_loss'] = future_kl_div.mean() * self.action_loss_weight * 0.25\r\n\r\n ## Traj Loss\r\n wp_loss = F.smooth_l1_loss(pred['pred_wp'], gt_waypoints.unsqueeze(1).repeat(1, pred['pred_wp'].shape[1], 1, 1), reduction=\"mean\")\r\n loss_dict['wp_loss'] = wp_loss * self.wp_loss_weight\r\n \r\n ## Feature Disillation Loss\r\n ## BEV Feature Loss of Encoder\r\n for feature_map_index in self.distil_index:\r\n gt_BEV_feature = batch[\"grid_feature\"][feature_map_index]\r\n pred_BEV_feature = mid_BEV_feature[feature_map_index]\r\n loss_dict['BEV_feature_loss'+str(feature_map_index)] = torch.clamp(F.smooth_l1_loss(pred_BEV_feature, gt_BEV_feature, reduction=\"none\"), min=-5.0, max=5.0).mean() * self.distil_kl_loss_weight_dict[feature_map_index]\r\n \r\n ## BEV Feature Loss of Look Module\r\n pred_BEV_feature = pred[\"refine_BEV_feature\"]\r\n gt_BEV_feature = batch[\"grid_feature\"][2].unsqueeze(1).repeat(1, pred_BEV_feature.shape[1], 1, 1, 1) ## 21x21 BEV feature map\r\n loss_dict['refine_BEV_feature_loss2'] = torch.clamp(F.smooth_l1_loss(pred_BEV_feature, gt_BEV_feature, reduction=\"none\"), min=-5.0, max=5.0).mean() * self.distil_kl_loss_weight_dict[2]\r\n\r\n loss_dict['refine_flattened_feature_loss'] = torch.clamp(F.smooth_l1_loss(pred['refine_flattned_BEV_feature'], gt_flattened_BEV_feature.unsqueeze(1).repeat(1, pred['refine_flattned_BEV_feature'].shape[1], 1), reduction=\"none\"), min=-5.0, max=5.0).mean() * self.config.features_weight * 0.1\r\n\r\n # Teacher Forcing Part\r\n ## All offset should be zero\r\n loss_dict['teacher_wp_loss'] = F.smooth_l1_loss(pred['teacher_pred_wp_offset'], torch.zeros_like(pred['teacher_pred_wp_offset']), reduction='mean')\r\n loss_dict['teacher_action_loss'] = F.smooth_l1_loss(pred['teacher_pred_ctrl_offset_lis'], torch.zeros_like(pred['teacher_pred_ctrl_offset_lis']), reduction='mean')\r\n\r\n ## Future Feature Supervision\r\n feature_map_index = 2 ## 21x21 feature map\r\n gt_future_BEV_feature = torch.stack([_[feature_map_index] for _ in batch[\"future_grid_feature\"]], dim=1)\r\n pred_future_BEV_feature = pred[\"teacher_future_BEV_feature\"]\r\n ## NumSamples, RefineNum, TemporalStep, Channel, Width, Height\r\n N,R,T,C,W,H = pred_future_BEV_feature.shape\r\n gt_future_BEV_feature = gt_future_BEV_feature.unsqueeze(1).repeat(1, R, 1, 1, 1, 1)\r\n loss_dict['teacher_future_BEV_feature_loss'+str(feature_map_index)] = torch.clamp(F.smooth_l1_loss(pred_future_BEV_feature, gt_future_BEV_feature, reduction=\"none\"), min=-5.0, max=5.0).mean() * self.distil_kl_loss_weight_dict[2]\r\n\r\n ## BEV Feature Loss of Look Module\r\n pred_BEV_feature = pred[\"teacher_refine_BEV_feature\"]\r\n gt_BEV_feature = batch[\"grid_feature\"][2].unsqueeze(1).repeat(1, pred_BEV_feature.shape[1], 1, 1, 1) ## 21x21 BEV feature map\r\n loss_dict['teacher_refine_BEV_feature_loss'+str(feature_map_index)] = torch.clamp(F.smooth_l1_loss(pred_BEV_feature, gt_BEV_feature, reduction=\"none\"), min=-5.0, max=5.0).mean() * self.distil_kl_loss_weight_dict[2]\r\n\r\n loss_dict['teacher_refine_flattened_feature_loss'] = torch.clamp(F.smooth_l1_loss(pred['teacher_refine_flattned_BEV_feature'], gt_flattened_BEV_feature.unsqueeze(1).repeat(1, pred['teacher_refine_flattned_BEV_feature'].shape[1], 1), reduction=\"none\"), min=-5.0, max=5.0).mean() * self.config.features_weight\r\n return loss_dict\r\n \r\n @force_fp32()\r\n def _get_action_beta(self, alpha, beta):\r\n x = torch.zeros_like(alpha)\r\n x[:, 1] += 0.5\r\n mask1 = (alpha > 1) & (beta > 1)\r\n x[mask1] = (alpha[mask1]-1)/(alpha[mask1]+beta[mask1]-2)\r\n mask2 = (alpha <= 1) & (beta > 1)\r\n x[mask2] = 0.0\r\n mask3 = (alpha > 1) & (beta <= 1)\r\n x[mask3] = 1.0\r\n # mean\r\n mask4 = (alpha <= 1) & (beta <= 1)\r\n x[mask4] = alpha[mask4]/torch.clamp((alpha[mask4]+beta[mask4]), min=1e-5)\r\n x = x * 2 - 1\r\n return x" }, { "path": "open_loop_training/code/model_code/dense_heads/utils.py", "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\ndef sigmoid_focal_loss(\n inputs: torch.Tensor,\n targets: torch.Tensor,\n alpha: float = 0.25,\n gamma: float = 2,\n reduction: str = \"none\",\n) -> torch.Tensor:\n p = torch.sigmoid(inputs)\n ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction=\"none\")\n p_t = p * targets + (1 - p) * (1 - targets)\n loss = ce_loss * ((1 - p_t) ** gamma)\n if alpha >= 0:\n alpha_t = alpha * targets + (1 - alpha) * (1 - targets)\n loss = alpha_t * loss\n if reduction == \"mean\":\n loss = loss.mean()\n elif reduction == \"sum\":\n loss = loss.sum()\n return loss\n\ndef init_weights(m):\n with torch.no_grad():\n classname = m.__class__.__name__\n if classname.find('Conv2d') != -1:\n nn.init.xavier_normal_(m.weight)\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif classname.find('LayerNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('BatchNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('GroupNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('Linear') != -1:\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n nn.init.xavier_normal_(m.weight)\n elif classname.find('Embedding') != -1:\n nn.init.trunc_normal_(m.weight, mean=0, std=0.02)\n\n\n\n\n\nclass SpatialGRU(nn.Module):\n \"\"\"A GRU cell that takes an input tensor [BxTxCxHxW] and an optional previous state and passes a\n convolutional gated recurrent unit over the data\"\"\"\n def __init__(self, input_size, hidden_size, act, gru_bias_init=0.0):\n super().__init__()\n self.input_size = input_size\n self.hidden_size = hidden_size\n self.gru_bias_init = gru_bias_init\n self.act = act\n self.conv_update = nn.Sequential(\n nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size=3, bias=True, padding=1),\n self.act(),\n nn.Conv2d(hidden_size, hidden_size, kernel_size=3, bias=True, padding=1)\n )\n self.conv_reset = nn.Sequential(\n nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size=3, bias=True, padding=1),\n self.act(),\n nn.Conv2d(hidden_size, hidden_size, kernel_size=3, bias=True, padding=1)\n )\n self.conv_state_tilde = nn.Sequential(\n nn.Conv2d(input_size + hidden_size, hidden_size, kernel_size=3, bias=True, padding=1),\n self.act(),\n nn.Conv2d(hidden_size, hidden_size, kernel_size=3, bias=True, padding=1)\n )\n self.conv_decoder = nn.Sequential(\n nn.Conv2d(hidden_size, hidden_size, kernel_size=3, bias=True, padding=1),\n self.act(),\n nn.Conv2d(hidden_size, hidden_size, kernel_size=3, bias=True, padding=1)\n )\n def forward(self, x, state=None):\n # Check size\n assert len(x.size()) == 5, 'Input tensor must be BxTxCxHxW.'\n # recurrent layers\n rnn_output = []\n b, timesteps, c, h, w = x.size()\n rnn_state = torch.zeros(b, self.hidden_size, h, w, device=x.device) if state is None else state\n for t in range(timesteps):\n x_t = x[:, t]\n rnn_state = self.gru_cell(x_t, rnn_state)\n rnn_output.append(self.conv_decoder(rnn_state))\n # reshape rnn output to batch tensor\n return torch.stack(rnn_output, dim=1)\n def gru_cell(self, x, state):\n # Compute gates\n x_and_state = torch.cat([x, state], dim=1)\n update_gate = self.conv_update(x_and_state)\n reset_gate = self.conv_reset(x_and_state)\n # Add bias to initialise gate as close to identity function\n update_gate = torch.sigmoid(update_gate + self.gru_bias_init)\n reset_gate = torch.sigmoid(reset_gate + self.gru_bias_init)\n # Compute proposal state, activation is defined in norm_act_config (can be tanh, ReLU etc)\n state_tilde = self.conv_state_tilde(torch.cat([x, (1.0 - reset_gate) * state], dim=1))\n output = (1.0 - update_gate) * state + update_gate * state_tilde\n return output\n" }, { "path": "open_loop_training/code/utils.py", "content": "from collections import deque\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom mmcv.runner import force_fp32, auto_fp16\n\nclass PIDController(object):\n def __init__(self, K_P=1.0, K_I=0.0, K_D=0.0, n=20):\n self._K_P = K_P\n self._K_I = K_I\n self._K_D = K_D\n\n self._window = deque([0 for _ in range(n)], maxlen=n)\n self._max = 0.0\n self._min = 0.0\n\n def step(self, error):\n self._window.append(error)\n self._max = max(self._max, abs(error))\n self._min = -abs(self._max)\n\n if len(self._window) >= 2:\n integral = np.mean(self._window)\n derivative = (self._window[-1] - self._window[-2])\n else:\n integral = 0.0\n derivative = 0.0\n\n return self._K_P * error + self._K_I * integral + self._K_D * derivative\n\nclass FocalLoss(nn.Module):\n def __init__(self, alpha=0.5, gamma=2, weight=None, ignore_index=255):\n super().__init__()\n self.alpha = alpha\n self.gamma = gamma\n self.weight = weight\n self.ignore_index = ignore_index\n self.ce_fn = nn.CrossEntropyLoss(weight=self.weight, ignore_index=self.ignore_index)\n self.fp16_enabled = False\n\n\n @force_fp32()\n def forward(self, preds, labels):\n logpt = -self.ce_fn(preds.float(), labels)\n pt = torch.exp(logpt)\n loss = -((1 - pt) ** self.gamma) * self.alpha * logpt\n return loss\n\ndef set_dropout_zero(m):\n with torch.no_grad():\n classname = m.__class__.__name__\n if classname.find('Dropout') != -1 or classname.find('Dropout2d') != -1:\n m.p = 0\n elif classname.find('DropPath') != -1:\n m.drop_prob = 0\n\n\n\ndef init_weights(m):\n with torch.no_grad():\n classname = m.__class__.__name__\n if classname.find('Conv2d') != -1:\n nn.init.xavier_normal_(m.weight)\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n elif classname.find('LayerNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('BatchNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('GroupNorm') != -1:\n nn.init.constant_(m.weight, 1)\n nn.init.constant_(m.bias, 0)\n elif classname.find('Linear') != -1:\n if m.bias is not None:\n nn.init.constant_(m.bias, 0)\n nn.init.xavier_normal_(m.weight)\n elif classname.find('Embedding') != -1:\n nn.init.trunc_normal_(m.weight, mean=0, std=0.02)\n\n \n\nclass SEModule(nn.Module):\n def __init__(self, channels, act):\n super(SEModule, self).__init__()\n self.fc1 = nn.Conv2d(channels, channels, kernel_size=1)\n self.act = act()\n self.fc2 = nn.Conv2d(channels, channels, kernel_size=1)\n def forward(self, x):\n x_se = x.mean((2, 3), keepdim=True)\n x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)\n x_se = self.fc1(x_se)\n x_se = self.act(x_se)\n x_se = self.fc2(x_se)\n return x * x_se.sigmoid()\n\n\nclass SEBasicBlock(nn.Module):\n def __init__(self, inplanes, act):\n super(SEBasicBlock, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, inplanes * 2, kernel_size=3, padding=1,)\n self.bn1 = nn.BatchNorm2d(inplanes * 2)\n self.act1 = act()\n self.conv2 = nn.Conv2d(inplanes*2, inplanes, kernel_size=3, padding=1,)\n self.bn2 = nn.BatchNorm2d(inplanes)\n self.act2 = act()\n self.se = SEModule(inplanes, act)\n self.act3 = act()\n def forward(self, x):\n shortcut = x\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.act1(x)\n x = self.conv2(x)\n x = self.bn2(x)\n x = self.act2(x)\n x = self.se(x)\n x += shortcut\n x = self.act3(x)\n return x\n\n" }, { "path": "open_loop_training/configs/_base_/default_runtime.py", "content": "checkpoint_config = dict(interval=1)\n# yapf:disable push\n# By default we use textlogger hook and tensorboard\n# For more loggers see\n# https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook\nlog_config = dict(\n interval=50,\n hooks=[\n dict(type='TextLoggerHook'),\n dict(type='TensorboardLoggerHook')\n ])\n# yapf:enable\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nwork_dir = None\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncustom_hooks = [dict(type=\"MySetEpochInfoHook\")]" }, { "path": "open_loop_training/configs/_base_/schedules/cosine.py", "content": "# This schedule is mainly used by models with dynamic voxelization\n# optimizer\nlr = 0.003 # max learning rate\noptimizer = dict(\n type='AdamW',\n lr=lr,\n betas=(0.95, 0.99), # the momentum is change during training\n weight_decay=0.001)\noptimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))\n\nlr_config = dict(\n policy='CosineAnnealing',\n warmup='linear',\n warmup_iters=1000,\n warmup_ratio=1.0 / 10,\n min_lr_ratio=1e-5)\n\nmomentum_config = None\n\nrunner = dict(type='EpochBasedRunner', max_epochs=40)\n" }, { "path": "open_loop_training/configs/_base_/schedules/cyclic_20e.py", "content": "# For nuScenes dataset, we usually evaluate the model at the end of training.\n# Since the models are trained by 24 epochs by default, we set evaluation\n# interval to be 20. Please change the interval accordingly if you do not\n# use a default schedule.\n# optimizer\n# This schedule is mainly used by models on nuScenes dataset\noptimizer = dict(type='AdamW', lr=1e-4, weight_decay=0.01)\n# max_norm=10 is better for SECOND\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n policy='cyclic',\n target_ratio=(10, 1e-4),\n cyclic_times=1,\n step_ratio_up=0.4,\n)\nmomentum_config = dict(\n policy='cyclic',\n target_ratio=(0.85 / 0.95, 1),\n cyclic_times=1,\n step_ratio_up=0.4,\n)\n\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=20)\n" }, { "path": "open_loop_training/configs/_base_/schedules/cyclic_40e.py", "content": "# The schedule is usually used by models trained on KITTI dataset\n\n# The learning rate set in the cyclic schedule is the initial learning rate\n# rather than the max learning rate. Since the target_ratio is (10, 1e-4),\n# the learning rate will change from 0.0018 to 0.018, than go to 0.0018*1e-4\nlr = 0.0018\n# The optimizer follows the setting in SECOND.Pytorch, but here we use\n# the offcial AdamW optimizer implemented by PyTorch.\noptimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)\noptimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))\n# We use cyclic learning rate and momentum schedule following SECOND.Pytorch\n# https://github.com/traveller59/second.pytorch/blob/3aba19c9688274f75ebb5e576f65cfe54773c021/torchplus/train/learning_schedules_fastai.py#L69 # noqa\n# We implement them in mmcv, for more details, please refer to\n# https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327 # noqa\n# https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130 # noqa\nlr_config = dict(\n policy='cyclic',\n target_ratio=(10, 1e-4),\n cyclic_times=1,\n step_ratio_up=0.4,\n)\nmomentum_config = dict(\n policy='cyclic',\n target_ratio=(0.85 / 0.95, 1),\n cyclic_times=1,\n step_ratio_up=0.4,\n)\n# Although the max_epochs is 40, this schedule is usually used we\n# RepeatDataset with repeat ratio N, thus the actual max epoch\n# number could be Nx40\nrunner = dict(type='EpochBasedRunner', max_epochs=40)\n" }, { "path": "open_loop_training/configs/_base_/schedules/mmdet_schedule_1x.py", "content": "# optimizer\noptimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\n# learning policy\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=500,\n warmup_ratio=0.001,\n step=[8, 11])\nrunner = dict(type='EpochBasedRunner', max_epochs=12)\n" }, { "path": "open_loop_training/configs/_base_/schedules/schedule_2x.py", "content": "# optimizer\n# This schedule is mainly used by models on nuScenes dataset\noptimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)\n# max_norm=10 is better for SECOND\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n policy='step',\n warmup='linear',\n warmup_iters=1000,\n warmup_ratio=1.0 / 1000,\n step=[20, 23])\nmomentum_config = None\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=24)\n" }, { "path": "open_loop_training/configs/_base_/schedules/schedule_3x.py", "content": "# optimizer\n# This schedule is mainly used by models on indoor dataset,\n# e.g., VoteNet on SUNRGBD and ScanNet\nlr = 0.008 # max learning rate\noptimizer = dict(type='AdamW', lr=lr, weight_decay=0.01)\noptimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))\nlr_config = dict(policy='step', warmup=None, step=[24, 32])\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=36)\n" }, { "path": "open_loop_training/configs/_base_/schedules/seg_cosine_150e.py", "content": "# optimizer\n# This schedule is mainly used on S3DIS dataset in segmentation task\noptimizer = dict(type='SGD', lr=0.2, weight_decay=0.0001, momentum=0.9)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='CosineAnnealing', warmup=None, min_lr=0.002)\nmomentum_config = None\n\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=150)\n" }, { "path": "open_loop_training/configs/_base_/schedules/seg_cosine_200e.py", "content": "# optimizer\n# This schedule is mainly used on ScanNet dataset in segmentation task\noptimizer = dict(type='Adam', lr=0.001, weight_decay=0.01)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='CosineAnnealing', warmup=None, min_lr=1e-5)\nmomentum_config = None\n\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=200)\n" }, { "path": "open_loop_training/configs/_base_/schedules/seg_cosine_50e.py", "content": "# optimizer\n# This schedule is mainly used on S3DIS dataset in segmentation task\noptimizer = dict(type='Adam', lr=0.001, weight_decay=0.001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='CosineAnnealing', warmup=None, min_lr=1e-5)\nmomentum_config = None\n\n# runtime settings\nrunner = dict(type='EpochBasedRunner', max_epochs=50)\n" }, { "path": "open_loop_training/configs/thinktwice.py", "content": "_base_ = [\n './_base_/default_runtime.py',\n]\n\ndev_max_sample_per_town = {\"town01\":10, \"town02\":10, \"town03\":10, \"town04\":10, \"town05\":10, \"town06\":10, \"town07\":10, \"town10\":10}\nlocal_dev_train = [\"town01_00\", ]\nlocal_dev_val = [\"town01_00\", ]\n\ntcp_max_sample_per_town = {\"town01\":50384, \"town02\":55943, \"town03\":42771, \"town04\":47954, \"town05\":53684, \"town06\":48415, \"town07\":51549, \"town10\":59898} ## tcp num\ntrain_towns = [\"01\", \"03\", \"04\", \"06\"]\nval_twons = [\"02\", \"05\"]\nindex_per_down = [\"val\"] + [str(_).zfill(2) for _ in range(0, 12)]\n\ntcp_train = []\nfor town in train_towns:\n for town_index in index_per_down:\n tcp_train.append(\"town\"+town + \"_\" + town_index)\n\ntcp_val = []\nfor town in val_twons:\n for town_index in index_per_down:\n tcp_val.append(\"town\"+town + \"_\" + town_index)\n\nfull_towns = [\"01\", \"02\", \"03\", \"04\", \"05\", \"06\", \"07\", \"10\"]\nfull_train = []\nfull_val = []\nfor town in full_towns:\n for town_index in index_per_down:\n if \"val\" == town_index:\n full_val.append(\"town\"+town + \"_\" + town_index)\n else:\n full_train.append(\"town\"+town + \"_\" + town_index)\nmax_sample_per_town_full = {\"town01\":1e9, \"town02\":1e9, \"town03\":1e9, \"town04\":1e9, \"town05\":1e9, \"town06\":1e9, \"town07\":1e9, \"town10\":1e9}\n\nplugin = True\nplugin_dir = 'code/'\n\nimg_aug = True\nSyncBN=True\npoint_cloud_range = [-8.0, -19.2, -4.0, 30.4, 19.2, 10.0] ## The same as Roach\ncfg = dict(\n ###From TCP\n pred_len = 4, # future waypoints predicted\n turn_KP = 0.75,\n turn_KI = 0.75,\n turn_KD = 0.3,\n turn_n = 40, # buffer size\n speed_KP = 5.0,\n speed_KI = 0.5,\n speed_KD = 1.0,\n speed_n = 40, # buffer size\n brake_speed = 0.4, # desired speed below which brake is triggered\n brake_ratio = 1.1, # ratio of speed to desired speed at which brake is triggered\n clip_delta = 0.25, # maximum change in speed input to logitudinal controller\n aim_dist = 4.0, # distance to search around for aim point\n angle_thresh = 0.3, # outlier control detection angle\n dist_thresh = 10, # target point y-distance for outlier filtering\n speed_weight = 0.05,\n value_weight = 0.001,\n features_weight = 0.05,\n img_aug=img_aug,\n\n ## ThinkTwice configuration\n undistort = True, ## Use the intrinsics of undistorted images\n unreal_coord = True, ## Use the coordinate system of Carla - Unreal https://carla.readthedocs.io/en/0.9.10/core_actors/\n is_dev=False, ## Turn it into True when running on a small dataset for debug\n is_local=True, ## Ignore it since we train our model on a cluster with ceph\n is_full=False, ## Set it as True will use the size of dataset exactly the same as TCP, while set it as False will use all possible data recorded in the ../dataset/dataset_metadata.pkl\n\n refine_num=5,\n total_epochs = 60, ## For 16 A100; With less GPUs, you could train less epochs\n\n ceph_conf=\"~/petreloss.conf\",\n FPN_out_channels=[256, 256, 256, 256],\n point_cloud_range=point_cloud_range,\n SyncBN=SyncBN,\n)\n\nckpt_interval = 1\nbatch_size_per_gpu = 8 ## 2 for 3090, 3 for V100, 8 for A100\nif cfg[\"is_dev\"]:\n cfg[\"train_town\"] = local_dev_train\n cfg[\"val_town\"] = local_dev_val\n cfg[\"max_sample_per_town\"] = dev_max_sample_per_town\n num_worker_per_gpu = 0\nelse:\n if cfg[\"is_full\"]:\n cfg[\"train_town\"] = full_train\n cfg[\"val_town\"] = full_val\n cfg[\"max_sample_per_town\"] = max_sample_per_town_full\n else:\n cfg[\"train_town\"] = tcp_train\n cfg[\"val_town\"] = tcp_val\n cfg[\"max_sample_per_town\"] = tcp_max_sample_per_town\n num_worker_per_gpu = 8\n\nbev_h = 21 ## The number of BEV grid\nbev_w = 21 ## The number of BEV grid\ncfg[\"history_query_index_lis\"] = [-1, 0] ## The index of frames used for BEV encoder\ncfg[\"queue_length\"] = len(cfg[\"history_query_index_lis\"]) ## The index of frames used for BEV encoder\n\ncamera_list = ['rgb_front', 'rgb_left', 'rgb_right', 'rgb_back']\nnum_cams = len(camera_list)\ncfg['camera_names'] = camera_list\ncfg[\"num_cams\"] = num_cams\ncfg['use_depth'] = True ## During Training\ncfg['use_seg'] = True ## During Training\ncfg['seg_label_idxs'] = [1,4,5,6,7,8,10,12,18]\ncfg[\"num_seg_type\"] = len(cfg['seg_label_idxs'])+2\n##From BEVDepth: https://github.com/Megvii-BaseDetection/BEVDepth, data augmentation\nida_aug_conf = {\n 'resize_lim': (0.56, 0.6255),\n 'final_dim':(448, 896),\n 'rot_lim': (0, 0),\n 'H': 900,\n 'W': 1600,\n 'rand_flip': True,\n 'bot_pct_lim': (0.0, 0.0),\n}\ncfg[\"img_size\"] = ida_aug_conf['final_dim']\n\nmodel = dict(\n type='EncoderDecoder',\n decoder=dict(\n type='ThinkTwiceDecoder',\n config=cfg,\n bev_h = bev_h,\n bev_w = bev_w,\n ),\n img_encoder=dict(\n type='LSS',\n ## Same BEV range with Roach\n x_bound=[-8.0, 30.4, 1.8285],\n y_bound=[-19.2, 19.2, 1.8285],\n z_bound=[-4, 10, 14],\n d_bound=[1.0, 41.0, 0.5],\n final_dim=cfg[\"img_size\"],\n output_channels=256,\n downsample_factor=16,\n queue_len=cfg['queue_length'],\n img_backbone_conf = dict(\n type='ResNet',\n depth=50,\n frozen_stages=-1,\n out_indices=[0, 1, 2, 3],\n norm_eval=False,\n init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),\n ),\n img_neck_conf = dict(\n type='PAFPN',\n in_channels=[256, 512, 1024, 2048],\n num_outs=4,\n out_channels=256,\n ),\n depth_net_conf=dict(in_channels=512, mid_channels=512),\n seg_net_conf=dict(in_channels=512, out_channels=cfg[\"num_seg_type\"]+1),\n fpn_in_channels=[256, 256, 256, 256],\n ),\n lidar_encoder=dict(\n type='LidarNet',\n pts_voxel_layer=dict(\n max_num_points=10,\n voxel_size=[0.0571428, 0.0571428, 0.2],\n max_voxels=(120000, 160000),\n point_cloud_range = point_cloud_range),\n pts_voxel_encoder=dict(type='HardSimpleVFE', num_features=5),\n pts_middle_encoder=dict(\n type='SparseEncoder_fp32',\n in_channels=5,\n sparse_shape=[41, 672, 672],\n output_channels=128,\n order=('conv', 'norm', 'act'),\n encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128,\n 128)),\n encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, [0, 1, 1]), (0, 0)),\n block_type='basicblock'),\n pts_backbone=dict(\n type='SECOND',\n in_channels=256,\n out_channels=[128, 256],\n layer_nums=[5, 5],\n layer_strides=[1, 2],\n norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),\n conv_cfg=dict(type='Conv2d', bias=False)),\n pts_neck=dict(\n type='SECONDFPN',\n in_channels=[128, 256],\n out_channels=[256, 256],\n upsample_strides=[1, 2],\n norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),\n upsample_cfg=dict(type='deconv', bias=False),\n use_conv_for_no_stride=True),\n ),\n use_depth=cfg['use_depth'],\n num_cams=num_cams,\n train_cfg=cfg,\n test_cfg=cfg,\n)\n\ndataset_type = 'CarlaDataset'\nfile_client_args = dict(backend='disk')\n#fp16 = dict(loss_scale=\"dynamic\") ## Use amp if you are familiar with debugging overflow gradients\n\ntrain_pipeline = [\n dict(type='LoadPoints', is_local=cfg[\"is_local\"], coord_type='LIDAR', load_dim=4, use_dim=4, ceph_conf=cfg[\"ceph_conf\"]), \n dict(type='LoadMultiImages', \n is_local=cfg[\"is_local\"],\n camera_names=camera_list, ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='LoadDepth', is_local=cfg[\"is_local\"], camera_names=camera_list, ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='LoadSeg', is_local=cfg[\"is_local\"], camera_names=camera_list, seg_label_idxs=cfg['seg_label_idxs'], ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='CarlaFormatBundle'),\n dict(type='CarlaCollect', keys=[\n 'img', 'points', 'depth', 'seg',\n 'waypoints', 'target_point', 'target_command', 'target_command_raw',\n 'speed', \"future_speed\", 'value', \"future_value\",\n 'feature', 'future_feature', \"grid_feature\", \"future_grid_feature\",\n 'action_sigma', 'action_mu',\n 'future_action_mu', 'future_action_sigma'],)\n]\ntest_pipeline = [\n dict(type='LoadPoints', is_local=cfg[\"is_local\"], coord_type='LIDAR', load_dim=4, use_dim=4, ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='LoadMultiImages', \n is_local=cfg[\"is_local\"],\n camera_names=camera_list, ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='LoadDepth', is_local=cfg[\"is_local\"], camera_names=camera_list, ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='LoadSeg', is_local=cfg[\"is_local\"], camera_names=camera_list, seg_label_idxs=cfg['seg_label_idxs'], ceph_conf=cfg[\"ceph_conf\"]),\n dict(type='CarlaFormatBundle'),\n dict(type='CarlaCollect', keys=[\n 'img', 'points', 'depth', 'seg',\n 'waypoints', 'target_point', 'target_command', 'target_command_raw',\n 'speed', \"future_speed\", 'value', \"future_value\", \n 'feature', 'future_feature', \"grid_feature\", \"future_grid_feature\",\n 'action_sigma', 'action_mu',\n 'future_action_mu', 'future_action_sigma'])\n]\n\n\ntrain_full_queue_pipeline = [\n dict(type='IDAImageTransform', cfg=cfg, ida_aug_conf=ida_aug_conf, is_train=True),\n dict(type='ImageTransformMulti', aug=True, batch_size=batch_size_per_gpu),\n]\nval_full_queue_pipeline = [\n dict(type='IDAImageTransform', cfg=cfg, ida_aug_conf=ida_aug_conf, is_train=False),\n dict(type='ImageTransformMulti', aug=False, batch_size=batch_size_per_gpu),\n]\n\ndata = dict(\n samples_per_gpu=batch_size_per_gpu,\n workers_per_gpu=num_worker_per_gpu,\n train=dict(\n type=dataset_type,\n cfg=cfg,\n used_town=cfg[\"train_town\"],\n pipeline=train_pipeline,\n full_queue_pipeline=train_full_queue_pipeline,\n is_local=cfg[\"is_local\"],\n test_mode=False\n ),\n val=dict(\n type=dataset_type,\n cfg = cfg,\n used_town=cfg[\"val_town\"],\n pipeline=test_pipeline,\n full_queue_pipeline=val_full_queue_pipeline,\n is_local=cfg[\"is_local\"],\n test_mode = False\n ),\n test=dict(\n type=dataset_type,\n cfg = cfg,\n used_town=cfg[\"val_town\"],\n pipeline=test_pipeline,\n full_queue_pipeline=val_full_queue_pipeline,\n is_local=cfg[\"is_local\"],\n test_mode = False\n ),\n shuffler_sampler=dict(type='DistributedGroupSampler'),\n nonshuffler_sampler=dict(type='DistributedSampler')\n)\n\n\noptimizer = dict(\n type='AdamW',\n lr=1e-4, ## 1e-4 - 16 A100; For less GPUs, please use smaller lr such as 3e-5 for stable training\n weight_decay=1e-7,)\n\noptimizer_config = dict(grad_clip=dict(max_norm=100, norm_type=2))\n# learning policy\nlr_config = dict(\n policy='CosineAnnealing',\n warmup='linear',\n warmup_iters=1000,\n warmup_ratio=1.0 / 3,\n min_lr_ratio=1e-3)\ntotal_epochs = cfg[\"total_epochs\"]\nevaluation = dict(interval=1)\n\nrunner = dict(type='EpochBasedRunner', max_epochs=total_epochs)\n\nif cfg[\"is_dev\"]:\n log_interval = 1\nelse:\n log_interval = 100\n\nlog_config = dict(\n interval=log_interval,\n hooks=[\n dict(type='TextLoggerHook'),\n dict(type='TensorboardLoggerHook')\n ])\n\ncheckpoint_config = dict(interval=ckpt_interval)" }, { "path": "open_loop_training/ops/voxel_pooling/__init__.py", "content": "from .voxel_pooling import voxel_pooling\n\n__all__ = ['voxel_pooling']\n" }, { "path": "open_loop_training/ops/voxel_pooling/src/voxel_pooling_forward.cpp", "content": "// Copyright (c) Megvii Inc. All rights reserved.\n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#define CHECK_CUDA(x) \\\n TORCH_CHECK(x.type().is_cuda(), #x, \" must be a CUDAtensor \")\n#define CHECK_CONTIGUOUS(x) \\\n TORCH_CHECK(x.is_contiguous(), #x, \" must be contiguous \")\n#define CHECK_INPUT(x) \\\n CHECK_CUDA(x); \\\n CHECK_CONTIGUOUS(x)\n\nint voxel_pooling_forward_wrapper(int batch_size, int num_points, int num_channels, int num_voxel_x, int num_voxel_y, int num_voxel_z, at::Tensor geom_xyz_tensor,\n at::Tensor input_features_tensor, at::Tensor output_features_tensor, at::Tensor pos_memo_tensor);\n\nvoid voxel_pooling_forward_kernel_launcher(int batch_size, int num_points, int num_channels, int num_voxel_x, int num_voxel_y, int num_voxel_z, const int *geom_xyz, const float *input_features,\n float *output_features, int *pos_memo, cudaStream_t stream);\n\nint voxel_pooling_forward_wrapper(int batch_size, int num_points, int num_channels, int num_voxel_x, int num_voxel_y, int num_voxel_z, at::Tensor geom_xyz_tensor,\n at::Tensor input_features_tensor, at::Tensor output_features_tensor, at::Tensor pos_memo_tensor) {\n CHECK_INPUT(geom_xyz_tensor);\n CHECK_INPUT(input_features_tensor);\n const int *geom_xyz = geom_xyz_tensor.data_ptr();\n const float *input_features = input_features_tensor.data_ptr();\n float *output_features = output_features_tensor.data_ptr();\n int *pos_memo = pos_memo_tensor.data_ptr();\n\n cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();\n voxel_pooling_forward_kernel_launcher(batch_size, num_points, num_channels, num_voxel_x, num_voxel_y, num_voxel_z, geom_xyz, input_features,\n output_features, pos_memo, stream);\n return 1;\n}\n\nPYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {\n m.def(\"voxel_pooling_forward_wrapper\", &voxel_pooling_forward_wrapper, \"voxel_pooling_forward_wrapper\");\n}\n" }, { "path": "open_loop_training/ops/voxel_pooling/src/voxel_pooling_forward_cuda.cu", "content": "// Copyright (c) Megvii Inc. All rights reserved.\n#include \n#include \n#include \n\n#define THREADS_PER_BLOCK 256\n#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))\n\n__global__ void voxel_pooling_forward_kernel(int batch_size, int num_points, int num_channels, int num_voxel_x,\n int num_voxel_y, int num_voxel_z, const int *geom_xyz,\n const float *input_features, float *output_features, int *pos_memo) {\n // Each thread process only one channel of one voxel.\n int blk_idx = blockIdx.x;\n int thd_idx = threadIdx.x;\n int pt_idx = blk_idx * blockDim.x + thd_idx;\n if (pt_idx >= batch_size * num_points) {\n return;\n } else {\n int batch_idx = pt_idx / num_points;\n int x = geom_xyz[pt_idx * 3];\n int y = geom_xyz[pt_idx * 3 + 1];\n int z = geom_xyz[pt_idx * 3 + 2];\n // if coord of current voxel is out of boundary, return.\n if (x < 0 || x >= num_voxel_x || y < 0 || y >= num_voxel_y || z < 0 || z >= num_voxel_z) {\n return;\n }\n pos_memo[pt_idx * 3] = batch_idx;\n pos_memo[pt_idx * 3 + 1] = y;\n pos_memo[pt_idx * 3 + 2] = x;\n for (int channel_idx = 0; channel_idx < num_channels; channel_idx++) {\n atomicAdd(\n &output_features[(batch_idx * num_voxel_y * num_voxel_x + y * num_voxel_x + x) * num_channels + channel_idx],\n input_features[pt_idx * num_channels + channel_idx]);\n }\n }\n}\n\nvoid voxel_pooling_forward_kernel_launcher(int batch_size, int num_points, int num_channels, int num_voxel_x,\n int num_voxel_y, int num_voxel_z, const int *geom_xyz,\n const float *input_features, float *output_features, int *pos_memo,\n cudaStream_t stream) {\n cudaError_t err;\n\n dim3 blocks(DIVUP(batch_size * num_points, THREADS_PER_BLOCK)); // blockIdx.x(col), blockIdx.y(row)\n dim3 threads(THREADS_PER_BLOCK);\n\n voxel_pooling_forward_kernel<<>>(batch_size, num_points, num_channels, num_voxel_x,\n num_voxel_y, num_voxel_z, geom_xyz, input_features,\n output_features, pos_memo);\n // cudaDeviceSynchronize(); // for using printf in kernel function\n err = cudaGetLastError();\n if (cudaSuccess != err) {\n fprintf(stderr, \"CUDA kernel failed : %s\\n\", cudaGetErrorString(err));\n exit(-1);\n }\n}\n" }, { "path": "open_loop_training/ops/voxel_pooling/voxel_pooling.py", "content": "# Copyright (c) Megvii Inc. All rights reserved.\nimport torch\nfrom torch.autograd import Function\n\nfrom . import voxel_pooling_ext\n\n\nclass VoxelPooling(Function):\n @staticmethod\n def forward(ctx, geom_xyz: torch.Tensor, input_features: torch.Tensor,\n voxel_num: torch.Tensor) -> torch.Tensor:\n \"\"\"Forward function for `voxel pooling.\n\n Args:\n geom_xyz (Tensor): xyz coord for each voxel with the shape\n of [B, N, 3].\n input_features (Tensor): feature for each voxel with the\n shape of [B, N, C].\n voxel_num (Tensor): Number of voxels for each dim with the\n shape of [3].\n\n Returns:\n Tensor: (B, C, H, W) bev feature map.\n \"\"\"\n assert geom_xyz.is_contiguous()\n assert input_features.is_contiguous()\n # no gradient for input_features and geom_feats\n ctx.mark_non_differentiable(geom_xyz)\n grad_input_features = torch.zeros_like(input_features)\n geom_xyz = geom_xyz.reshape(geom_xyz.shape[0], -1, geom_xyz.shape[-1])\n input_features = input_features.reshape(\n (geom_xyz.shape[0], -1, input_features.shape[-1]))\n assert geom_xyz.shape[1] == input_features.shape[1]\n batch_size = input_features.shape[0]\n num_points = input_features.shape[1]\n num_channels = input_features.shape[2]\n output_features = input_features.new_zeros(batch_size, voxel_num[1],\n voxel_num[0], num_channels)\n # Save the position of bev_feature_map for each input point.\n pos_memo = geom_xyz.new_ones(batch_size, num_points, 3) * -1\n voxel_pooling_ext.voxel_pooling_forward_wrapper(\n batch_size,\n num_points,\n num_channels,\n voxel_num[0],\n voxel_num[1],\n voxel_num[2],\n geom_xyz,\n input_features,\n output_features,\n pos_memo,\n )\n # save grad_input_features and pos_memo for backward\n ctx.save_for_backward(grad_input_features, pos_memo)\n return output_features.permute(0, 3, 1, 2)\n\n @staticmethod\n def backward(ctx, grad_output_features):\n (grad_input_features, pos_memo) = ctx.saved_tensors\n kept = (pos_memo != -1)[..., 0]\n grad_input_features_shape = grad_input_features.shape\n grad_input_features = grad_input_features.reshape(\n grad_input_features.shape[0], -1, grad_input_features.shape[-1])\n grad_input_features[kept] = grad_output_features[\n pos_memo[kept][..., 0].long(), :, pos_memo[kept][..., 1].long(),\n pos_memo[kept][..., 2].long()]\n grad_input_features = grad_input_features.reshape(\n grad_input_features_shape)\n return None, grad_input_features, None\n\n\nvoxel_pooling = VoxelPooling.apply\n" }, { "path": "open_loop_training/setup.py", "content": "# setup.py\nfrom setuptools import setup\nimport os\nimport torch\nfrom torch.utils.cpp_extension import BuildExtension, CUDAExtension\n\ndef make_cuda_ext(name,\n module,\n sources,\n sources_cuda=[],\n extra_args=[],\n extra_include_path=[]):\n\n define_macros = []\n extra_compile_args = {'cxx': [] + extra_args}\n\n if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':\n define_macros += [('WITH_CUDA', None)]\n extension = CUDAExtension\n extra_compile_args['nvcc'] = extra_args + [\n '-D__CUDA_NO_HALF_OPERATORS__',\n '-D__CUDA_NO_HALF_CONVERSIONS__',\n '-D__CUDA_NO_HALF2_OPERATORS__',\n ]\n sources += sources_cuda\n else:\n raise EnvironmentError('CUDA is required to compile MMDetection!')\n\n return extension(\n name='{}.{}'.format(module, name),\n sources=[os.path.join(*module.split('.'), p) for p in sources],\n include_dirs=extra_include_path,\n define_macros=define_macros,\n extra_compile_args=extra_compile_args,\n )\n\nsetup(\n name='voxel_pooling',\n ext_modules=[\n make_cuda_ext(\n name='voxel_pooling_ext',\n module='ops.voxel_pooling',\n sources=['src/voxel_pooling_forward.cpp'],\n sources_cuda=['src/voxel_pooling_forward_cuda.cu'],\n ),\n ],\n cmdclass={'build_ext': BuildExtension})\n\n" }, { "path": "open_loop_training/train.py", "content": "# ---------------------------------------------\n# Copyright (c) OpenMMLab. All rights reserved.\n# ---------------------------------------------\n# Modified by Zhiqi Li\n# Modified by Xiaosong Jia\n# ---------------------------------------------\nfrom __future__ import division\nimport argparse\nimport copy\nimport mmcv\nimport os\nimport time\nimport torch\nimport warnings\nfrom mmcv import Config, DictAction\nfrom mmcv.runner import get_dist_info, init_dist\nfrom os import path as osp\n\nfrom mmdet import __version__ as mmdet_version\nfrom mmdet3d import __version__ as mmdet3d_version\n#from mmdet3d.apis import train_model\n\nfrom mmdet3d.datasets import build_dataset\nfrom mmdet3d.models import build_model\nfrom mmdet3d.utils import collect_env, get_root_logger\nfrom mmdet.apis import set_random_seed\nfrom mmseg import __version__ as mmseg_version\n\nfrom mmcv.utils import TORCH_VERSION, digit_version\ntorch.hub.set_dir(\"./ckpt\")\n\ndef str2bool(v):\n if isinstance(v, bool):\n return v\n if v.lower() in ('yes', 'true', 't', 'y', '1'):\n return True\n elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n return False\n else:\n raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Train a detector')\n parser.add_argument('config', help='train config file path')\n parser.add_argument('--work-dir', help='the dir to save logs and models')\n parser.add_argument(\n '--resume-from', help='the checkpoint file to resume from')\n parser.add_argument(\n '--load-from', help='the checkpoint file to resume from')\n parser.add_argument(\n '--no-validate',\n action='store_true',\n help='whether not to evaluate the checkpoint during training')\n group_gpus = parser.add_mutually_exclusive_group()\n group_gpus.add_argument(\n '--gpus',\n type=int,\n help='number of gpus to use '\n '(only applicable to non-distributed training)')\n group_gpus.add_argument(\n '--gpu-ids',\n type=int,\n nargs='+',\n help='ids of gpus to use '\n '(only applicable to non-distributed training)')\n parser.add_argument('--seed', type=int, default=0, help='random seed')\n parser.add_argument(\n '--deterministic',\n action='store_true',\n help='whether to set deterministic options for CUDNN backend.')\n parser.add_argument(\n '--options',\n nargs='+',\n action=DictAction,\n help='override some settings in the used config, the key-value pair '\n 'in xxx=yyy format will be merged into config file (deprecate), '\n 'change to --cfg-options instead.')\n parser.add_argument(\n '--cfg-options',\n nargs='+',\n action=DictAction,\n help='override some settings in the used config, the key-value pair '\n 'in xxx=yyy format will be merged into config file. If the value to '\n 'be overwritten is a list, it should be like key=\"[a,b]\" or key=a,b '\n 'It also allows nested list/tuple values, e.g. key=\"[(a,b),(c,d)]\" '\n 'Note that the quotation marks are necessary and that no white space '\n 'is allowed.')\n parser.add_argument(\n '--launcher',\n choices=['none', 'pytorch', 'slurm', 'mpi'],\n default='none',\n help='job launcher')\n parser.add_argument('--local_rank', type=int, default=0)\n parser.add_argument(\n '--autoscale-lr',\n action='store_true',\n help='automatically scale lr with the number of gpus')\n args = parser.parse_args()\n if 'LOCAL_RANK' not in os.environ:\n os.environ['LOCAL_RANK'] = str(args.local_rank)\n\n if args.options and args.cfg_options:\n raise ValueError(\n '--options and --cfg-options cannot be both specified, '\n '--options is deprecated in favor of --cfg-options')\n if args.options:\n warnings.warn('--options is deprecated in favor of --cfg-options')\n args.cfg_options = args.options\n\n return args\n\n\ndef main():\n args = parse_args()\n cfg = Config.fromfile(args.config)\n if args.cfg_options is not None:\n cfg.merge_from_dict(args.cfg_options)\n # import modules from string list.\n if cfg.get('custom_imports', None):\n from mmcv.utils import import_modules_from_strings\n import_modules_from_strings(**cfg['custom_imports'])\n\n # import modules from plguin/xx, registry will be updated\n if hasattr(cfg, 'plugin'):\n if cfg.plugin:\n import importlib\n if hasattr(cfg, 'plugin_dir'):\n plugin_dir = cfg.plugin_dir\n _module_dir = os.path.dirname(plugin_dir)\n _module_dir = _module_dir.split('/')\n _module_path = _module_dir[0]\n\n for m in _module_dir[1:]:\n _module_path = _module_path + '.' + m\n print(_module_path)\n plg_lib = importlib.import_module(_module_path)\n else:\n # import dir is the dirpath for the config file\n _module_dir = os.path.dirname(args.config)\n _module_dir = _module_dir.split('/')\n _module_path = _module_dir[0]\n for m in _module_dir[1:]:\n _module_path = _module_path + '.' + m\n print(_module_path)\n plg_lib = importlib.import_module(_module_path)\n\n from code.apis.train import custom_train_model\n # set cudnn_benchmark\n if cfg.get('cudnn_benchmark', False):\n torch.backends.cudnn.benchmark = True\n\n # work_dir is determined in this priority: CLI > segment in file > filename\n if args.work_dir is not None:\n # update configs according to CLI args if args.work_dir is not None\n cfg.work_dir = args.work_dir\n elif cfg.get('work_dir', None) is None:\n # use config filename as default work_dir if cfg.work_dir is None\n cfg.work_dir = osp.join('./work_dirs',\n osp.splitext(osp.basename(args.config))[0])\n # if args.resume_from is not None:\n if args.resume_from is not None and osp.isfile(args.resume_from):\n cfg.resume_from = args.resume_from\n if args.load_from is not None and osp.isfile(args.load_from):\n cfg.load_from = args.load_from\n if args.gpu_ids is not None:\n cfg.gpu_ids = args.gpu_ids\n else:\n cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)\n # init distributed env first, since logger depends on the dist info.\n if args.launcher == 'none':\n distributed = False\n else:\n distributed = True\n init_dist(args.launcher, **cfg.dist_params)\n # re-set gpu_ids with distributed training mode\n _, world_size = get_dist_info()\n cfg.gpu_ids = range(world_size)\n \n # create work_dir\n mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n # dump config\n cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))\n # init the logger before other steps\n timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())\n log_file = osp.join(cfg.work_dir, f'{timestamp}.log')\n # specify logger name, if we still use 'mmdet', the output info will be\n # filtered and won't be saved in the log_file\n if cfg.model.type in ['EncoderDecoder3D']:\n logger_name = 'mmseg'\n else:\n logger_name = 'mmdet'\n logger = get_root_logger(\n log_file=log_file, log_level=cfg.log_level, name=logger_name)\n\n # init the meta dict to record some important information such as\n # environment info and seed, which will be logged\n meta = dict()\n # log env info\n env_info_dict = collect_env()\n env_info = '\\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])\n dash_line = '-' * 60 + '\\n'\n logger.info('Environment info:\\n' + dash_line + env_info + '\\n' +\n dash_line)\n meta['env_info'] = env_info\n meta['config'] = cfg.pretty_text\n\n # log some basic info\n logger.info(f'Distributed training: {distributed}')\n logger.info(f'Config:\\n{cfg.pretty_text}')\n\n # set random seeds\n if args.seed is not None:\n logger.info(f'Set random seed to {args.seed}, '\n f'deterministic: {args.deterministic}')\n set_random_seed(args.seed, deterministic=args.deterministic)\n cfg.seed = args.seed\n meta['seed'] = args.seed\n meta['exp_name'] = osp.basename(args.config)\n\n model = build_model(\n cfg.model,\n train_cfg=cfg.get('train_cfg'),\n test_cfg=cfg.get('test_cfg'))\n model.init_weights()\n logger.info(f'Model:\\n{model}')\n datasets = [build_dataset(cfg.data.train)]\n if len(cfg.workflow) == 2:\n val_dataset = copy.deepcopy(cfg.data.val)\n # in case we use a dataset wrapper\n if 'dataset' in cfg.data.train:\n val_dataset.pipeline = cfg.data.train.dataset.pipeline\n else:\n val_dataset.pipeline = cfg.data.train.pipeline\n val_dataset.test_mode = False\n datasets.append(build_dataset(val_dataset))\n custom_train_model(\n model,\n datasets,\n cfg,\n distributed=distributed,\n validate=(not args.no_validate),\n timestamp=timestamp,\n meta=meta)\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "roach/config/config_agent.yaml", "content": "entry_point: roach.rl_birdview_agent:RlBirdviewAgent\nwb_run_path: null\nwb_ckpt_step: null\nenv_wrapper:\n entry_point: roach.utils.rl_birdview_wrapper:RlBirdviewWrapper\n kwargs:\n input_states:\n - control\n - vel_xy\n acc_as_action: true\npolicy:\n entry_point: roach.models.ppo_policy:PpoPolicy\n kwargs:\n policy_head_arch:\n - 256\n - 256\n value_head_arch:\n - 256\n - 256\n features_extractor_entry_point: roach.models.torch_layers:XtMaCNN\n features_extractor_kwargs:\n states_neurons:\n - 256\n - 256\n distribution_entry_point: roach.models.distributions:BetaDistribution\n distribution_kwargs:\n dist_init: null\ntraining:\n entry_point: roach.models.ppo:PPO\n kwargs:\n learning_rate: 1.0e-05\n n_steps_total: 12288\n batch_size: 256\n n_epochs: 20\n gamma: 0.99\n gae_lambda: 0.9\n clip_range: 0.2\n clip_range_vf: null\n ent_coef: 0.01\n explore_coef: 0.05\n vf_coef: 0.5\n max_grad_norm: 0.5\n target_kl: 0.01\n update_adv: false\n lr_schedule_step: 8\nobs_configs:\n birdview:\n module: birdview.chauffeurnet\n width_in_pixels: 192\n pixels_ev_to_bottom: 40\n pixels_per_meter: 5.0\n history_idx:\n - -16\n - -11\n - -6\n - -1\n scale_bbox: true\n scale_mask_col: 1.0\n speed:\n module: actor_state.speed\n control:\n module: actor_state.control\n velocity:\n module: actor_state.velocity\n" }, { "path": "roach/criteria/blocked.py", "content": "import numpy as np\n\n\nclass Blocked():\n\n def __init__(self, speed_threshold=0.1, below_threshold_max_time=90.0):\n self._speed_threshold = speed_threshold\n self._below_threshold_max_time = below_threshold_max_time\n self._time_last_valid_state = None\n\n def tick(self, vehicle, timestamp):\n info = None\n linear_speed = self._calculate_speed(vehicle.get_velocity())\n\n if linear_speed < self._speed_threshold and self._time_last_valid_state:\n if (timestamp['relative_simulation_time'] - self._time_last_valid_state) > self._below_threshold_max_time:\n # The actor has been \"blocked\" for too long\n ev_loc = vehicle.get_location()\n info = {\n 'step': timestamp['step'],\n 'simulation_time': timestamp['relative_simulation_time'],\n 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z]\n }\n else:\n self._time_last_valid_state = timestamp['relative_simulation_time']\n return info\n\n @staticmethod\n def _calculate_speed(carla_velocity):\n return np.linalg.norm([carla_velocity.x, carla_velocity.y])\n" }, { "path": "roach/criteria/collision.py", "content": "import carla\nimport weakref\nimport numpy as np\n\n\nclass Collision():\n def __init__(self, vehicle, carla_world, intensity_threshold=0.0,\n min_area_of_collision=3, max_area_of_collision=5, max_id_time=5):\n blueprint = carla_world.get_blueprint_library().find('sensor.other.collision')\n self._collision_sensor = carla_world.spawn_actor(blueprint, carla.Transform(), attach_to=vehicle)\n self._collision_sensor.listen(lambda event: self._on_collision(weakref.ref(self), event))\n self._collision_info = None\n\n self.registered_collisions = []\n self.last_id = None\n self.collision_time = None\n\n # If closer than this distance, the collision is ignored\n self._min_area_of_collision = min_area_of_collision\n # If further than this distance, the area is forgotten\n self._max_area_of_collision = max_area_of_collision\n # Amount of time the last collision if is remembered\n self._max_id_time = max_id_time\n # intensity_threshold, LBC uses 400, leaderboard does not use it (set to 0)\n self._intensity_threshold = intensity_threshold\n\n def tick(self, vehicle, timestamp):\n ev_loc = vehicle.get_location()\n new_registered_collisions = []\n # Loops through all the previous registered collisions\n for collision_location in self.registered_collisions:\n distance = ev_loc.distance(collision_location)\n # If far away from a previous collision, forget it\n if distance <= self._max_area_of_collision:\n new_registered_collisions.append(collision_location)\n\n self.registered_collisions = new_registered_collisions\n\n if self.last_id and timestamp['relative_simulation_time'] - self.collision_time > self._max_id_time:\n self.last_id = None\n\n info = self._collision_info\n self._collision_info = None\n if info is not None:\n info['step'] -= timestamp['start_frame']\n info['simulation_time'] -= timestamp['start_simulation_time']\n return info\n\n @staticmethod\n def _on_collision(weakself, event):\n self = weakself()\n if not self:\n return\n # Ignore the current one if it's' the same id as before\n if self.last_id == event.other_actor.id:\n return\n # Ignore if it's too close to a previous collision (avoid micro collisions)\n ev_loc = event.actor.get_transform().location\n for collision_location in self.registered_collisions:\n if ev_loc.distance(collision_location) <= self._min_area_of_collision:\n return\n # Ignore if its intensity is smaller than self._intensity_threshold\n impulse = event.normal_impulse\n intensity = np.linalg.norm([impulse.x, impulse.y, impulse.z])\n if intensity < self._intensity_threshold:\n return\n\n # collision_type\n if ('static' in event.other_actor.type_id or 'traffic' in event.other_actor.type_id) \\\n and 'sidewalk' not in event.other_actor.type_id:\n collision_type = 0 # TrafficEventType.COLLISION_STATIC\n elif 'vehicle' in event.other_actor.type_id:\n collision_type = 1 # TrafficEventType.COLLISION_VEHICLE\n elif 'walker' in event.other_actor.type_id:\n collision_type = 2 # TrafficEventType.COLLISION_PEDESTRIAN\n else:\n collision_type = -1\n\n # write to info, all quantities in in world coordinate\n event_loc = event.transform.location\n event_rot = event.transform.rotation\n oa_loc = event.other_actor.get_transform().location\n oa_rot = event.other_actor.get_transform().rotation\n oa_vel = event.other_actor.get_velocity()\n ev_rot = event.actor.get_transform().rotation\n ev_vel = event.actor.get_velocity()\n\n self._collision_info = {\n 'step': event.frame,\n 'simulation_time': event.timestamp,\n 'collision_type': collision_type,\n 'other_actor_id': event.other_actor.id,\n 'other_actor_type_id': event.other_actor.type_id,\n 'intensity': intensity,\n 'normal_impulse': [impulse.x, impulse.y, impulse.z],\n 'event_loc': [event_loc.x, event_loc.y, event_loc.z],\n 'event_rot': [event_rot.roll, event_rot.pitch, event_rot.yaw],\n 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z],\n 'ev_rot': [ev_rot.roll, ev_rot.pitch, ev_rot.yaw],\n 'ev_vel': [ev_vel.x, ev_vel.y, ev_vel.z],\n 'oa_loc': [oa_loc.x, oa_loc.y, oa_loc.z],\n 'oa_rot': [oa_rot.roll, oa_rot.pitch, oa_rot.yaw],\n 'oa_vel': [oa_vel.x, oa_vel.y, oa_vel.z]\n }\n\n self.collision_time = event.timestamp\n\n self.registered_collisions.append(ev_loc)\n\n # Number 0: static objects -> ignore it\n if event.other_actor.id != 0:\n self.last_id = event.other_actor.id\n\n def clean(self):\n self._collision_sensor.stop()\n self._collision_sensor.destroy()\n self._collision_sensor = None\n" }, { "path": "roach/criteria/encounter_light.py", "content": "from carla_gym.utils.traffic_light import TrafficLightHandler\n\n\nclass EncounterLight():\n\n def __init__(self, dist_threshold=7.5):\n self._last_light_id = None\n self._dist_threshold = dist_threshold\n\n def tick(self, vehicle, timestamp):\n info = None\n\n light_state, light_loc, light_id = TrafficLightHandler.get_light_state(\n vehicle, dist_threshold=self._dist_threshold)\n\n if light_id is not None:\n if light_id != self._last_light_id:\n self._last_light_id = light_id\n info = {\n 'step': timestamp['step'],\n 'simulation_time': timestamp['relative_simulation_time'],\n 'id': light_id,\n 'tl_loc': light_loc.tolist()\n }\n\n return info\n" }, { "path": "roach/criteria/outside_route_lane.py", "content": "import carla\nfrom carla_gym.utils.transforms import cast_angle\n\n\nclass OutsideRouteLane():\n\n def __init__(self, carla_map, vehicle_loc,\n allowed_out_distance=1.3, max_allowed_vehicle_angle=120.0, max_allowed_waypint_angle=150.0):\n self._map = carla_map\n self._pre_ego_waypoint = self._map.get_waypoint(vehicle_loc)\n\n self._allowed_out_distance = allowed_out_distance\n self._max_allowed_vehicle_angle = max_allowed_vehicle_angle\n self._max_allowed_waypint_angle = max_allowed_waypint_angle\n\n self._outside_lane_active = False\n self._wrong_lane_active = False\n self._last_road_id = None\n self._last_lane_id = None\n\n def tick(self, vehicle, timestamp, distance_traveled):\n ev_loc = vehicle.get_location()\n ev_yaw = vehicle.get_transform().rotation.yaw\n self._is_outside_driving_lanes(ev_loc)\n self._is_at_wrong_lane(ev_loc, ev_yaw)\n\n info = None\n if self._outside_lane_active or self._wrong_lane_active:\n info = {\n 'step': timestamp['step'],\n 'simulation_time': timestamp['relative_simulation_time'],\n 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z],\n 'distance_traveled': distance_traveled,\n 'outside_lane': self._outside_lane_active,\n 'wrong_lane': self._wrong_lane_active\n }\n return info\n\n def _is_outside_driving_lanes(self, location):\n \"\"\"\n Detects if the ego_vehicle is outside driving/parking lanes\n \"\"\"\n\n current_driving_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True)\n current_parking_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Parking, project_to_road=True)\n\n driving_distance = location.distance(current_driving_wp.transform.location)\n if current_parking_wp is not None: # Some towns have no parking\n parking_distance = location.distance(current_parking_wp.transform.location)\n else:\n parking_distance = float('inf')\n\n if driving_distance >= parking_distance:\n distance = parking_distance\n lane_width = current_parking_wp.lane_width\n else:\n distance = driving_distance\n lane_width = current_driving_wp.lane_width\n\n self._outside_lane_active = distance > (lane_width / 2 + self._allowed_out_distance)\n\n def _is_at_wrong_lane(self, location, yaw):\n \"\"\"\n Detects if the ego_vehicle has invaded a wrong driving lane\n \"\"\"\n\n current_waypoint = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True)\n current_lane_id = current_waypoint.lane_id\n current_road_id = current_waypoint.road_id\n\n # Lanes and roads are too chaotic at junctions\n if current_waypoint.is_junction:\n self._wrong_lane_active = False\n elif self._last_road_id != current_road_id or self._last_lane_id != current_lane_id:\n\n # Route direction can be considered continuous, except after exiting a junction.\n if self._pre_ego_waypoint.is_junction:\n # cast angle to [-180, +180)\n vehicle_lane_angle = cast_angle(\n current_waypoint.transform.rotation.yaw - yaw)\n\n self._wrong_lane_active = abs(vehicle_lane_angle) > self._max_allowed_vehicle_angle\n\n else:\n # Check for a big gap in waypoint directions.\n waypoint_angle = cast_angle(\n current_waypoint.transform.rotation.yaw - self._pre_ego_waypoint.transform.rotation.yaw)\n\n if abs(waypoint_angle) >= self._max_allowed_waypint_angle:\n # Is the ego vehicle going back to the lane, or going out? Take the opposite\n self._wrong_lane_active = not bool(self._wrong_lane_active)\n else:\n # Changing to a lane with the same direction\n self._wrong_lane_active = False\n\n # Remember the last state\n self._last_lane_id = current_lane_id\n self._last_road_id = current_road_id\n self._pre_ego_waypoint = current_waypoint\n" }, { "path": "roach/criteria/route_deviation.py", "content": "class RouteDeviation():\n\n def __init__(self, offroad_min=15, offroad_max=30, max_route_percentage=0.3):\n self._offroad_min = offroad_min\n self._offroad_max = offroad_max\n self._max_route_percentage = max_route_percentage\n self._out_route_distance = 0.0\n\n def tick(self, vehicle, timestamp, ref_waypoint, distance_traveled, route_length):\n ev_loc = vehicle.get_location()\n\n distance = ev_loc.distance(ref_waypoint.transform.location)\n\n # fail if off_route is True\n off_route_max = distance > self._offroad_max\n\n # fail if off_safe_route more than 30% of total route length\n off_route_min = False\n if distance > self._offroad_min:\n self._out_route_distance += distance_traveled\n out_route_percentage = self._out_route_distance / route_length\n if out_route_percentage > self._max_route_percentage:\n off_route_min = True\n\n info = None\n if off_route_max or off_route_min:\n info = {\n 'step': timestamp['step'],\n 'simulation_time': timestamp['relative_simulation_time'],\n 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z],\n 'off_route_max': off_route_max,\n 'off_route_min': off_route_min\n }\n return info\n" }, { "path": "roach/criteria/run_red_light.py", "content": "import carla\nimport shapely.geometry\nfrom carla_gym.utils.traffic_light import TrafficLightHandler\n\n\nclass RunRedLight():\n\n def __init__(self, carla_map, distance_light=30):\n self._map = carla_map\n self._last_red_light_id = None\n self._distance_light = distance_light\n\n def tick(self, vehicle, timestamp):\n ev_tra = vehicle.get_transform()\n ev_loc = ev_tra.location\n ev_dir = ev_tra.get_forward_vector()\n ev_extent = vehicle.bounding_box.extent.x\n\n tail_close_pt = ev_tra.transform(carla.Location(x=-0.8 * ev_extent))\n tail_far_pt = ev_tra.transform(carla.Location(x=-ev_extent - 1.0))\n tail_wp = self._map.get_waypoint(tail_far_pt)\n\n info = None\n for idx_tl in range(TrafficLightHandler.num_tl):\n traffic_light = TrafficLightHandler.list_tl_actor[idx_tl]\n tl_tv_loc = TrafficLightHandler.list_tv_loc[idx_tl]\n if tl_tv_loc.distance(ev_loc) > self._distance_light:\n continue\n if traffic_light.state != carla.TrafficLightState.Red:\n continue\n if self._last_red_light_id and self._last_red_light_id == traffic_light.id:\n continue\n\n for idx_wp in range(len(TrafficLightHandler.list_stopline_wps[idx_tl])):\n wp = TrafficLightHandler.list_stopline_wps[idx_tl][idx_wp]\n wp_dir = wp.transform.get_forward_vector()\n dot_ve_wp = ev_dir.x * wp_dir.x + ev_dir.y * wp_dir.y + ev_dir.z * wp_dir.z\n\n if tail_wp.road_id == wp.road_id and tail_wp.lane_id == wp.lane_id and dot_ve_wp > 0:\n # This light is red and is affecting our lane\n stop_left_loc, stop_right_loc = TrafficLightHandler.list_stopline_vtx[idx_tl][idx_wp]\n # Is the vehicle traversing the stop line?\n if self._is_vehicle_crossing_line((tail_close_pt, tail_far_pt), (stop_left_loc, stop_right_loc)):\n tl_loc = traffic_light.get_location()\n # loc_in_ev = trans_utils.loc_global_to_ref(tl_loc, ev_tra)\n self._last_red_light_id = traffic_light.id\n info = {\n 'step': timestamp['step'],\n 'simulation_time': timestamp['relative_simulation_time'],\n 'id': traffic_light.id,\n 'tl_loc': [tl_loc.x, tl_loc.y, tl_loc.z],\n 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z]\n }\n return info\n\n @staticmethod\n def _is_vehicle_crossing_line(seg1, seg2):\n \"\"\"\n check if vehicle crosses a line segment\n \"\"\"\n line1 = shapely.geometry.LineString([(seg1[0].x, seg1[0].y), (seg1[1].x, seg1[1].y)])\n line2 = shapely.geometry.LineString([(seg2[0].x, seg2[0].y), (seg2[1].x, seg2[1].y)])\n inter = line1.intersection(line2)\n return not inter.is_empty\n" }, { "path": "roach/criteria/run_stop_sign.py", "content": "import carla\nimport numpy as np\n\n\nclass RunStopSign():\n\n def __init__(self, carla_world, proximity_threshold=50.0, speed_threshold=0.1, waypoint_step=1.0):\n self._map = carla_world.get_map()\n self._proximity_threshold = proximity_threshold\n self._speed_threshold = speed_threshold\n self._waypoint_step = waypoint_step\n\n all_actors = carla_world.get_actors()\n self._list_stop_signs = []\n for _actor in all_actors:\n if 'traffic.stop' in _actor.type_id:\n self._list_stop_signs.append(_actor)\n\n self._target_stop_sign = None\n self._stop_completed = False\n self._affected_by_stop = False\n\n def tick(self, vehicle, timestamp):\n info = None\n ev_loc = vehicle.get_location()\n ev_f_vec = vehicle.get_transform().get_forward_vector()\n\n if self._target_stop_sign is None:\n self._target_stop_sign = self._scan_for_stop_sign(vehicle.get_transform())\n if self._target_stop_sign is not None:\n stop_loc = self._target_stop_sign.get_location()\n # info = {\n # 'event': 'encounter',\n # 'step': timestamp['step'],\n # 'simulation_time': timestamp['relative_simulation_time'],\n # 'id': self._target_stop_sign.id,\n # 'stop_loc': [stop_loc.x, stop_loc.y, stop_loc.z],\n # 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z]\n # }\n else:\n # we were in the middle of dealing with a stop sign\n if not self._stop_completed:\n # did the ego-vehicle stop?\n current_speed = self._calculate_speed(vehicle.get_velocity())\n if current_speed < self._speed_threshold:\n self._stop_completed = True\n\n if not self._affected_by_stop:\n stop_t = self._target_stop_sign.get_transform()\n transformed_tv = stop_t.transform(self._target_stop_sign.trigger_volume.location)\n stop_extent = self._target_stop_sign.trigger_volume.extent\n if self.point_inside_boundingbox(ev_loc, transformed_tv, stop_extent):\n self._affected_by_stop = True\n\n if not self.is_affected_by_stop(ev_loc, self._target_stop_sign):\n # is the vehicle out of the influence of this stop sign now?\n if not self._stop_completed and self._affected_by_stop:\n # did we stop?\n stop_loc = self._target_stop_sign.get_transform().location\n # info = {\n # 'event': 'run',\n # 'step': timestamp['step'],\n # 'simulation_time': timestamp['relative_simulation_time'],\n # 'id': self._target_stop_sign.id,\n # 'stop_loc': [stop_loc.x, stop_loc.y, stop_loc.z],\n # 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z]\n # }\n # reset state\n self._target_stop_sign = None\n self._stop_completed = False\n self._affected_by_stop = False\n\n # return info\n\n def _scan_for_stop_sign(self, vehicle_transform):\n target_stop_sign = None\n\n ve_dir = vehicle_transform.get_forward_vector()\n\n wp = self._map.get_waypoint(vehicle_transform.location)\n wp_dir = wp.transform.get_forward_vector()\n\n dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z\n\n if dot_ve_wp > 0: # Ignore all when going in a wrong lane\n for stop_sign in self._list_stop_signs:\n if self.is_affected_by_stop(vehicle_transform.location, stop_sign):\n # this stop sign is affecting the vehicle\n target_stop_sign = stop_sign\n break\n\n return target_stop_sign\n\n def is_affected_by_stop(self, vehicle_loc, stop, multi_step=20):\n \"\"\"\n Check if the given actor is affected by the stop\n \"\"\"\n affected = False\n # first we run a fast coarse test\n stop_t = stop.get_transform()\n stop_location = stop_t.location\n if stop_location.distance(vehicle_loc) > self._proximity_threshold:\n return affected\n\n transformed_tv = stop_t.transform(stop.trigger_volume.location)\n\n # slower and accurate test based on waypoint's horizon and geometric test\n list_locations = [vehicle_loc]\n waypoint = self._map.get_waypoint(vehicle_loc)\n for _ in range(multi_step):\n if waypoint:\n next_wps = waypoint.next(self._waypoint_step)\n if not next_wps:\n break\n waypoint = next_wps[0]\n if not waypoint:\n break\n list_locations.append(waypoint.transform.location)\n\n for actor_location in list_locations:\n if self.point_inside_boundingbox(actor_location, transformed_tv, stop.trigger_volume.extent):\n affected = True\n\n return affected\n\n @staticmethod\n def _calculate_speed(carla_velocity):\n return np.linalg.norm([carla_velocity.x, carla_velocity.y])\n\n @staticmethod\n def point_inside_boundingbox(point, bb_center, bb_extent):\n \"\"\"\n X\n :param point:\n :param bb_center:\n :param bb_extent:\n :return:\n \"\"\"\n # bugfix slim bbox\n bb_extent.x = max(bb_extent.x, bb_extent.y)\n bb_extent.y = max(bb_extent.x, bb_extent.y)\n\n # pylint: disable=invalid-name\n A = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y - bb_extent.y)\n B = carla.Vector2D(bb_center.x + bb_extent.x, bb_center.y - bb_extent.y)\n D = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y + bb_extent.y)\n M = carla.Vector2D(point.x, point.y)\n\n AB = B - A\n AD = D - A\n AM = M - A\n am_ab = AM.x * AB.x + AM.y * AB.y\n ab_ab = AB.x * AB.x + AB.y * AB.y\n am_ad = AM.x * AD.x + AM.y * AD.y\n ad_ad = AD.x * AD.x + AD.y * AD.y\n\n return am_ab > 0 and am_ab < ab_ab and am_ad > 0 and am_ad < ad_ad\n" }, { "path": "roach/models/distributions.py", "content": "from typing import Optional, Tuple\nimport torch as th\nimport torch.nn as nn\nfrom torch.distributions import Beta, Normal\nfrom torch.nn import functional as F\nimport numpy as np\n\n\ndef sum_independent_dims(tensor: th.Tensor) -> th.Tensor:\n if len(tensor.shape) > 1:\n tensor = tensor.sum(dim=1)\n else:\n tensor = tensor.sum()\n return tensor\n\n\nclass DiagGaussianDistribution():\n def __init__(self, action_dim: int, dist_init=None, action_dependent_std=False):\n self.distribution = None\n self.action_dim = action_dim\n self.dist_init = dist_init\n self.action_dependent_std = action_dependent_std\n\n self.low = None\n self.high = None\n self.log_std_max = 2\n self.log_std_min = -20\n\n # [mu, log_std], [0, 1]\n self.acc_exploration_dist = {\n 'go': th.FloatTensor([0.66, -3]),\n 'stop': th.FloatTensor([-0.66, -3])\n }\n self.steer_exploration_dist = {\n 'turn': th.FloatTensor([0.0, -1]),\n 'straight': th.FloatTensor([3.0, 3.0])\n }\n\n if th.cuda.is_available():\n self.device = 'cuda'\n else:\n self.device = 'cpu'\n\n def proba_distribution_net(self, latent_dim: int) -> Tuple[nn.Module, nn.Parameter]:\n mean_actions = nn.Linear(latent_dim, self.action_dim)\n if self.action_dependent_std:\n log_std = nn.Linear(latent_dim, self.action_dim)\n else:\n log_std = nn.Parameter(-2.0*th.ones(self.action_dim), requires_grad=True)\n\n if self.dist_init is not None:\n # log_std.weight.data.fill_(0.01)\n # mean_actions.weight.data.fill_(0.01)\n # acc/steer\n mean_actions.bias.data[0] = self.dist_init[0][0]\n mean_actions.bias.data[1] = self.dist_init[1][0]\n if self.action_dependent_std:\n log_std.bias.data[0] = self.dist_init[0][1]\n log_std.bias.data[1] = self.dist_init[1][1]\n else:\n init_tensor = th.FloatTensor([self.dist_init[0][1], self.dist_init[1][1]])\n log_std = nn.Parameter(init_tensor, requires_grad=True)\n\n return mean_actions, log_std\n\n def proba_distribution(self, mean_actions: th.Tensor, log_std: th.Tensor) -> \"DiagGaussianDistribution\":\n if self.action_dependent_std:\n log_std = th.clamp(log_std, self.log_std_min, self.log_std_max)\n action_std = th.ones_like(mean_actions) * log_std.exp()\n self.distribution = Normal(mean_actions, action_std)\n return self\n\n def log_prob(self, actions: th.Tensor) -> th.Tensor:\n log_prob = self.distribution.log_prob(actions)\n return sum_independent_dims(log_prob)\n\n def entropy_loss(self) -> th.Tensor:\n entropy_loss = -1.0 * self.distribution.entropy()\n return th.mean(entropy_loss)\n\n def exploration_loss(self, exploration_suggests) -> th.Tensor:\n # [('stop'/'go'/None, 'turn'/'straight'/None)]\n # (batch_size, action_dim)\n mu = self.distribution.loc.detach().clone()\n sigma = self.distribution.scale.detach().clone()\n\n for i, (acc_suggest, steer_suggest) in enumerate(exploration_suggests):\n if acc_suggest != '':\n mu[i, 0] = self.acc_exploration_dist[acc_suggest][0]\n sigma[i, 0] = self.acc_exploration_dist[acc_suggest][1]\n if steer_suggest != '':\n mu[i, 1] = self.steer_exploration_dist[steer_suggest][0]\n sigma[i, 1] = self.steer_exploration_dist[steer_suggest][1]\n\n dist_ent = Normal(mu, sigma)\n\n exploration_loss = th.distributions.kl_divergence(dist_ent, self.distribution)\n return th.mean(exploration_loss)\n\n def sample(self) -> th.Tensor:\n return self.distribution.rsample()\n\n def mode(self) -> th.Tensor:\n return self.distribution.mean\n\n def get_actions(self, deterministic: bool = False) -> th.Tensor:\n if deterministic:\n return self.mode()\n return self.sample()\n\n\nclass SquashedDiagGaussianDistribution():\n def __init__(self, action_dim: int, log_std_init: float = 0.0, action_dependent_std=False):\n self.distribution = None\n\n self.action_dim = action_dim\n self.log_std_init = log_std_init\n self.eps = 1e-7\n self.action_dependent_std = action_dependent_std\n\n self.low = -1.0\n self.high = 1.0\n self.log_std_max = 2\n self.log_std_min = -20\n\n self.gaussian_actions = None\n\n def proba_distribution_net(self, latent_dim: int):\n mean_actions = nn.Linear(latent_dim, self.action_dim)\n if self.action_dependent_std:\n log_std = nn.Linear(latent_dim, self.action_dim)\n else:\n log_std = nn.Parameter(th.ones(self.action_dim) * self.log_std_init, requires_grad=True)\n return mean_actions, log_std\n\n def proba_distribution(self, mean_actions: th.Tensor, log_std: th.Tensor):\n if self.action_dependent_std:\n log_std = th.clamp(log_std, self.log_std_min, self.log_std_max)\n action_std = th.ones_like(mean_actions) * log_std.exp()\n self.distribution = Normal(mean_actions, action_std)\n return self\n\n def log_prob(self, actions: th.Tensor, gaussian_actions: Optional[th.Tensor] = None) -> th.Tensor:\n # Inverse tanh\n if gaussian_actions is None:\n gaussian_actions = th.clamp(actions, min=-1.0 + self.eps, max=1.0 - self.eps)\n gaussian_actions = 0.5 * (gaussian_actions.log1p() - (-gaussian_actions).log1p())\n\n # Log likelihood for a Gaussian distribution\n log_prob = self.distribution.log_prob(gaussian_actions)\n log_prob = sum_independent_dims(log_prob)\n\n # sb3 correction\n # log_prob -= th.sum(th.log(1 - actions ** 2 + self.eps), dim=1)\n # spinning-up correction\n log_prob -= (2*(np.log(2) - gaussian_actions - F.softplus(-2*gaussian_actions))).sum(axis=1)\n return log_prob\n\n def entropy(self) -> Optional[th.Tensor]:\n return None\n\n def sample(self) -> th.Tensor:\n return th.tanh(self.distribution.rsample())\n\n def mode(self) -> th.Tensor:\n return th.tanh(self.distribution.mean)\n\n def get_actions(self, deterministic: bool = False) -> th.Tensor:\n if deterministic:\n return self.mode()\n return self.sample()\n\n\nclass BetaDistribution():\n def __init__(self, action_dim=2, dist_init=None):\n assert action_dim == 2\n\n self.distribution = None\n self.action_dim = action_dim\n self.dist_init = dist_init\n self.low = 0.0\n self.high = 1.0\n\n # [beta, alpha], [0, 1]\n self.acc_exploration_dist = {\n # [1, 2.5]\n # [1.5, 1.0]\n 'go': th.FloatTensor([1.0, 2.5]),\n 'stop': th.FloatTensor([1.5, 1.0])\n }\n self.steer_exploration_dist = {\n 'turn': th.FloatTensor([1.0, 1.0]),\n 'straight': th.FloatTensor([3.0, 3.0])\n }\n\n if th.cuda.is_available():\n self.device = 'cuda'\n else:\n self.device = 'cpu'\n\n def proba_distribution_net(self, latent_dim: int) -> Tuple[nn.Module, nn.Module]:\n\n linear_alpha = nn.Linear(latent_dim, self.action_dim)\n linear_beta = nn.Linear(latent_dim, self.action_dim)\n\n if self.dist_init is not None:\n # linear_alpha.weight.data.fill_(0.01)\n # linear_beta.weight.data.fill_(0.01)\n # acc\n linear_alpha.bias.data[0] = self.dist_init[0][1]\n linear_beta.bias.data[0] = self.dist_init[0][0]\n # steer\n linear_alpha.bias.data[1] = self.dist_init[1][1]\n linear_beta.bias.data[1] = self.dist_init[1][0]\n\n alpha = nn.Sequential(linear_alpha, nn.Softplus())\n beta = nn.Sequential(linear_beta, nn.Softplus())\n return alpha, beta\n\n def proba_distribution(self, alpha, beta):\n self.distribution = Beta(alpha, beta)\n return self\n\n def log_prob(self, actions: th.Tensor) -> th.Tensor:\n log_prob = self.distribution.log_prob(actions)\n return sum_independent_dims(log_prob)\n\n def entropy_loss(self) -> th.Tensor:\n entropy_loss = -1.0 * self.distribution.entropy()\n return th.mean(entropy_loss)\n\n def exploration_loss(self, exploration_suggests) -> th.Tensor:\n # [('stop'/'go'/None, 'turn'/'straight'/None)]\n # (batch_size, action_dim)\n alpha = self.distribution.concentration1.detach().clone()\n beta = self.distribution.concentration0.detach().clone()\n\n for i, (acc_suggest, steer_suggest) in enumerate(exploration_suggests):\n if acc_suggest != '':\n beta[i, 0] = self.acc_exploration_dist[acc_suggest][0]\n alpha[i, 0] = self.acc_exploration_dist[acc_suggest][1]\n if steer_suggest != '':\n beta[i, 1] = self.steer_exploration_dist[steer_suggest][0]\n alpha[i, 1] = self.steer_exploration_dist[steer_suggest][1]\n\n dist_ent = Beta(alpha, beta)\n\n exploration_loss = th.distributions.kl_divergence(self.distribution, dist_ent)\n return th.mean(exploration_loss)\n\n def sample(self) -> th.Tensor:\n # Reparametrization trick to pass gradients\n return self.distribution.rsample()\n\n def mode(self) -> th.Tensor:\n alpha = self.distribution.concentration1\n beta = self.distribution.concentration0\n x = th.zeros_like(alpha)\n x[:, 1] += 0.5\n mask1 = (alpha > 1) & (beta > 1)\n x[mask1] = (alpha[mask1]-1)/(alpha[mask1]+beta[mask1]-2)\n\n mask2 = (alpha <= 1) & (beta > 1)\n x[mask2] = 0.0\n\n mask3 = (alpha > 1) & (beta <= 1)\n x[mask3] = 1.0\n\n # mean\n mask4 = (alpha <= 1) & (beta <= 1)\n x[mask4] = self.distribution.mean[mask4]\n\n return x\n\n def get_actions(self, deterministic: bool = False) -> th.Tensor:\n if deterministic:\n return self.mode()\n return self.sample()\n" }, { "path": "roach/models/ppo.py", "content": "import time\nimport torch as th\nimport numpy as np\nfrom collections import deque\nfrom torch.nn import functional as F\n\nfrom stable_baselines3.common.vec_env import VecEnv\nfrom stable_baselines3.common.callbacks import BaseCallback\nfrom stable_baselines3.common.utils import explained_variance\n\nfrom .ppo_buffer import PpoBuffer\n\n\nclass PPO():\n def __init__(self, policy, env,\n learning_rate: float = 1e-5,\n n_steps_total: int = 8192,\n batch_size: int = 256,\n n_epochs: int = 20,\n gamma: float = 0.99,\n gae_lambda: float = 0.9,\n clip_range: float = 0.2,\n clip_range_vf: float = None,\n ent_coef: float = 0.05,\n explore_coef: float = 0.05,\n vf_coef: float = 0.5,\n max_grad_norm: float = 0.5,\n target_kl: float = 0.01,\n update_adv=False,\n lr_schedule_step=None,\n start_num_timesteps: int = 0):\n\n self.policy = policy\n self.env = env\n self.learning_rate = learning_rate\n self.n_steps_total = n_steps_total\n self.n_steps = n_steps_total//env.num_envs\n self.batch_size = batch_size\n self.n_epochs = n_epochs\n self.gamma = gamma\n self.gae_lambda = gae_lambda\n self.clip_range = clip_range\n self.clip_range_vf = clip_range_vf\n self.ent_coef = ent_coef\n self.explore_coef = explore_coef\n self.vf_coef = vf_coef\n self.max_grad_norm = max_grad_norm\n self.target_kl = target_kl\n self.update_adv = update_adv\n self.lr_schedule_step = lr_schedule_step\n self.start_num_timesteps = start_num_timesteps\n self.num_timesteps = start_num_timesteps\n\n self._last_obs = None\n self._last_dones = None\n self.ep_stat_buffer = None\n\n self.buffer = PpoBuffer(self.n_steps, self.env.observation_space, self.env.action_space,\n gamma=self.gamma, gae_lambda=self.gae_lambda, n_envs=self.env.num_envs)\n self.policy = self.policy.to(self.policy.device)\n\n model_parameters = filter(lambda p: p.requires_grad, self.policy.parameters())\n total_params = sum([np.prod(p.size()) for p in model_parameters])\n print(f'trainable parameters: {total_params/1000000:.2f}M')\n\n def collect_rollouts(self, env: VecEnv, callback: BaseCallback,\n rollout_buffer: PpoBuffer, n_rollout_steps: int) -> bool:\n assert self._last_obs is not None, \"No previous observation was provided\"\n n_steps = 0\n rollout_buffer.reset()\n\n self.action_statistics = []\n self.mu_statistics = []\n self.sigma_statistics = []\n\n while n_steps < n_rollout_steps:\n actions, values, log_probs, mu, sigma, _ = self.policy.forward(self._last_obs)\n self.action_statistics.append(actions)\n self.mu_statistics.append(mu)\n self.sigma_statistics.append(sigma)\n\n new_obs, rewards, dones, infos = env.step(actions)\n\n if callback.on_step() is False:\n return False\n\n # update_info_buffer\n for i in np.where(dones)[0]:\n self.ep_stat_buffer.append(infos[i]['episode_stat'])\n\n n_steps += 1\n self.num_timesteps += env.num_envs\n\n rollout_buffer.add(self._last_obs, actions, rewards, self._last_dones, values, log_probs, mu, sigma, infos)\n self._last_obs = new_obs\n self._last_dones = dones\n\n last_values = self.policy.forward_value(self._last_obs)\n rollout_buffer.compute_returns_and_advantage(last_values, dones=self._last_dones)\n\n return True\n\n def train(self):\n for param_group in self.policy.optimizer.param_groups:\n param_group[\"lr\"] = self.learning_rate\n\n entropy_losses, exploration_losses, pg_losses, value_losses, losses = [], [], [], [], []\n clip_fractions = []\n approx_kl_divs = []\n\n # train for gradient_steps epochs\n epoch = 0\n data_len = int(self.buffer.buffer_size * self.buffer.n_envs / self.batch_size)\n for epoch in range(self.n_epochs):\n approx_kl_divs = []\n # Do a complete pass on the rollout buffer\n self.buffer.start_caching(self.batch_size)\n # while self.buffer.sample_queue.qsize() < 3:\n # time.sleep(0.01)\n for i in range(data_len):\n\n if self.buffer.sample_queue.empty():\n while self.buffer.sample_queue.empty():\n # print(f'buffer_empty: {self.buffer.sample_queue.qsize()}')\n time.sleep(0.01)\n rollout_data = self.buffer.sample_queue.get()\n\n values, log_prob, entropy_loss, exploration_loss, distribution = self.policy.evaluate_actions(\n rollout_data.observations, rollout_data.actions, rollout_data.exploration_suggests,\n detach_values=False)\n # Normalize advantage\n advantages = rollout_data.advantages\n # advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)\n\n # ratio between old and new policy, should be one at the first iteration\n ratio = th.exp(log_prob - rollout_data.old_log_prob)\n\n # clipped surrogate loss\n policy_loss_1 = advantages * ratio\n policy_loss_2 = advantages * th.clamp(ratio, 1 - self.clip_range, 1 + self.clip_range)\n policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()\n\n # Logging\n clip_fraction = th.mean((th.abs(ratio - 1) > self.clip_range).float()).item()\n clip_fractions.append(clip_fraction)\n\n if self.clip_range_vf is None:\n # No clipping\n values_pred = values\n else:\n # Clip the different between old and new value\n # NOTE: this depends on the reward scaling\n values_pred = rollout_data.old_values + th.clamp(values - rollout_data.old_values,\n -self.clip_range_vf, self.clip_range_vf)\n # Value loss using the TD(gae_lambda) target\n value_loss = F.mse_loss(rollout_data.returns, values_pred)\n\n loss = policy_loss + self.vf_coef * value_loss \\\n + self.ent_coef * entropy_loss + self.explore_coef * exploration_loss\n\n losses.append(loss.item())\n pg_losses.append(policy_loss.item())\n value_losses.append(value_loss.item())\n entropy_losses.append(entropy_loss.item())\n exploration_losses.append(exploration_loss.item())\n\n # Optimization step\n self.policy.optimizer.zero_grad()\n loss.backward()\n # Clip grad norm\n th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)\n self.policy.optimizer.step()\n\n with th.no_grad():\n old_distribution = self.policy.action_dist.proba_distribution(\n rollout_data.old_mu, rollout_data.old_sigma)\n kl_div = th.distributions.kl_divergence(old_distribution.distribution, distribution)\n\n approx_kl_divs.append(kl_div.mean().item())\n\n if self.target_kl is not None and np.mean(approx_kl_divs) > 1.5 * self.target_kl:\n if self.lr_schedule_step is not None:\n self.kl_early_stop += 1\n if self.kl_early_stop >= self.lr_schedule_step:\n self.learning_rate *= 0.5\n self.kl_early_stop = 0\n break\n\n # update advantages\n if self.update_adv:\n self.buffer.update_values(self.policy)\n last_values = self.policy.forward_value(self._last_obs)\n self.buffer.compute_returns_and_advantage(last_values, dones=self._last_dones)\n\n explained_var = explained_variance(self.buffer.returns.flatten(), self.buffer.values.flatten())\n\n # Logs\n self.train_debug = {\n \"train/entropy_loss\": np.mean(entropy_losses),\n \"train/exploration_loss\": np.mean(exploration_losses),\n \"train/policy_gradient_loss\": np.mean(pg_losses),\n \"train/value_loss\": np.mean(value_losses),\n \"train/last_epoch_kl\": np.mean(approx_kl_divs),\n \"train/clip_fraction\": np.mean(clip_fractions),\n \"train/loss\": np.mean(losses),\n \"train/explained_variance\": explained_var,\n \"train/clip_range\": self.clip_range,\n \"train/train_epoch\": epoch,\n \"train/learning_rate\": self.learning_rate\n }\n\n def learn(self, total_timesteps, callback=None, seed=2021):\n # reset env seed\n self.env.action_space.seed(seed)\n self.env.observation_space.seed(seed)\n self.env.seed(seed)\n\n self.start_time = time.time()\n\n self.kl_early_stop = 0\n self.t_train_values = 0.0\n\n self.ep_stat_buffer = deque(maxlen=100)\n self._last_obs = self.env.reset()\n self._last_dones = np.zeros((self.env.num_envs,), dtype=np.bool)\n\n callback.init_callback(self)\n\n callback.on_training_start(locals(), globals())\n\n while self.num_timesteps < total_timesteps:\n callback.on_rollout_start()\n t0 = time.time()\n self.policy = self.policy.train()\n continue_training = self.collect_rollouts(self.env, callback, self.buffer, self.n_steps)\n self.t_rollout = time.time() - t0\n callback.on_rollout_end()\n\n if continue_training is False:\n break\n\n t0 = time.time()\n self.train()\n self.t_train = time.time() - t0\n callback.on_training_end()\n\n return self\n\n def _get_init_kwargs(self):\n init_kwargs = dict(\n learning_rate=self.learning_rate,\n n_steps_total=self.n_steps_total,\n batch_size=self.batch_size,\n n_epochs=self.n_epochs,\n gamma=self.gamma,\n gae_lambda=self.gae_lambda,\n clip_range=self.clip_range,\n clip_range_vf=self.clip_range_vf,\n ent_coef=self.ent_coef,\n explore_coef=self.explore_coef,\n vf_coef=self.vf_coef,\n max_grad_norm=self.max_grad_norm,\n target_kl=self.target_kl,\n update_adv=self.update_adv,\n lr_schedule_step=self.lr_schedule_step,\n start_num_timesteps=self.num_timesteps,\n )\n return init_kwargs\n\n def save(self, path: str) -> None:\n th.save({'policy_state_dict': self.policy.state_dict(),\n 'policy_init_kwargs': self.policy.get_init_kwargs(),\n 'train_init_kwargs': self._get_init_kwargs()},\n path)\n\n def get_env(self):\n return self.env\n" }, { "path": "roach/models/ppo_buffer.py", "content": "from gym import spaces\nimport numpy as np\nfrom typing import Optional, Generator, NamedTuple, Dict, List\nimport torch as th\nfrom stable_baselines3.common.vec_env.base_vec_env import tile_images\nimport cv2\nimport time\nfrom threading import Thread\nimport queue\n\nCOLORS = [\n [46, 52, 54],\n [136, 138, 133],\n [255, 0, 255],\n [0, 255, 255],\n [0, 0, 255],\n [255, 0, 0],\n [255, 255, 0],\n [255, 255, 255]\n]\n\n\nclass PpoBufferSamples(NamedTuple):\n observations: Dict[str, th.Tensor]\n actions: th.Tensor\n old_values: th.Tensor\n old_log_prob: th.Tensor\n old_mu: th.Tensor\n old_sigma: th.Tensor\n advantages: th.Tensor\n returns: th.Tensor\n exploration_suggests: List[tuple]\n\n\nclass PpoBuffer():\n def __init__(self, buffer_size: int, observation_space: spaces.Space, action_space: spaces.Space,\n gae_lambda: float = 1, gamma: float = 0.99, n_envs: int = 1):\n\n self.buffer_size = buffer_size\n self.observation_space = observation_space\n self.action_space = action_space\n self.gae_lambda = gae_lambda\n self.gamma = gamma\n self.n_envs = n_envs\n self.reset()\n\n self.pos = 0\n self.full = False\n if th.cuda.is_available():\n self.device = 'cuda'\n else:\n self.device = 'cpu'\n\n self.sample_queue = queue.Queue()\n\n def reset(self) -> None:\n self.observations = {}\n for k, s in self.observation_space.spaces.items():\n self.observations[k] = np.zeros((self.buffer_size, self.n_envs,)+s.shape, dtype=s.dtype)\n # int(np.prod(self.action_space.shape))\n self.actions = np.zeros((self.buffer_size, self.n_envs)+self.action_space.shape, dtype=np.float32)\n self.rewards = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.returns = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.advantages = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.dones = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.values = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.log_probs = np.zeros((self.buffer_size, self.n_envs), dtype=np.float32)\n self.mus = np.zeros((self.buffer_size, self.n_envs)+self.action_space.shape, dtype=np.float32)\n self.sigmas = np.zeros((self.buffer_size, self.n_envs)+self.action_space.shape, dtype=np.float32)\n self.exploration_suggests = np.zeros((self.buffer_size, self.n_envs), dtype=[('acc', 'U10'), ('steer', 'U10')])\n\n self.reward_debugs = [[] for i in range(self.n_envs)]\n self.terminal_debugs = [[] for i in range(self.n_envs)]\n\n self.pos = 0\n self.full = False\n\n def compute_returns_and_advantage(self, last_value: th.Tensor, dones: np.ndarray) -> None:\n last_gae_lam = 0\n for step in reversed(range(self.buffer_size)):\n if step == self.buffer_size - 1:\n next_non_terminal = 1.0 - dones\n next_value = last_value\n # spinning up return calculation\n # self.returns[step] = self.rewards[step] + self.gamma * last_value * next_non_terminal\n else:\n next_non_terminal = 1.0 - self.dones[step + 1]\n next_value = self.values[step + 1]\n # spinning up return calculation\n # self.returns[step] = self.rewards[step] + self.gamma * self.returns[step+1] * next_non_terminal\n delta = self.rewards[step] + self.gamma * next_value * next_non_terminal - self.values[step]\n last_gae_lam = delta + self.gamma * self.gae_lambda * next_non_terminal * last_gae_lam\n self.advantages[step] = last_gae_lam\n\n # sb3 return\n self.returns = self.advantages + self.values\n\n def add(self,\n obs_dict: Dict[str, np.ndarray],\n action: np.ndarray,\n reward: np.ndarray,\n done: np.ndarray,\n value: np.ndarray,\n log_prob: np.ndarray,\n mu: np.ndarray,\n sigma: np.ndarray,\n infos) -> None:\n\n for k, v in obs_dict.items():\n self.observations[k][self.pos] = v\n self.actions[self.pos] = action\n self.rewards[self.pos] = reward\n self.dones[self.pos] = done\n self.values[self.pos] = value\n self.log_probs[self.pos] = log_prob\n self.mus[self.pos] = mu\n self.sigmas[self.pos] = sigma\n\n for i in range(self.n_envs):\n self.reward_debugs[i].append(infos[i]['reward_debug']['debug_texts'])\n self.terminal_debugs[i].append(infos[i]['terminal_debug']['debug_texts'])\n\n n_steps = infos[i]['terminal_debug']['exploration_suggest']['n_steps']\n if n_steps > 0:\n n_start = max(0, self.pos-n_steps)\n self.exploration_suggests[n_start:self.pos, i] = \\\n infos[i]['terminal_debug']['exploration_suggest']['suggest']\n\n self.pos += 1\n if self.pos == self.buffer_size:\n self.full = True\n\n def update_values(self, policy):\n for i in range(self.buffer_size):\n obs_dict = {}\n for k in self.observations.keys():\n obs_dict[k] = self.observations[k][i]\n values = policy.forward_value(obs_dict)\n self.values[i] = values\n\n def get(self, batch_size: Optional[int] = None) -> Generator[PpoBufferSamples, None, None]:\n assert self.full, ''\n indices = np.random.permutation(self.buffer_size * self.n_envs)\n # Prepare the data\n for tensor in ['actions', 'values', 'log_probs', 'advantages', 'returns',\n 'mus', 'sigmas', 'exploration_suggests']:\n self.__dict__['flat_'+tensor] = self.flatten(self.__dict__[tensor])\n self.flat_observations = {}\n for k in self.observations.keys():\n self.flat_observations[k] = self.flatten(self.observations[k])\n\n # spinning up: the next two lines implement the advantage normalization trick\n adv_mean = np.mean(self.advantages)\n adv_std = np.std(self.advantages) + np.finfo(np.float32).eps\n self.advantages = (self.advantages - adv_mean) / adv_std\n\n # Return everything, don't create minibatches\n if batch_size is None:\n batch_size = self.buffer_size * self.n_envs\n\n start_idx = 0\n while start_idx < self.buffer_size * self.n_envs:\n yield self._get_samples(indices[start_idx:start_idx + batch_size])\n start_idx += batch_size\n\n def _get_samples(self, batch_inds: np.ndarray) -> PpoBufferSamples:\n def to_torch(x):\n return th.as_tensor(x).to(self.device)\n # return th.from_numpy(x.astype(np.float32)).to(self.device)\n\n obs_dict = {}\n for k in self.observations.keys():\n obs_dict[k] = to_torch(self.flat_observations[k][batch_inds])\n\n data = (self.flat_actions[batch_inds],\n self.flat_values[batch_inds],\n self.flat_log_probs[batch_inds],\n self.flat_mus[batch_inds],\n self.flat_sigmas[batch_inds],\n self.flat_advantages[batch_inds],\n self.flat_returns[batch_inds]\n )\n\n data_torch = (obs_dict,) + tuple(map(to_torch, data)) + (self.flat_exploration_suggests[batch_inds],)\n return PpoBufferSamples(*data_torch)\n\n @staticmethod\n def flatten(arr: np.ndarray) -> np.ndarray:\n shape = arr.shape\n # if len(shape) < 3:\n # return arr.swapaxes(0, 1).reshape(shape[0] * shape[1])\n # else:\n return arr.reshape(shape[0] * shape[1], *shape[2:])\n\n def render(self):\n assert self.full, ''\n list_render = []\n\n _, _, c, h, w = self.observations['birdview'].shape\n vis_idx = np.array([0, 1, 2, 6, 10, 14])\n\n for i in range(self.buffer_size):\n im_envs = []\n for j in range(self.n_envs):\n\n masks = self.observations['birdview'][i, j, vis_idx, :, :] > 100\n\n im_birdview = np.zeros([h, w, 3], dtype=np.uint8)\n for idx_c in range(len(vis_idx)):\n im_birdview[masks[idx_c]] = COLORS[idx_c]\n\n im = np.zeros([h, w*2, 3], dtype=np.uint8)\n im[:h, :w] = im_birdview\n\n action_str = np.array2string(self.actions[i, j], precision=1, separator=',', suppress_small=True)\n state_str = np.array2string(self.observations['state'][i, j],\n precision=1, separator=',', suppress_small=True)\n\n reward = self.rewards[i, j]\n ret = self.returns[i, j]\n advantage = self.advantages[i, j]\n done = int(self.dones[i, j])\n value = self.values[i, j]\n log_prob = self.log_probs[i, j]\n\n txt_1 = f'v:{value:5.2f} p:{log_prob:5.2f} a{action_str}'\n im = cv2.putText(im, txt_1, (2, 12), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)\n txt_2 = f'{done} {state_str}'\n im = cv2.putText(im, txt_2, (2, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)\n txt_3 = f'rw:{reward:5.2f} rt:{ret:5.2f} a:{advantage:5.2f}'\n im = cv2.putText(im, txt_3, (2, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)\n\n for i_txt, txt in enumerate(self.reward_debugs[j][i] + self.terminal_debugs[j][i]):\n im = cv2.putText(im, txt, (w, (i_txt+1)*15), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (255, 255, 255), 1)\n\n im_envs.append(im)\n\n big_im = tile_images(im_envs)\n list_render.append(big_im)\n\n return list_render\n\n def start_caching(self, batch_size):\n thread1 = Thread(target=self.cache_to_cuda, args=(batch_size,))\n thread1.start()\n\n def cache_to_cuda(self, batch_size):\n self.sample_queue.queue.clear()\n\n for rollout_data in self.get(batch_size):\n while self.sample_queue.qsize() >= 2:\n time.sleep(0.01)\n self.sample_queue.put(rollout_data)\n\n def size(self) -> int:\n \"\"\"\n :return: (int) The current size of the buffer\n \"\"\"\n if self.full:\n return self.buffer_size\n return self.pos\n" }, { "path": "roach/models/ppo_policy.py", "content": "from typing import Union, Dict, Tuple, Any\nfrom functools import partial\nimport gym\nimport torch as th\nimport torch.nn as nn\nimport numpy as np\n\nfrom roach.utils.config_utils import load_entry_point\n\n\nclass PpoPolicy(nn.Module):\n\n def __init__(self,\n observation_space: gym.spaces.Space,\n action_space: gym.spaces.Space,\n policy_head_arch=[256, 256],\n value_head_arch=[256, 256],\n features_extractor_entry_point=None,\n features_extractor_kwargs={},\n distribution_entry_point=None,\n distribution_kwargs={}):\n\n super(PpoPolicy, self).__init__()\n self.observation_space = observation_space\n self.action_space = action_space\n self.features_extractor_entry_point = features_extractor_entry_point\n self.features_extractor_kwargs = features_extractor_kwargs\n self.distribution_entry_point = distribution_entry_point\n self.distribution_kwargs = distribution_kwargs\n\n if th.cuda.is_available():\n self.device = 'cuda'\n else:\n self.device = 'cpu'\n\n self.optimizer_class = th.optim.Adam\n self.optimizer_kwargs = {'eps': 1e-5}\n\n features_extractor_entry_point = features_extractor_entry_point.replace(\"agents.rl_birdview\",\"roach\")\n features_extractor_class = load_entry_point(features_extractor_entry_point)\n self.features_extractor = features_extractor_class(observation_space, **features_extractor_kwargs)\n\n distribution_entry_point = distribution_entry_point.replace(\"agents.rl_birdview\",\"roach\")\n distribution_class = load_entry_point(distribution_entry_point)\n self.action_dist = distribution_class(int(np.prod(action_space.shape)), **distribution_kwargs)\n\n if 'StateDependentNoiseDistribution' in distribution_entry_point:\n self.use_sde = True\n self.sde_sample_freq = 4\n else:\n self.use_sde = False\n self.sde_sample_freq = None\n\n # best_so_far\n # self.net_arch = [dict(pi=[256, 128, 64], vf=[128, 64])]\n self.policy_head_arch = list(policy_head_arch)\n self.value_head_arch = list(value_head_arch)\n self.activation_fn = nn.ReLU\n self.ortho_init = False\n self._build()\n\n def reset_noise(self, n_envs: int = 1) -> None:\n assert self.use_sde, 'reset_noise() is only available when using gSDE'\n self.action_dist.sample_weights(self.dist_sigma, batch_size=n_envs)\n\n def _build(self) -> None:\n last_layer_dim_pi = self.features_extractor.features_dim\n policy_net = []\n for layer_size in self.policy_head_arch:\n policy_net.append(nn.Linear(last_layer_dim_pi, layer_size))\n policy_net.append(self.activation_fn())\n last_layer_dim_pi = layer_size\n self.policy_head = nn.Sequential(*policy_net).to(self.device)\n # mu->alpha/mean, sigma->beta/log_std (nn.Module, nn.Parameter)\n self.dist_mu, self.dist_sigma = self.action_dist.proba_distribution_net(last_layer_dim_pi)\n last_layer_dim_vf = self.features_extractor.features_dim\n value_net = []\n for layer_size in self.value_head_arch:\n value_net.append(nn.Linear(last_layer_dim_vf, layer_size))\n value_net.append(self.activation_fn())\n last_layer_dim_vf = layer_size\n value_net.append(nn.Linear(last_layer_dim_vf, 1))\n self.value_head = nn.Sequential(*value_net).to(self.device)\n # Init weights: use orthogonal initialization\n # with small initial weight for the output\n if self.ortho_init:\n # check for features_extractor\n # Values from stable-baselines.\n # feature_extractor/mlp values are\n # originally from openai/baselines (default gains/init_scales).\n module_gains = {\n # self.features_extractor: np.sqrt(2),\n self.policy_head: np.sqrt(2),\n self.value_head: np.sqrt(2)\n # self.action_net: 0.01,\n }\n for module, gain in module_gains.items():\n module.apply(partial(self.init_weights, gain=gain))\n\n self.optimizer = self.optimizer_class(self.parameters(), **self.optimizer_kwargs)\n\n def _get_features(self, birdview: th.Tensor, state: th.Tensor) -> th.Tensor:\n \"\"\"\n :param birdview: th.Tensor (num_envs, frame_stack*channel, height, width)\n :param state: th.Tensor (num_envs, state_dim)\n \"\"\"\n birdview = birdview.float() / 255.0\n features, cnn_feature = self.features_extractor(birdview, state)\n return features, cnn_feature\n\n def _get_action_dist_from_features(self, features: th.Tensor):\n latent_pi = self.policy_head(features)\n mu = self.dist_mu(latent_pi)\n if isinstance(self.dist_sigma, nn.Parameter):\n sigma = self.dist_sigma\n else:\n sigma = self.dist_sigma(latent_pi)\n return self.action_dist.proba_distribution(mu, sigma), mu.detach().cpu().numpy(), sigma.detach().cpu().numpy()\n\n def evaluate_actions(self, obs_dict: Dict[str, th.Tensor], actions: th.Tensor, exploration_suggests,\n detach_values=False):\n features, cnn_feature = self._get_features(**obs_dict)\n\n if detach_values:\n detached_features = features.detach()\n values = self.value_head(detached_features)\n else:\n values = self.value_head(features)\n\n distribution, mu, sigma = self._get_action_dist_from_features(features)\n actions = self.scale_action(actions)\n log_prob = distribution.log_prob(actions)\n return values.flatten(), log_prob, distribution.entropy_loss(), \\\n distribution.exploration_loss(exploration_suggests), distribution.distribution\n\n def evaluate_values(self, obs_dict: Dict[str, th.Tensor]):\n features, cnn_feature = self._get_features(**obs_dict)\n values = self.value_head(features)\n distribution, mu, sigma = self._get_action_dist_from_features(features)\n return values.flatten(), distribution.distribution\n\n def forward(self, obs_dict: Dict[str, np.ndarray], deterministic: bool = False, clip_action: bool = False, only_feature: bool = False, feature_input: np.ndarray = None):\n '''\n used in collect_rollouts(), do not clamp actions\n '''\n with th.no_grad():\n if feature_input is None:\n obs_tensor_dict = dict([(k, th.as_tensor(v).to(self.device).unsqueeze(0)) for k, v in obs_dict.items()])\n features, cnn_feature = self._get_features(**obs_tensor_dict)\n else:\n features = th.tensor(feature_input).to(self.device)\n if only_feature:\n return features.cpu().numpy()\n values = self.value_head(features)\n distribution, mu, sigma = self._get_action_dist_from_features(features)\n actions = distribution.get_actions(deterministic=deterministic)\n log_prob = distribution.log_prob(actions)\n actions = actions.cpu().numpy()\n actions = self.unscale_action(actions)\n if clip_action:\n actions = np.clip(actions, self.action_space.low, self.action_space.high)\n values = values.cpu().numpy().flatten()\n log_prob = log_prob.cpu().numpy()\n features = features.cpu().numpy()\n return actions, values, log_prob, mu, sigma, features, cnn_feature\n\n def forward_value(self, obs_dict: Dict[str, np.ndarray]) -> np.ndarray:\n with th.no_grad():\n obs_tensor_dict = dict([(k, th.as_tensor(v).to(self.device)) for k, v in obs_dict.items()])\n features, cnn_feature = self._get_features(**obs_tensor_dict)\n values = self.value_head(features)\n values = values.cpu().numpy().flatten()\n return values\n\n def forward_policy(self, obs_dict: Dict[str, np.ndarray]) -> np.ndarray:\n with th.no_grad():\n obs_tensor_dict = dict([(k, th.as_tensor(v).to(self.device)) for k, v in obs_dict.items()])\n features, cnn_feature = self._get_features(**obs_tensor_dict)\n distribution, mu, sigma = self._get_action_dist_from_features(features)\n return mu, sigma\n\n def scale_action(self, action: th.Tensor, eps=1e-7) -> th.Tensor:\n # input action \\in [a_low, a_high]\n # output action \\in [d_low+eps, d_high-eps]\n d_low, d_high = self.action_dist.low, self.action_dist.high # scalar\n\n if d_low is not None and d_high is not None:\n a_low = th.as_tensor(self.action_space.low.astype(np.float32)).to(action.device)\n a_high = th.as_tensor(self.action_space.high.astype(np.float32)).to(action.device)\n action = (action-a_low)/(a_high-a_low) * (d_high-d_low) + d_low\n action = th.clamp(action, d_low+eps, d_high-eps)\n return action\n\n def unscale_action(self, action: np.ndarray, eps=0.0) -> np.ndarray:\n # input action \\in [d_low, d_high]\n # output action \\in [a_low+eps, a_high-eps]\n d_low, d_high = self.action_dist.low, self.action_dist.high # scalar\n\n if d_low is not None and d_high is not None:\n # batch_size = action.shape[0]\n a_low, a_high = self.action_space.low, self.action_space.high\n # same shape as action [batch_size, action_dim]\n # a_high = np.tile(self.action_space.high, [batch_size, 1])\n action = (action-d_low)/(d_high-d_low) * (a_high-a_low) + a_low\n # action = np.clip(action, a_low+eps, a_high-eps)\n return action\n\n def get_init_kwargs(self) -> Dict[str, Any]:\n init_kwargs = dict(\n observation_space=self.observation_space,\n action_space=self.action_space,\n policy_head_arch=self.policy_head_arch,\n value_head_arch=self.value_head_arch,\n features_extractor_entry_point=self.features_extractor_entry_point,\n features_extractor_kwargs=self.features_extractor_kwargs,\n distribution_entry_point=self.distribution_entry_point,\n distribution_kwargs=self.distribution_kwargs,\n )\n return init_kwargs\n\n @classmethod\n def load(cls, path):\n if th.cuda.is_available():\n device = 'cuda'\n else:\n device = 'cpu'\n saved_variables = th.load(path, map_location=device)\n # Create policy object\n model = cls(**saved_variables['policy_init_kwargs'])\n\n state_dict = saved_variables['policy_state_dict']\n for index in [2, 4, 6, 8, 10]:\n state_dict[\"features_extractor.cnn.\"+str(index//2)+\".weight\"] = state_dict.pop(\"features_extractor.cnn.\"+str(index)+\".weight\")\n state_dict[\"features_extractor.cnn.\"+str(index//2)+\".bias\"] = state_dict.pop(\"features_extractor.cnn.\"+str(index)+\".bias\")\n #OLD!!! odict_keys(['features_extractor.cnn.0.weight', 'features_extractor.cnn.0.bias', 'features_extractor.cnn.2.weight', 'features_extractor.cnn.2.bias', 'features_extractor.cnn.4.weight', 'features_extractor.cnn.4.bias', 'features_extractor.cnn.6.weight', 'features_extractor.cnn.6.bias', 'features_extractor.cnn.8.weight', 'features_extractor.cnn.8.bias', 'features_extractor.cnn.10.weight', 'features_extractor.cnn.10.bias', 'features_extractor.linear.0.weight', 'features_extractor.linear.0.bias', 'features_extractor.linear.2.weight', 'features_extractor.linear.2.bias', 'features_extractor.state_linear.0.weight', 'features_extractor.state_linear.0.bias', 'features_extractor.state_linear.2.weight', 'features_extractor.state_linear.2.bias', 'policy_head.0.weight', 'policy_head.0.bias', 'policy_head.2.weight', 'policy_head.2.bias', 'dist_mu.0.weight', 'dist_mu.0.bias', 'dist_sigma.0.weight', 'dist_sigma.0.bias', 'value_head.0.weight', 'value_head.0.bias', 'value_head.2.weight', 'value_head.2.bias', 'value_head.4.weight', 'value_head.4.bias'])\n # odict_keys(['features_extractor.cnn.0.weight', 'features_extractor.cnn.0.bias', 'features_extractor.cnn.1.weight', 'features_extractor.cnn.1.bias', 'features_extractor.cnn.2.weight', 'features_extractor.cnn.2.bias', 'features_extractor.cnn.3.weight', 'features_extractor.cnn.3.bias', 'features_extractor.cnn.4.weight', 'features_extractor.cnn.4.bias', 'features_extractor.cnn.5.weight', 'features_extractor.cnn.5.bias', 'features_extractor.linear.0.weight', 'features_extractor.linear.0.bias', 'features_extractor.linear.2.weight', 'features_extractor.linear.2.bias', 'features_extractor.state_linear.0.weight', 'features_extractor.state_linear.0.bias', 'features_extractor.state_linear.2.weight', 'features_extractor.state_linear.2.bias', 'policy_head.0.weight', 'policy_head.0.bias', 'policy_head.2.weight', 'policy_head.2.bias', 'dist_mu.0.weight', 'dist_mu.0.bias', 'dist_sigma.0.weight', 'dist_sigma.0.bias', 'value_head.0.weight', 'value_head.0.bias', 'value_head.2.weight', 'value_head.2.bias', 'value_head.4.weight', 'value_head.4.bias'])\n # Load weights\n model.load_state_dict(saved_variables['policy_state_dict'])\n model.to(device)\n return model, saved_variables['train_init_kwargs']\n\n @staticmethod\n def init_weights(module: nn.Module, gain: float = 1) -> None:\n \"\"\"\n Orthogonal initialization (used in PPO and A2C)\n \"\"\"\n if isinstance(module, (nn.Linear, nn.Conv2d)):\n nn.init.orthogonal_(module.weight, gain=gain)\n if module.bias is not None:\n module.bias.data.fill_(0.0)\n" }, { "path": "roach/models/torch_layers.py", "content": "\"\"\"Policies: abstract base class and concrete implementations.\"\"\"\n\nimport torch as th\nimport torch.nn as nn\nimport numpy as np\n\nfrom . import torch_util as tu\n\n\nclass XtMaCNN(nn.Module):\n '''\n Inspired by https://github.com/xtma/pytorch_car_caring\n '''\n\n def __init__(self, observation_space, features_dim=256, states_neurons=[256]):\n super().__init__()\n self.features_dim = features_dim\n\n n_input_channels = observation_space['birdview'].shape[0]\n\n self.cnn = nn.ModuleList([\n nn.Conv2d(n_input_channels, 8, kernel_size=5, stride=2),\n #nn.ReLU(),\n nn.Conv2d(8, 16, kernel_size=5, stride=2),\n #nn.ReLU(),\n nn.Conv2d(16, 32, kernel_size=5, stride=2),\n #nn.ReLU(),\n nn.Conv2d(32, 64, kernel_size=3, stride=2),\n #nn.ReLU(),\n nn.Conv2d(64, 128, kernel_size=3, stride=2),\n #nn.ReLU(),\n nn.Conv2d(128, 256, kernel_size=3, stride=1),]\n #nn.ReLU(),\n #nn.Flatten(),\n )\n self.relu = nn.ReLU()\n # self.cnn = nn.ModuleList(\n # nn.Conv2d(n_input_channels, 8, kernel_size=5, stride=2),\n # nn.ReLU(),\n # nn.Conv2d(8, 16, kernel_size=5, stride=2),\n # nn.ReLU(),\n # nn.Conv2d(16, 32, kernel_size=5, stride=2),\n # nn.ReLU(),\n # nn.Conv2d(32, 64, kernel_size=3, stride=2),\n # nn.ReLU(),\n # nn.Conv2d(64, 128, kernel_size=3, stride=2),\n # nn.ReLU(),\n # nn.Conv2d(128, 256, kernel_size=3, stride=1),\n # nn.ReLU(),\n # #nn.Flatten(),\n # )\n # Compute shape by doing one forward pass\n #with th.no_grad():\n #n_flatten = self.cnn(th.as_tensor(observation_space['birdview'].sample()[None]).float()).flatten(start_dim=1).shape[1]\n n_flatten = 1024\n self.linear = nn.Sequential(nn.Linear(n_flatten+states_neurons[-1], 512), nn.ReLU(),\n nn.Linear(512, features_dim), nn.ReLU())\n\n states_neurons = [observation_space['state'].shape[0]] + states_neurons\n self.state_linear = []\n for i in range(len(states_neurons)-1):\n self.state_linear.append(nn.Linear(states_neurons[i], states_neurons[i+1]))\n self.state_linear.append(nn.ReLU())\n self.state_linear = nn.Sequential(*self.state_linear)\n\n self.apply(self._weights_init)\n\n @staticmethod\n def _weights_init(m):\n if isinstance(m, nn.Conv2d):\n nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('relu'))\n nn.init.constant_(m.bias, 0.1)\n\n def forward(self, birdview, state):\n cnn_feature = []\n feature = birdview\n for i in range(len(self.cnn)):\n feature = self.relu(self.cnn[i](feature))\n if birdview.shape[0] == 1:\n cnn_feature.append(feature[0, ...].detach().cpu().numpy())\n #cnn_feature = self.cnn(birdview)\n x = feature.flatten(start_dim=1)\n latent_state = self.state_linear(state)\n\n # latent_state = state.repeat(1, state.shape[1]*256)\n\n x = th.cat((x, latent_state), dim=1)\n x = self.linear(x)\n return x, cnn_feature\n\n\nclass ImpalaCNN(nn.Module):\n def __init__(self, observation_space, chans=(16, 32, 32, 64, 64), states_neurons=[256],\n features_dim=256, nblock=2, batch_norm=False, final_relu=True):\n # (16, 32, 32)\n super().__init__()\n self.features_dim = features_dim\n self.final_relu = final_relu\n\n # image encoder\n curshape = observation_space['birdview'].shape\n s = 1 / np.sqrt(len(chans)) # per stack scale\n self.stacks = nn.ModuleList()\n for outchan in chans:\n stack = tu.CnnDownStack(curshape[0], nblock=nblock, outchan=outchan, scale=s, batch_norm=batch_norm)\n self.stacks.append(stack)\n curshape = stack.output_shape(curshape)\n\n # dense after concatenate\n n_image_latent = tu.intprod(curshape)\n self.dense = tu.NormedLinear(n_image_latent+states_neurons[-1], features_dim, scale=1.4)\n\n # state encoder\n states_neurons = [observation_space['state'].shape[0]] + states_neurons\n self.state_linear = []\n for i in range(len(states_neurons)-1):\n self.state_linear.append(tu.NormedLinear(states_neurons[i], states_neurons[i+1]))\n self.state_linear.append(nn.ReLU())\n self.state_linear = nn.Sequential(*self.state_linear)\n\n def forward(self, birdview, state):\n # birdview: [b, c, h, w]\n # x = x.to(dtype=th.float32) / self.scale_ob\n\n for layer in self.stacks:\n birdview = layer(birdview)\n\n x = th.flatten(birdview, 1)\n x = th.relu(x)\n\n latent_state = self.state_linear(state)\n\n x = th.cat((x, latent_state), dim=1)\n x = self.dense(x)\n if self.final_relu:\n x = th.relu(x)\n return x\n" }, { "path": "roach/models/torch_util.py", "content": "import torch as th\nfrom torch import nn\nimport math\nimport torch.nn.functional as F\n\n\ndef NormedLinear(*args, scale=1.0, dtype=th.float32, **kwargs):\n \"\"\"\n nn.Linear but with normalized fan-in init\n \"\"\"\n out = nn.Linear(*args, **kwargs)\n out.weight.data *= scale / out.weight.norm(dim=1, p=2, keepdim=True)\n if kwargs.get(\"bias\", True):\n out.bias.data *= 0\n return out\n\n\ndef NormedConv2d(*args, scale=1, **kwargs):\n \"\"\"\n nn.Conv2d but with normalized fan-in init\n \"\"\"\n out = nn.Conv2d(*args, **kwargs)\n out.weight.data *= scale / out.weight.norm(dim=(1, 2, 3), p=2, keepdim=True)\n if kwargs.get(\"bias\", True):\n out.bias.data *= 0\n return out\n\n\ndef intprod(xs):\n \"\"\"\n Product of a sequence of integers\n \"\"\"\n out = 1\n for x in xs:\n out *= x\n return out\n\n\nclass CnnBasicBlock(nn.Module):\n \"\"\"\n Residual basic block (without batchnorm), as in ImpalaCNN\n Preserves channel number and shape\n \"\"\"\n\n def __init__(self, inchan, scale=1, batch_norm=False):\n super().__init__()\n self.inchan = inchan\n self.batch_norm = batch_norm\n s = math.sqrt(scale)\n self.conv0 = NormedConv2d(self.inchan, self.inchan, 3, padding=1, scale=s)\n self.conv1 = NormedConv2d(self.inchan, self.inchan, 3, padding=1, scale=s)\n if self.batch_norm:\n self.bn0 = nn.BatchNorm2d(self.inchan)\n self.bn1 = nn.BatchNorm2d(self.inchan)\n\n def residual(self, x):\n # inplace should be False for the first relu, so that it does not change the input,\n # which will be used for skip connection.\n # getattr is for backwards compatibility with loaded models\n if getattr(self, \"batch_norm\", False):\n x = self.bn0(x)\n x = F.relu(x, inplace=False)\n x = self.conv0(x)\n if getattr(self, \"batch_norm\", False):\n x = self.bn1(x)\n x = F.relu(x, inplace=True)\n x = self.conv1(x)\n return x\n\n def forward(self, x):\n return x + self.residual(x)\n\n\nclass CnnDownStack(nn.Module):\n \"\"\"\n Downsampling stack from Impala CNN\n \"\"\"\n\n def __init__(self, inchan, nblock, outchan, scale=1, pool=True, **kwargs):\n super().__init__()\n self.inchan = inchan\n self.outchan = outchan\n self.pool = pool\n self.firstconv = NormedConv2d(inchan, outchan, 3, padding=1)\n s = scale / math.sqrt(nblock)\n self.blocks = nn.ModuleList(\n [CnnBasicBlock(outchan, scale=s, **kwargs) for _ in range(nblock)]\n )\n\n def forward(self, x):\n x = self.firstconv(x)\n if getattr(self, \"pool\", True):\n x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)\n for block in self.blocks:\n x = block(x)\n return x\n\n def output_shape(self, inshape):\n c, h, w = inshape\n assert c == self.inchan\n if getattr(self, \"pool\", True):\n return (self.outchan, (h + 1) // 2, (w + 1) // 2)\n else:\n return (self.outchan, h, w)" }, { "path": "roach/obs_manager/actor_state/control.py", "content": "import numpy as np\nfrom gym import spaces\n\nfrom roach.obs_manager.obs_manager import ObsManagerBase\n\n\nclass ObsManager():\n\n def __init__(self, obs_configs):\n self._parent_actor = None\n def attach_ego_vehicle(self, parent_actor):\n self._parent_actor = parent_actor\n\n def get_observation(self):\n control = self._parent_actor.vehicle.get_control()\n speed_limit = self._parent_actor.vehicle.get_speed_limit() / 3.6 * 0.8\n obs = {\n 'throttle': np.array([control.throttle], dtype=np.float32),\n 'steer': np.array([control.steer], dtype=np.float32),\n 'brake': np.array([control.brake], dtype=np.float32),\n 'gear': np.array([control.gear], dtype=np.float32),\n 'speed_limit': np.array([speed_limit], dtype=np.float32),\n }\n return obs\n\n def clean(self):\n self._parent_actor = None\n" }, { "path": "roach/obs_manager/actor_state/route.py", "content": "import numpy as np\nfrom gym import spaces\n\nfrom carla_gym.core.obs_manager.obs_manager import ObsManagerBase\nimport carla_gym.utils.transforms as trans_utils\n\n\nclass ObsManager(ObsManagerBase):\n\n def __init__(self, obs_configs):\n self._parent_actor = None\n self._route_steps = 5\n super(ObsManager, self).__init__()\n\n def _define_obs_space(self):\n self.obs_space = spaces.Dict({\n 'lateral_dist': spaces.Box(low=0.0, high=2.0, shape=(1,), dtype=np.float32),\n 'angle_diff': spaces.Box(low=-2.0, high=2.0, shape=(1,), dtype=np.float32),\n 'route_locs': spaces.Box(low=-5.0, high=5.0, shape=(self._route_steps*2,), dtype=np.float32),\n 'dist_remaining': spaces.Box(low=0.0, high=100, shape=(1,), dtype=np.float32)\n })\n\n def attach_ego_vehicle(self, parent_actor):\n self._parent_actor = parent_actor\n\n def get_observation(self):\n ev_transform = self._parent_actor.vehicle.get_transform()\n route_plan = self._parent_actor.route_plan\n\n # lateral_dist\n waypoint, road_option = route_plan[0]\n wp_transform = waypoint.transform\n\n d_vec = ev_transform.location - wp_transform.location\n np_d_vec = np.array([d_vec.x, d_vec.y], dtype=np.float32)\n wp_unit_forward = wp_transform.rotation.get_forward_vector()\n np_wp_unit_right = np.array([-wp_unit_forward.y, wp_unit_forward.x], dtype=np.float32)\n\n lateral_dist = np.abs(np.dot(np_wp_unit_right, np_d_vec))\n lateral_dist = np.clip(lateral_dist, 0, 2)\n\n # angle_diff\n angle_diff = np.deg2rad(np.abs(trans_utils.cast_angle(ev_transform.rotation.yaw - wp_transform.rotation.yaw)))\n angle_diff = np.clip(angle_diff, -2, 2)\n \n # route_locs\n location_list = []\n route_length = len(route_plan)\n for i in range(self._route_steps):\n if i < route_length:\n waypoint, road_option = route_plan[i]\n else:\n waypoint, road_option = route_plan[-1]\n\n wp_location_world_coord = waypoint.transform.location\n wp_location_actor_coord = trans_utils.loc_global_to_ref(wp_location_world_coord, ev_transform)\n location_list += [wp_location_actor_coord.x, wp_location_actor_coord.y]\n \n # dist_remaining_in_km\n dist_remaining_in_km = (self._parent_actor.route_length - self._parent_actor.route_completed) / 1000.0\n\n obs = {\n 'lateral_dist': np.array([lateral_dist], dtype=np.float32),\n 'angle_diff': np.array([angle_diff], dtype=np.float32),\n 'route_locs': np.array(location_list, dtype=np.float32),\n 'dist_remaining': np.array([dist_remaining_in_km], dtype=np.float32)\n }\n return obs\n\n def clean(self):\n self._parent_actor = None\n" }, { "path": "roach/obs_manager/actor_state/speed.py", "content": "import numpy as np\nfrom gym import spaces\n\nfrom carla_gym.core.obs_manager.obs_manager import ObsManagerBase\n\n\nclass ObsManager(ObsManagerBase):\n \"\"\"\n in m/s\n \"\"\"\n\n def __init__(self, obs_configs):\n self._parent_actor = None\n super(ObsManager, self).__init__()\n\n def _define_obs_space(self):\n self.obs_space = spaces.Dict({\n 'speed': spaces.Box(low=-10.0, high=30.0, shape=(1,), dtype=np.float32),\n 'speed_xy': spaces.Box(low=-10.0, high=30.0, shape=(1,), dtype=np.float32),\n 'forward_speed': spaces.Box(low=-10.0, high=30.0, shape=(1,), dtype=np.float32)\n })\n\n def attach_ego_vehicle(self, parent_actor):\n self._parent_actor = parent_actor\n\n def get_observation(self):\n velocity = self._parent_actor.vehicle.get_velocity()\n transform = self._parent_actor.vehicle.get_transform()\n forward_vec = transform.get_forward_vector()\n\n np_vel = np.array([velocity.x, velocity.y, velocity.z])\n np_fvec = np.array([forward_vec.x, forward_vec.y, forward_vec.z])\n\n speed = np.linalg.norm(np_vel)\n speed_xy = np.linalg.norm(np_vel[0:2])\n forward_speed = np.dot(np_vel, np_fvec)\n\n obs = {\n 'speed': np.array([speed], dtype=np.float32),\n 'speed_xy': np.array([speed_xy], dtype=np.float32),\n 'forward_speed': np.array([forward_speed], dtype=np.float32)\n }\n return obs\n\n def clean(self):\n self._parent_actor = None\n" }, { "path": "roach/obs_manager/actor_state/velocity.py", "content": "import numpy as np\nfrom gym import spaces\n\nfrom carla_gym.core.obs_manager.obs_manager import ObsManagerBase\nimport carla_gym.utils.transforms as trans_utils\n\n\nclass ObsManager(ObsManagerBase):\n\n def __init__(self, obs_configs):\n super(ObsManager, self).__init__()\n\n def _define_obs_space(self):\n # acc_x, acc_y: m/s2\n # vel_x, vel_y: m/s\n # vel_angular z: rad/s\n self.obs_space = spaces.Dict({\n 'acc_xy': spaces.Box(low=-1e3, high=1e3, shape=(2,), dtype=np.float32),\n 'vel_xy': spaces.Box(low=-1e2, high=1e2, shape=(2,), dtype=np.float32),\n 'vel_ang_z': spaces.Box(low=-1e3, high=1e3, shape=(1,), dtype=np.float32)\n })\n\n def attach_ego_vehicle(self, parent_actor):\n self._parent_actor = parent_actor\n\n def get_observation(self):\n ev_transform = self._parent_actor.vehicle.get_transform()\n acc_w = self._parent_actor.vehicle.get_acceleration()\n vel_w = self._parent_actor.vehicle.get_velocity()\n ang_w = self._parent_actor.vehicle.get_angular_velocity()\n\n acc_ev = trans_utils.vec_global_to_ref(acc_w, ev_transform.rotation)\n vel_ev = trans_utils.vec_global_to_ref(vel_w, ev_transform.rotation)\n\n obs = {\n 'acc_xy': np.array([acc_ev.x, acc_ev.y], dtype=np.float32),\n 'vel_xy': np.array([vel_ev.x, vel_ev.y], dtype=np.float32),\n 'vel_ang_z': np.array([ang_w.z], dtype=np.float32)\n }\n return obs\n\n def clean(self):\n self._parent_actor = None\n" }, { "path": "roach/obs_manager/birdview/chauffeurnet.py", "content": "import numpy as np\nimport carla\nfrom gym import spaces\nimport cv2 as cv\nfrom collections import deque\nfrom pathlib import Path\nimport h5py\n\nfrom roach.utils.traffic_light import TrafficLightHandler\n\n\nCOLOR_BLACK = (0, 0, 0)\nCOLOR_RED = (255, 0, 0)\nCOLOR_GREEN = (0, 255, 0)\nCOLOR_BLUE = (0, 0, 255)\nCOLOR_CYAN = (0, 255, 255)\nCOLOR_MAGENTA = (255, 0, 255)\nCOLOR_MAGENTA_2 = (255, 140, 255)\nCOLOR_YELLOW = (255, 255, 0)\nCOLOR_YELLOW_2 = (160, 160, 0)\nCOLOR_WHITE = (255, 255, 255)\nCOLOR_ALUMINIUM_0 = (238, 238, 236)\nCOLOR_ALUMINIUM_3 = (136, 138, 133)\nCOLOR_ALUMINIUM_5 = (46, 52, 54)\n\n\ndef tint(color, factor):\n r, g, b = color\n r = int(r + (255-r) * factor)\n g = int(g + (255-g) * factor)\n b = int(b + (255-b) * factor)\n r = min(r, 255)\n g = min(g, 255)\n b = min(b, 255)\n return (r, g, b)\n\n\nclass ObsManager():\n def __init__(self, obs_configs, criteria_stop=None):\n self._width = int(obs_configs['width_in_pixels'])\n self._pixels_ev_to_bottom = obs_configs['pixels_ev_to_bottom']\n self._pixels_per_meter = obs_configs['pixels_per_meter']\n self._history_idx = obs_configs['history_idx']\n self._scale_bbox = obs_configs.get('scale_bbox', True)\n self._scale_mask_col = obs_configs.get('scale_mask_col', 1.1)\n\n self._history_queue = deque(maxlen=20)\n\n self._image_channels = 3\n self._masks_channels = 3 + 3*len(self._history_idx)\n self._parent_actor = None\n self._world = None\n\n self._map_dir = Path(__file__).resolve().parent / 'maps'\n\n self._criteria_stop =criteria_stop\n\n super(ObsManager, self).__init__()\n\n def attach_ego_vehicle(self, ego_vehicle):\n self._parent_actor = ego_vehicle\n self._world = self._parent_actor.get_world()\n\n maps_h5_path = self._map_dir / (self._world.get_map().name + '.h5')\n with h5py.File(maps_h5_path, 'r', libver='latest', swmr=True) as hf:\n self._road = np.array(hf['road'], dtype=np.uint8)\n self._lane_marking_all = np.array(hf['lane_marking_all'], dtype=np.uint8)\n self._lane_marking_white_broken = np.array(hf['lane_marking_white_broken'], dtype=np.uint8)\n\n self._world_offset = np.array(hf.attrs['world_offset_in_meters'], dtype=np.float32)\n assert np.isclose(self._pixels_per_meter, float(hf.attrs['pixels_per_meter']))\n\n self._distance_threshold = np.ceil(self._width / self._pixels_per_meter)\n\n @staticmethod\n def _get_stops(criteria_stop):\n stop_sign = criteria_stop._target_stop_sign\n stops = []\n if (stop_sign is not None) and (not criteria_stop._stop_completed):\n bb_loc = carla.Location(stop_sign.trigger_volume.location)\n bb_ext = carla.Vector3D(stop_sign.trigger_volume.extent)\n bb_ext.x = max(bb_ext.x, bb_ext.y)\n bb_ext.y = max(bb_ext.x, bb_ext.y)\n trans = stop_sign.get_transform()\n stops = [(carla.Transform(trans.location, trans.rotation), bb_loc, bb_ext)]\n return stops\n\n def get_observation(self, route_plan):\n ev_transform = self._parent_actor.get_transform()\n ev_loc = ev_transform.location\n ev_rot = ev_transform.rotation\n ev_bbox = self._parent_actor.bounding_box\n\n def is_within_distance(w):\n c_distance = abs(ev_loc.x - w.location.x) < self._distance_threshold \\\n and abs(ev_loc.y - w.location.y) < self._distance_threshold \\\n and abs(ev_loc.z - w.location.z) < 8.0\n c_ev = abs(ev_loc.x - w.location.x) < 1.0 and abs(ev_loc.y - w.location.y) < 1.0\n return c_distance and (not c_ev)\n \n\n vehicle_bbox_list = self._world.get_level_bbs(carla.CityObjectLabel.Vehicles)\n walker_bbox_list = self._world.get_level_bbs(carla.CityObjectLabel.Pedestrians)\n if self._scale_bbox:\n vehicles = self._get_surrounding_actors(vehicle_bbox_list, is_within_distance, 1.0)\n walkers = self._get_surrounding_actors(walker_bbox_list, is_within_distance, 2.0)\n else:\n vehicles = self._get_surrounding_actors(vehicle_bbox_list, is_within_distance)\n walkers = self._get_surrounding_actors(walker_bbox_list, is_within_distance)\n\n tl_green = TrafficLightHandler.get_stopline_vtx(ev_loc, 0)\n tl_yellow = TrafficLightHandler.get_stopline_vtx(ev_loc, 1)\n tl_red = TrafficLightHandler.get_stopline_vtx(ev_loc, 2)\n stops = self._get_stops(self._criteria_stop)\n\n self._history_queue.append((vehicles, walkers, tl_green, tl_yellow, tl_red, stops))\n\n M_warp = self._get_warp_transform(ev_loc, ev_rot)\n\n # objects with history\n vehicle_masks, walker_masks, tl_green_masks, tl_yellow_masks, tl_red_masks, stop_masks \\\n = self._get_history_masks(M_warp)\n\n # road_mask, lane_mask\n road_mask = cv.warpAffine(self._road, M_warp, (self._width, self._width)).astype(np.bool)\n lane_mask_all = cv.warpAffine(self._lane_marking_all, M_warp, (self._width, self._width)).astype(np.bool)\n lane_mask_broken = cv.warpAffine(self._lane_marking_white_broken, M_warp,\n (self._width, self._width)).astype(np.bool)\n\n # route_mask\n route_mask = np.zeros([self._width, self._width], dtype=np.uint8)\n route_in_pixel = np.array([[self._world_to_pixel(wp.transform.location)]\n for wp, _ in route_plan[0:80]])\n route_warped = cv.transform(route_in_pixel, M_warp)\n cv.polylines(route_mask, [np.round(route_warped).astype(np.int32)], False, 1, thickness=16)\n route_mask = route_mask.astype(np.bool)\n\n # ev_mask\n ev_mask = self._get_mask_from_actor_list([(ev_transform, ev_bbox.location, ev_bbox.extent)], M_warp)\n ev_mask_col = self._get_mask_from_actor_list([(ev_transform, ev_bbox.location,\n ev_bbox.extent*self._scale_mask_col)], M_warp)\n # render\n image = np.zeros([self._width, self._width, 3], dtype=np.uint8)\n image[road_mask] = COLOR_ALUMINIUM_5\n image[route_mask] = COLOR_ALUMINIUM_3\n image[lane_mask_all] = COLOR_MAGENTA\n image[lane_mask_broken] = COLOR_MAGENTA_2\n\n h_len = len(self._history_idx)-1\n for i, mask in enumerate(stop_masks):\n image[mask] = tint(COLOR_YELLOW_2, (h_len-i)*0.2)\n for i, mask in enumerate(tl_green_masks):\n image[mask] = tint(COLOR_GREEN, (h_len-i)*0.2)\n for i, mask in enumerate(tl_yellow_masks):\n image[mask] = tint(COLOR_YELLOW, (h_len-i)*0.2)\n for i, mask in enumerate(tl_red_masks):\n image[mask] = tint(COLOR_RED, (h_len-i)*0.2)\n\n for i, mask in enumerate(vehicle_masks):\n image[mask] = tint(COLOR_BLUE, (h_len-i)*0.2)\n for i, mask in enumerate(walker_masks):\n image[mask] = tint(COLOR_CYAN, (h_len-i)*0.2)\n\n image[ev_mask] = COLOR_WHITE\n # image[obstacle_mask] = COLOR_BLUE\n\n # masks\n c_road = road_mask * 255\n c_route = route_mask * 255\n c_lane = lane_mask_all * 255\n c_lane[lane_mask_broken] = 120\n\n # masks with history\n c_tl_history = []\n for i in range(len(self._history_idx)):\n c_tl = np.zeros([self._width, self._width], dtype=np.uint8)\n c_tl[tl_green_masks[i]] = 80\n c_tl[tl_yellow_masks[i]] = 170\n c_tl[tl_red_masks[i]] = 255\n c_tl[stop_masks[i]] = 255\n c_tl_history.append(c_tl)\n\n c_vehicle_history = [m*255 for m in vehicle_masks]\n c_walker_history = [m*255 for m in walker_masks]\n\n masks = np.stack((c_road, c_route, c_lane, *c_vehicle_history, *c_walker_history, *c_tl_history), axis=2)\n masks = np.transpose(masks, [2, 0, 1])\n\n obs_dict = {'rendered': image, 'masks': masks}\n\n # self._parent_actor.collision_px = np.any(ev_mask_col & walker_masks[-1])\n\n return obs_dict\n\n def _get_history_masks(self, M_warp):\n qsize = len(self._history_queue)\n vehicle_masks, walker_masks, tl_green_masks, tl_yellow_masks, tl_red_masks, stop_masks = [], [], [], [], [], []\n for idx in self._history_idx:\n idx = max(idx, -1 * qsize)\n\n vehicles, walkers, tl_green, tl_yellow, tl_red, stops = self._history_queue[idx]\n\n vehicle_masks.append(self._get_mask_from_actor_list(vehicles, M_warp))\n walker_masks.append(self._get_mask_from_actor_list(walkers, M_warp))\n tl_green_masks.append(self._get_mask_from_stopline_vtx(tl_green, M_warp))\n tl_yellow_masks.append(self._get_mask_from_stopline_vtx(tl_yellow, M_warp))\n tl_red_masks.append(self._get_mask_from_stopline_vtx(tl_red, M_warp))\n stop_masks.append(self._get_mask_from_actor_list(stops, M_warp))\n\n return vehicle_masks, walker_masks, tl_green_masks, tl_yellow_masks, tl_red_masks, stop_masks\n\n def _get_mask_from_stopline_vtx(self, stopline_vtx, M_warp):\n mask = np.zeros([self._width, self._width], dtype=np.uint8)\n for sp_locs in stopline_vtx:\n stopline_in_pixel = np.array([[self._world_to_pixel(x)] for x in sp_locs])\n stopline_warped = cv.transform(stopline_in_pixel, M_warp)\n cv.line(mask, tuple(stopline_warped[0, 0].astype(np.int32)), tuple(stopline_warped[1, 0].astype(np.int32)),\n color=1, thickness=6)\n return mask.astype(np.bool)\n\n def _get_mask_from_actor_list(self, actor_list, M_warp):\n mask = np.zeros([self._width, self._width], dtype=np.uint8)\n for actor_transform, bb_loc, bb_ext in actor_list:\n\n corners = [carla.Location(x=-bb_ext.x, y=-bb_ext.y),\n carla.Location(x=bb_ext.x, y=-bb_ext.y),\n carla.Location(x=bb_ext.x, y=0),\n carla.Location(x=bb_ext.x, y=bb_ext.y),\n carla.Location(x=-bb_ext.x, y=bb_ext.y)]\n corners = [bb_loc + corner for corner in corners]\n\n corners = [actor_transform.transform(corner) for corner in corners]\n corners_in_pixel = np.array([[self._world_to_pixel(corner)] for corner in corners])\n corners_warped = cv.transform(corners_in_pixel, M_warp)\n\n cv.fillConvexPoly(mask, np.round(corners_warped).astype(np.int32), 1)\n return mask.astype(np.bool)\n\n @staticmethod\n def _get_surrounding_actors(bbox_list, criterium, scale=None):\n actors = []\n for bbox in bbox_list:\n is_within_distance = criterium(bbox)\n if is_within_distance:\n bb_loc = carla.Location()\n bb_ext = carla.Vector3D(bbox.extent)\n if scale is not None:\n bb_ext = bb_ext * scale\n bb_ext.x = max(bb_ext.x, 0.8)\n bb_ext.y = max(bb_ext.y, 0.8)\n\n actors.append((carla.Transform(bbox.location, bbox.rotation), bb_loc, bb_ext))\n return actors\n\n def _get_warp_transform(self, ev_loc, ev_rot):\n ev_loc_in_px = self._world_to_pixel(ev_loc)\n yaw = np.deg2rad(ev_rot.yaw)\n\n forward_vec = np.array([np.cos(yaw), np.sin(yaw)])\n right_vec = np.array([np.cos(yaw + 0.5*np.pi), np.sin(yaw + 0.5*np.pi)])\n\n bottom_left = ev_loc_in_px - self._pixels_ev_to_bottom * forward_vec - (0.5*self._width) * right_vec\n top_left = ev_loc_in_px + (self._width-self._pixels_ev_to_bottom) * forward_vec - (0.5*self._width) * right_vec\n top_right = ev_loc_in_px + (self._width-self._pixels_ev_to_bottom) * forward_vec + (0.5*self._width) * right_vec\n\n src_pts = np.stack((bottom_left, top_left, top_right), axis=0).astype(np.float32)\n dst_pts = np.array([[0, self._width-1],\n [0, 0],\n [self._width-1, 0]], dtype=np.float32)\n return cv.getAffineTransform(src_pts, dst_pts)\n\n def _world_to_pixel(self, location, projective=False):\n \"\"\"Converts the world coordinates to pixel coordinates\"\"\"\n x = self._pixels_per_meter * (location.x - self._world_offset[0])\n y = self._pixels_per_meter * (location.y - self._world_offset[1])\n\n if projective:\n p = np.array([x, y, 1], dtype=np.float32)\n else:\n p = np.array([x, y], dtype=np.float32)\n return p\n\n def _world_to_pixel_width(self, width):\n \"\"\"Converts the world units to pixel units\"\"\"\n return self._pixels_per_meter * width\n\n def clean(self):\n self._parent_actor = None\n self._world = None\n self._history_queue.clear()\n" }, { "path": "roach/obs_manager/birdview/hdmap_generate.py", "content": "import numpy as np\nimport carla\nfrom gym import spaces\nimport cv2 as cv\nfrom collections import deque\nfrom pathlib import Path\nimport h5py\n\n\nCOLOR_BLACK = (0, 0, 0)\nCOLOR_RED = (255, 0, 0)\nCOLOR_GREEN = (0, 255, 0)\nCOLOR_BLUE = (0, 0, 255)\nCOLOR_CYAN = (0, 255, 255)\nCOLOR_MAGENTA = (255, 0, 255)\nCOLOR_MAGENTA_2 = (255, 140, 255)\nCOLOR_YELLOW = (255, 255, 0)\nCOLOR_YELLOW_2 = (160, 160, 0)\nCOLOR_WHITE = (255, 255, 255)\nCOLOR_ALUMINIUM_0 = (238, 238, 236)\nCOLOR_ALUMINIUM_3 = (136, 138, 133)\nCOLOR_ALUMINIUM_5 = (46, 52, 54)\n\ndef tint(color, factor):\n r, g, b = color\n r = int(r + (255-r) * factor)\n g = int(g + (255-g) * factor)\n b = int(b + (255-b) * factor)\n r = min(r, 255)\n g = min(g, 255)\n b = min(b, 255)\n return (r, g, b)\n\n" }, { "path": "roach/rl_birdview_agent.py", "content": "import logging\nimport numpy as np\nfrom omegaconf import OmegaConf\nimport copy\n\nfrom roach.utils.config_utils import load_entry_point\n\n\nclass RlBirdviewAgent():\n def __init__(self, path_to_conf_file='config_agent.yaml', ckpt = None):\n\n self._render_dict = None\n self.supervision_dict = None\n self._ckpt = ckpt\n self.setup(path_to_conf_file)\n\n def setup(self, path_to_conf_file):\n cfg = OmegaConf.load(path_to_conf_file)\n cfg = OmegaConf.to_container(cfg)\n\n self._obs_configs = cfg['obs_configs']\n self._train_cfg = cfg['training']\n\n\n self._policy_class = load_entry_point(cfg['policy']['entry_point'])\n self._policy_kwargs = cfg['policy']['kwargs']\n if self._ckpt is None:\n self._policy = None\n else:\n self._policy, self._train_cfg['kwargs'] = self._policy_class.load(self._ckpt)\n self._policy = self._policy.eval()\n\n self._wrapper_class = load_entry_point(cfg['env_wrapper']['entry_point'])\n self._wrapper_kwargs = cfg['env_wrapper']['kwargs']\n\n def run_step(self, input_data, timestamp):\n input_data = copy.deepcopy(input_data)\n\n policy_input = self._wrapper_class.process_obs(input_data, self._wrapper_kwargs['input_states'], train=False)\n\n actions, values, log_probs, mu, sigma, features = self._policy.forward(\n policy_input, deterministic=True, clip_action=True)\n control = self._wrapper_class.process_act(actions, self._wrapper_kwargs['acc_as_action'], train=False)\n self.supervision_dict = {\n 'action': np.array([control.throttle, control.steer, control.brake], dtype=np.float32),\n 'value': values[0],\n 'action_mu': mu[0],\n 'action_sigma': sigma[0],\n 'features': features[0],\n 'speed': input_data['speed']['forward_speed']\n }\n self.supervision_dict = copy.deepcopy(self.supervision_dict)\n\n return control\n\n @property\n def obs_configs(self):\n return self._obs_configs\n" }, { "path": "roach/utils/config_utils.py", "content": "from importlib import import_module\nimport json\nfrom pathlib import Path\nimport socket\nimport xml.etree.ElementTree as ET\nimport h5py\nimport carla\nimport numpy as np\nimport hydra\n\n\ndef check_h5_maps(env_configs, obs_configs, carla_sh_path):\n pixels_per_meter = None\n for agent_id, obs_cfg in obs_configs.items():\n for k,v in obs_cfg.items():\n if 'birdview' in v['module']:\n pixels_per_meter = float(v['pixels_per_meter'])\n\n if pixels_per_meter is None:\n # agent does not require birdview map as observation\n return\n\n save_dir = Path(hydra.utils.get_original_cwd()) / 'carla_gym/core/obs_manager/birdview/maps'\n txt_command = f'Please run map generation script from project root directory. \\n' \\\n f'\\033[93m' \\\n f'python -m carla_gym.utils.birdview_map ' \\\n f'--save_dir {save_dir} --pixels_per_meter {pixels_per_meter:.2f} ' \\\n f'--carla_sh_path {carla_sh_path}' \\\n f'\\033[0m'\n \n # check if pixels_per_meter match\n for env_cfg in env_configs:\n carla_map = env_cfg['env_configs']['carla_map']\n hf_file_path = save_dir / (carla_map+'.h5')\n\n file_exists = hf_file_path.exists()\n if file_exists:\n map_hf = h5py.File(hf_file_path, 'r')\n hf_pixels_per_meter = float(map_hf.attrs['pixels_per_meter'])\n map_hf.close()\n pixels_per_meter_match = np.isclose(hf_pixels_per_meter, pixels_per_meter)\n txt_assert = f'pixel_per_meter mismatch between h5 file ({hf_pixels_per_meter}) '\\\n f'and obs_config ({pixels_per_meter}). '\n else:\n txt_assert = f'{hf_file_path} does not exists. '\n pixels_per_meter_match = False\n\n assert file_exists and pixels_per_meter_match, txt_assert + txt_command\n\n\ndef load_entry_point(name):\n mod_name, attr_name = name.split(\":\")\n mod = import_module(mod_name)\n fn = getattr(mod, attr_name)\n return fn\n\n\ndef load_obs_configs(agent_configs_dict):\n obs_configs = {}\n for actor_id, cfg in agent_configs_dict.items():\n obs_configs[actor_id] = json.load(open(cfg['path_to_conf_file'], 'r'))['obs_configs']\n return obs_configs\n\n\ndef init_agents(agent_configs_dict, **kwargs):\n agents_dict = {}\n for actor_id, cfg in agent_configs_dict.items():\n AgentClass = load_entry_point(cfg['entry_point'])\n agents_dict[actor_id] = AgentClass(cfg['path_to_conf_file'], **kwargs)\n return agents_dict\n\n\ndef parse_routes_file(routes_xml_filename):\n route_descriptions_dict = {}\n tree = ET.parse(routes_xml_filename)\n\n for route in tree.iter(\"route\"):\n\n route_id = int(route.attrib['id'])\n\n route_descriptions_dict[route_id] = {}\n\n for actor_type in ['ego_vehicle', 'scenario_actor']:\n route_descriptions_dict[route_id][actor_type+'s'] = {}\n for actor in route.iter(actor_type):\n actor_id = actor.attrib['id']\n\n waypoint_list = [] # the list of waypoints that can be found on this route for this actor\n for waypoint in actor.iter('waypoint'):\n location = carla.Location(\n x=float(waypoint.attrib['x']),\n y=float(waypoint.attrib['y']),\n z=float(waypoint.attrib['z']))\n rotation = carla.Rotation(\n roll=float(waypoint.attrib['roll']),\n pitch=float(waypoint.attrib['pitch']),\n yaw=float(waypoint.attrib['yaw']))\n waypoint_list.append(carla.Transform(location, rotation))\n\n route_descriptions_dict[route_id][actor_type+'s'][actor_id] = waypoint_list\n\n return route_descriptions_dict\n\n\ndef get_single_route(routes_xml_filename, route_id):\n tree = ET.parse(routes_xml_filename)\n route = tree.find(f'.//route[@id=\"{route_id}\"]')\n\n route_dict = {}\n for actor_type in ['ego_vehicle', 'scenario_actor']:\n route_dict[actor_type+'s'] = {}\n for actor in route.iter(actor_type):\n actor_id = actor.attrib['id']\n\n waypoint_list = [] # the list of waypoints that can be found on this route for this actor\n for waypoint in actor.iter('waypoint'):\n location = carla.Location(\n x=float(waypoint.attrib['x']),\n y=float(waypoint.attrib['y']),\n z=float(waypoint.attrib['z']))\n rotation = carla.Rotation(\n roll=float(waypoint.attrib['roll']),\n pitch=float(waypoint.attrib['pitch']),\n yaw=float(waypoint.attrib['yaw']))\n waypoint_list.append(carla.Transform(location, rotation))\n\n route_dict[actor_type+'s'][actor_id] = waypoint_list\n return route_dict\n\n\ndef to_camel_case(snake_str, init_capital=False):\n # agent_class_str = to_camel_case(agent_module_str.split('.')[-1], init_capital=True)\n components = snake_str.split('_')\n if init_capital:\n init_letter = components[0].title()\n else:\n init_letter = components[0]\n return init_letter + ''.join(x.title() for x in components[1:])\n\n\ndef get_free_tcp_port():\n s = socket.socket()\n s.bind((\"\", 0)) # Request the sys to provide a free port dynamically\n server_port = s.getsockname()[1]\n s.close()\n # 2000 works fine for now\n server_port = 2000\n return server_port\n" }, { "path": "roach/utils/expert_noiser.py", "content": "import time\nimport random\n\n\nclass ExpertNoiser(object):\n\n # define frequency into noise events per minute\n # define the amount_of_time of setting the noise\n\n def __init__(self, noise_type, frequency=15, intensity=10, min_noise_time_amount=2.0):\n\n # self.variable_type = variable\n self.noise_type = noise_type\n self.frequency = frequency\n self.noise_being_set = False\n self.noise_start_time = time.time()\n self.noise_end_time = time.time() + 1\n self.min_noise_time_amount = min_noise_time_amount\n self.noise_time_amount = min_noise_time_amount + float(random.randint(50, 200) / 100.0)\n self.second_counter = time.time()\n self.steer_noise_time = 0\n self.intensity = intensity + random.randint(-2, 2)\n self.remove_noise = False\n self.current_noise_mean = 0\n\n def set_noise(self):\n\n if self.noise_type == 'Spike' or self.noise_type == 'Throttle':\n\n # flip between positive and negative\n coin = random.randint(0, 1)\n if coin == 0: # negative\n self.current_noise_mean = 0.001 # -random.gauss(0.05,0.02)\n else: # positive\n self.current_noise_mean = -0.001 # random.gauss(0.05,0.02)\n\n def get_noise(self):\n\n if self.noise_type == 'Spike' or self.noise_type == 'Throttle':\n if self.current_noise_mean > 0:\n\n return min(0.55,\n self.current_noise_mean + (\n time.time() - self.noise_start_time) * 0.03 * self.intensity)\n else:\n\n return max(-0.55,\n self.current_noise_mean - (\n time.time() - self.noise_start_time) * 0.03 * self.intensity)\n\n def get_noise_removing(self):\n # print 'REMOVING'\n added_noise = (self.noise_end_time - self.noise_start_time) * 0.02 * self.intensity\n # print added_noise\n if self.noise_type == 'Spike' or self.noise_type == 'Throttle':\n if self.current_noise_mean > 0:\n added_noise = min(0.55, added_noise + self.current_noise_mean)\n return added_noise - (time.time() - self.noise_end_time) * 0.03 * self.intensity\n else:\n added_noise = max(-0.55, self.current_noise_mean - added_noise)\n return added_noise + (time.time() - self.noise_end_time) * 0.03 * self.intensity\n\n def is_time_for_noise(self, steer):\n\n # Count Seconds\n second_passed = False\n if time.time() - self.second_counter >= 1.0:\n second_passed = True\n self.second_counter = time.time()\n\n if time.time() - self.noise_start_time >= self.noise_time_amount and not self.remove_noise and self.noise_being_set:\n self.noise_being_set = False\n self.remove_noise = True\n self.noise_end_time = time.time()\n\n if self.noise_being_set:\n return True\n\n if self.remove_noise:\n # print \"TIME REMOVING \",(time.time()-self.noise_end_time)\n if (time.time() - self.noise_end_time) > (\n self.noise_time_amount): # if half the amount put passed\n self.remove_noise = False\n self.noise_time_amount = self.min_noise_time_amount + float(\n random.randint(50, 200) / 100.0)\n return False\n else:\n return True\n\n if second_passed and not self.noise_being_set:\n # Ok, if noise is not being set but a second passed... we may start puting more\n\n seed = random.randint(0, 60)\n if seed < self.frequency:\n if not self.noise_being_set:\n self.noise_being_set = True\n self.set_noise()\n self.steer_noise_time = steer\n self.noise_start_time = time.time()\n return True\n else:\n return False\n\n else:\n return False\n\n def set_noise_exist(self, noise_exist):\n self.noise_being_set = noise_exist\n\n def compute_noise(self, action, speed):\n\n # noisy_action = action\n if self.noise_type == 'None':\n return action, False, False\n\n if self.noise_type == 'Spike':\n\n if self.is_time_for_noise(action.steer):\n steer = action.steer\n\n if self.remove_noise:\n steer_noisy = max(\n min(steer + self.get_noise_removing() * (25 / (2.3 * speed + 5)), 1), -1)\n\n else:\n steer_noisy = max(min(steer + self.get_noise() * (25 / (2.3 * speed + 5)), 1),\n -1)\n\n noisy_action = action\n\n noisy_action.steer = steer_noisy\n\n return noisy_action, False, not self.remove_noise\n\n else:\n return action, False, False\n\n if self.noise_type == 'Throttle':\n if self.is_time_for_noise(action.throttle):\n throttle_noisy = action.throttle\n brake_noisy = action.brake\n\n if self.remove_noise:\n # print(\" Throttle noise removing\", self.get_noise_removing())\n noise = self.get_noise_removing()\n if noise > 0:\n throttle_noisy = max(min(throttle_noisy + noise, 1), 0)\n else:\n brake_noisy = max(min(brake_noisy + -noise, 1), 0)\n\n else:\n\n # print(\" Throttle noise \", self.get_noise())\n noise = self.get_noise()\n if noise > 0:\n throttle_noisy = max(min(throttle_noisy + noise, 1), 0)\n else:\n brake_noisy = max(min(brake_noisy + -noise, 1), 0)\n\n noisy_action = action\n noisy_action.throttle = throttle_noisy\n noisy_action.brake = brake_noisy\n\n # print 'timefornosie'\n return noisy_action, False, not self.remove_noise\n else:\n return action, False, False\n" }, { "path": "roach/utils/rl_birdview_wrapper.py", "content": "import gym\nimport numpy as np\nimport cv2\nimport carla\n\neval_num_zombie_vehicles = {\n 'Town01': 120,\n 'Town02': 70,\n 'Town03': 70,\n 'Town04': 150,\n 'Town05': 120,\n 'Town06': 120\n}\neval_num_zombie_walkers = {\n 'Town01': 120,\n 'Town02': 70,\n 'Town03': 70,\n 'Town04': 80,\n 'Town05': 120,\n 'Town06': 80\n}\n\nclass RlBirdviewWrapper(gym.Wrapper):\n def __init__(self, env, input_states=[], acc_as_action=False):\n assert len(env._obs_configs) == 1\n self._ev_id = list(env._obs_configs.keys())[0]\n self._input_states = input_states\n self._acc_as_action = acc_as_action\n self._render_dict = {}\n\n state_spaces = []\n if 'speed' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['speed']['speed_xy'])\n if 'speed_limit' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['control']['speed_limit'])\n if 'control' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['control']['throttle'])\n state_spaces.append(env.observation_space[self._ev_id]['control']['steer'])\n state_spaces.append(env.observation_space[self._ev_id]['control']['brake'])\n state_spaces.append(env.observation_space[self._ev_id]['control']['gear'])\n if 'acc_xy' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['velocity']['acc_xy'])\n if 'vel_xy' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['velocity']['vel_xy'])\n if 'vel_ang_z' in self._input_states:\n state_spaces.append(env.observation_space[self._ev_id]['velocity']['vel_ang_z'])\n\n state_low = np.concatenate([s.low for s in state_spaces])\n state_high = np.concatenate([s.high for s in state_spaces])\n\n env.observation_space = gym.spaces.Dict(\n {'state': gym.spaces.Box(low=state_low, high=state_high, dtype=np.float32),\n 'birdview': env.observation_space[self._ev_id]['birdview']['masks']})\n\n if self._acc_as_action:\n # act: acc(throttle/brake), steer\n env.action_space = gym.spaces.Box(low=np.array([-1, -1]), high=np.array([1, 1]), dtype=np.float32)\n else:\n # act: throttle, steer, brake\n env.action_space = gym.spaces.Box(low=np.array([0, -1, 0]), high=np.array([1, 1, 1]), dtype=np.float32)\n\n super(RlBirdviewWrapper, self).__init__(env)\n\n self.eval_mode = False\n\n def step(self, action):\n action_ma = {self._ev_id: self.process_act(action, self._acc_as_action)}\n\n obs_ma, reward_ma, done_ma, info_ma = self.env.step(action_ma)\n\n obs = self.process_obs(obs_ma[self._ev_id], self._input_states)\n reward = reward_ma[self._ev_id]\n done = done_ma[self._ev_id]\n info = info_ma[self._ev_id]\n\n self._render_dict = {\n 'timestamp': self.env.timestamp,\n 'obs': self._render_dict['prev_obs'],\n 'prev_obs': obs,\n 'im_render': self._render_dict['prev_im_render'],\n 'prev_im_render': obs_ma[self._ev_id]['birdview']['rendered'],\n 'action': action,\n 'reward_debug': info['reward_debug'],\n 'terminal_debug': info['terminal_debug']\n }\n return obs, reward, done, info\n\n\n @staticmethod\n def process_obs(obs, input_states, train=True):\n\n state_list = []\n if 'speed' in input_states:\n state_list.append(obs['speed']['speed_xy'])\n if 'speed_limit' in input_states:\n state_list.append(obs['control']['speed_limit'])\n if 'control' in input_states:\n state_list.append(obs['control']['throttle'])\n state_list.append(obs['control']['steer'])\n state_list.append(obs['control']['brake'])\n state_list.append(obs['control']['gear']/5.0)\n if 'acc_xy' in input_states:\n state_list.append(obs['velocity']['acc_xy'])\n if 'vel_xy' in input_states:\n state_list.append(obs['velocity']['vel_xy'])\n if 'vel_ang_z' in input_states:\n state_list.append(obs['velocity']['vel_ang_z'])\n\n state = np.concatenate(state_list)\n\n birdview = obs['birdview']['masks']\n\n if not train:\n birdview = np.expand_dims(birdview, 0)\n state = np.expand_dims(state, 0)\n\n obs_dict = {\n 'state': state.astype(np.float32),\n 'birdview': birdview\n }\n return obs_dict\n\n @staticmethod\n def process_act(action, acc_as_action, train=True):\n if not train:\n action = action[0]\n if acc_as_action:\n acc, steer = action.astype(np.float64)\n if acc >= 0.0:\n throttle = acc\n brake = 0.0\n else:\n throttle = 0.0\n brake = np.abs(acc)\n else:\n throttle, steer, brake = action.astype(np.float64)\n\n throttle = np.clip(throttle, 0, 1)\n steer = np.clip(steer, -1, 1)\n brake = np.clip(brake, 0, 1)\n control = carla.VehicleControl(throttle=throttle, steer=steer, brake=brake)\n return control\n" }, { "path": "roach/utils/traffic_light.py", "content": "from collections import deque\nimport carla\nimport numpy as np\nimport roach.utils.transforms as trans_utils\n\n\ndef _get_traffic_light_waypoints(traffic_light, carla_map):\n \"\"\"\n get area of a given traffic light\n adapted from \"carla-simulator/scenario_runner/srunner/scenariomanager/scenarioatomics/atomic_criteria.py\"\n \"\"\"\n base_transform = traffic_light.get_transform()\n tv_loc = traffic_light.trigger_volume.location\n tv_ext = traffic_light.trigger_volume.extent\n\n # Discretize the trigger box into points\n x_values = np.arange(-0.9 * tv_ext.x, 0.9 * tv_ext.x, 1.0) # 0.9 to avoid crossing to adjacent lanes\n area = []\n for x in x_values:\n point_location = base_transform.transform(tv_loc + carla.Location(x=x))\n area.append(point_location)\n\n # Get the waypoints of these points, removing duplicates\n ini_wps = []\n for pt in area:\n wpx = carla_map.get_waypoint(pt)\n # As x_values are arranged in order, only the last one has to be checked\n if not ini_wps or ini_wps[-1].road_id != wpx.road_id or ini_wps[-1].lane_id != wpx.lane_id:\n ini_wps.append(wpx)\n\n # Leaderboard: Advance them until the intersection\n stopline_wps = []\n stopline_vertices = []\n junction_wps = []\n for wpx in ini_wps:\n # Below: just use trigger volume, otherwise it's on the zebra lines.\n # stopline_wps.append(wpx)\n # vec_forward = wpx.transform.get_forward_vector()\n # vec_right = carla.Vector3D(x=-vec_forward.y, y=vec_forward.x, z=0)\n\n # loc_left = wpx.transform.location - 0.4 * wpx.lane_width * vec_right\n # loc_right = wpx.transform.location + 0.4 * wpx.lane_width * vec_right\n # stopline_vertices.append([loc_left, loc_right])\n\n while not wpx.is_intersection:\n next_wp = wpx.next(0.5)[0]\n if next_wp and not next_wp.is_intersection:\n wpx = next_wp\n else:\n break\n junction_wps.append(wpx)\n\n stopline_wps.append(wpx)\n vec_forward = wpx.transform.get_forward_vector()\n vec_right = carla.Vector3D(x=-vec_forward.y, y=vec_forward.x, z=0)\n\n loc_left = wpx.transform.location - 0.4 * wpx.lane_width * vec_right\n loc_right = wpx.transform.location + 0.4 * wpx.lane_width * vec_right\n stopline_vertices.append([loc_left, loc_right])\n\n # all paths at junction for this traffic light\n junction_paths = []\n path_wps = []\n wp_queue = deque(junction_wps)\n while len(wp_queue) > 0:\n current_wp = wp_queue.pop()\n path_wps.append(current_wp)\n next_wps = current_wp.next(1.0)\n for next_wp in next_wps:\n if next_wp.is_junction:\n wp_queue.append(next_wp)\n else:\n junction_paths.append(path_wps)\n path_wps = []\n\n return carla.Location(base_transform.transform(tv_loc)), stopline_wps, stopline_vertices, junction_paths\n\n\nclass TrafficLightHandler:\n num_tl = 0\n list_tl_actor = []\n list_tv_loc = []\n list_stopline_wps = []\n list_stopline_vtx = []\n list_junction_paths = []\n carla_map = None\n\n @staticmethod\n def reset(world):\n TrafficLightHandler.carla_map = world.get_map()\n\n TrafficLightHandler.num_tl = 0\n TrafficLightHandler.list_tl_actor = []\n TrafficLightHandler.list_tv_loc = []\n TrafficLightHandler.list_stopline_wps = []\n TrafficLightHandler.list_stopline_vtx = []\n TrafficLightHandler.list_junction_paths = []\n\n all_actors = world.get_actors()\n for _actor in all_actors:\n if 'traffic_light' in _actor.type_id:\n tv_loc, stopline_wps, stopline_vtx, junction_paths = _get_traffic_light_waypoints(\n _actor, TrafficLightHandler.carla_map)\n\n TrafficLightHandler.list_tl_actor.append(_actor)\n TrafficLightHandler.list_tv_loc.append(tv_loc)\n TrafficLightHandler.list_stopline_wps.append(stopline_wps)\n TrafficLightHandler.list_stopline_vtx.append(stopline_vtx)\n TrafficLightHandler.list_junction_paths.append(junction_paths)\n\n TrafficLightHandler.num_tl += 1\n\n @staticmethod\n def get_light_state(vehicle, offset=0.0, dist_threshold=15.0):\n '''\n vehicle: carla.Vehicle\n '''\n vec_tra = vehicle.get_transform()\n veh_dir = vec_tra.get_forward_vector()\n\n hit_loc = vec_tra.transform(carla.Location(x=offset))\n hit_wp = TrafficLightHandler.carla_map.get_waypoint(hit_loc)\n\n light_loc = None\n light_state = None\n light_id = None\n for i in range(TrafficLightHandler.num_tl):\n traffic_light = TrafficLightHandler.list_tl_actor[i]\n tv_loc = 0.5*TrafficLightHandler.list_stopline_wps[i][0].transform.location \\\n + 0.5*TrafficLightHandler.list_stopline_wps[i][-1].transform.location\n\n distance = np.sqrt((tv_loc.x-hit_loc.x)**2 + (tv_loc.y-hit_loc.y)**2)\n if distance > dist_threshold:\n continue\n\n for wp in TrafficLightHandler.list_stopline_wps[i]:\n\n wp_dir = wp.transform.get_forward_vector()\n dot_ve_wp = veh_dir.x * wp_dir.x + veh_dir.y * wp_dir.y + veh_dir.z * wp_dir.z\n\n wp_1 = wp.previous(4.0)[0]\n same_road = (hit_wp.road_id == wp.road_id) and (hit_wp.lane_id == wp.lane_id)\n same_road_1 = (hit_wp.road_id == wp_1.road_id) and (hit_wp.lane_id == wp_1.lane_id)\n\n # if (wp.road_id != wp_1.road_id) or (wp.lane_id != wp_1.lane_id):\n # print(f'Traffic Light Problem: {wp.road_id}={wp_1.road_id}, {wp.lane_id}={wp_1.lane_id}')\n\n if (same_road or same_road_1) and dot_ve_wp > 0:\n # This light is red and is affecting our lane\n loc_in_ev = trans_utils.loc_global_to_ref(wp.transform.location, vec_tra)\n light_loc = np.array([loc_in_ev.x, loc_in_ev.y, loc_in_ev.z], dtype=np.float32)\n light_state = traffic_light.state\n light_id = traffic_light.id\n break\n\n return light_state, light_loc, light_id\n\n @staticmethod\n def get_junctoin_paths(veh_loc, color=0, dist_threshold=50.0):\n if color == 0:\n tl_state = carla.TrafficLightState.Green\n elif color == 1:\n tl_state = carla.TrafficLightState.Yellow\n elif color == 2:\n tl_state = carla.TrafficLightState.Red\n\n junctoin_paths = []\n for i in range(TrafficLightHandler.num_tl):\n traffic_light = TrafficLightHandler.list_tl_actor[i]\n tv_loc = TrafficLightHandler.list_tv_loc[i]\n if tv_loc.distance(veh_loc) > dist_threshold:\n continue\n if traffic_light.state != tl_state:\n continue\n\n junctoin_paths += TrafficLightHandler.list_junction_paths[i]\n\n return junctoin_paths\n\n @staticmethod\n def get_stopline_vtx(veh_loc, color, dist_threshold=50.0):\n if color == 0:\n tl_state = carla.TrafficLightState.Green\n elif color == 1:\n tl_state = carla.TrafficLightState.Yellow\n elif color == 2:\n tl_state = carla.TrafficLightState.Red\n\n stopline_vtx = []\n for i in range(TrafficLightHandler.num_tl):\n traffic_light = TrafficLightHandler.list_tl_actor[i]\n tv_loc = TrafficLightHandler.list_tv_loc[i]\n if tv_loc.distance(veh_loc) > dist_threshold:\n continue\n if traffic_light.state != tl_state:\n continue\n stopline_vtx += TrafficLightHandler.list_stopline_vtx[i]\n\n return stopline_vtx\n" }, { "path": "roach/utils/transforms.py", "content": "import numpy as np\nimport carla\n\n\ndef loc_global_to_ref(target_loc_in_global, ref_trans_in_global):\n \"\"\"\n :param target_loc_in_global: carla.Location in global coordinate (world, actor)\n :param ref_trans_in_global: carla.Transform in global coordinate (world, actor)\n :return: carla.Location in ref coordinate\n \"\"\"\n x = target_loc_in_global.x - ref_trans_in_global.location.x\n y = target_loc_in_global.y - ref_trans_in_global.location.y\n z = target_loc_in_global.z - ref_trans_in_global.location.z\n vec_in_global = carla.Vector3D(x=x, y=y, z=z)\n vec_in_ref = vec_global_to_ref(vec_in_global, ref_trans_in_global.rotation)\n\n target_loc_in_ref = carla.Location(x=vec_in_ref.x, y=vec_in_ref.y, z=vec_in_ref.z)\n return target_loc_in_ref\n\n\ndef vec_global_to_ref(target_vec_in_global, ref_rot_in_global):\n \"\"\"\n :param target_vec_in_global: carla.Vector3D in global coordinate (world, actor)\n :param ref_rot_in_global: carla.Rotation in global coordinate (world, actor)\n :return: carla.Vector3D in ref coordinate\n \"\"\"\n R = carla_rot_to_mat(ref_rot_in_global)\n np_vec_in_global = np.array([[target_vec_in_global.x],\n [target_vec_in_global.y],\n [target_vec_in_global.z]])\n np_vec_in_ref = R.T.dot(np_vec_in_global)\n target_vec_in_ref = carla.Vector3D(x=np_vec_in_ref[0, 0], y=np_vec_in_ref[1, 0], z=np_vec_in_ref[2, 0])\n return target_vec_in_ref\n\n\ndef rot_global_to_ref(target_rot_in_global, ref_rot_in_global):\n target_roll_in_ref = cast_angle(target_rot_in_global.roll - ref_rot_in_global.roll)\n target_pitch_in_ref = cast_angle(target_rot_in_global.pitch - ref_rot_in_global.pitch)\n target_yaw_in_ref = cast_angle(target_rot_in_global.yaw - ref_rot_in_global.yaw)\n\n target_rot_in_ref = carla.Rotation(roll=target_roll_in_ref, pitch=target_pitch_in_ref, yaw=target_yaw_in_ref)\n return target_rot_in_ref\n\ndef rot_ref_to_global(target_rot_in_ref, ref_rot_in_global):\n target_roll_in_global = cast_angle(target_rot_in_ref.roll + ref_rot_in_global.roll)\n target_pitch_in_global = cast_angle(target_rot_in_ref.pitch + ref_rot_in_global.pitch)\n target_yaw_in_global = cast_angle(target_rot_in_ref.yaw + ref_rot_in_global.yaw)\n\n target_rot_in_global = carla.Rotation(roll=target_roll_in_global, pitch=target_pitch_in_global, yaw=target_yaw_in_global)\n return target_rot_in_global\n\n\ndef carla_rot_to_mat(carla_rotation):\n \"\"\"\n Transform rpy in carla.Rotation to rotation matrix in np.array\n\n :param carla_rotation: carla.Rotation \n :return: np.array rotation matrix\n \"\"\"\n roll = np.deg2rad(carla_rotation.roll)\n pitch = np.deg2rad(carla_rotation.pitch)\n yaw = np.deg2rad(carla_rotation.yaw)\n\n yaw_matrix = np.array([\n [np.cos(yaw), -np.sin(yaw), 0],\n [np.sin(yaw), np.cos(yaw), 0],\n [0, 0, 1]\n ])\n pitch_matrix = np.array([\n [np.cos(pitch), 0, -np.sin(pitch)],\n [0, 1, 0],\n [np.sin(pitch), 0, np.cos(pitch)]\n ])\n roll_matrix = np.array([\n [1, 0, 0],\n [0, np.cos(roll), np.sin(roll)],\n [0, -np.sin(roll), np.cos(roll)]\n ])\n\n rotation_matrix = yaw_matrix.dot(pitch_matrix).dot(roll_matrix)\n return rotation_matrix\n\ndef get_loc_rot_vel_in_ev(actor_list, ev_transform, get_acceleration = False, origin = False):\n location, rotation, absolute_velocity = [], [], []\n if get_acceleration:\n absolute_acceleration = []\n if origin:\n origin_velocity = []\n origin_acceleration = []\n for actor in actor_list:\n # location\n location_in_world = actor.get_transform().location\n location_in_ev = loc_global_to_ref(location_in_world, ev_transform)\n location.append([location_in_ev.x, location_in_ev.y, location_in_ev.z])\n # rotation\n rotation_in_world = actor.get_transform().rotation\n rotation_in_ev = rot_global_to_ref(rotation_in_world, ev_transform.rotation)\n rotation.append([rotation_in_ev.roll, rotation_in_ev.pitch, rotation_in_ev.yaw])\n # velocity\n vel_in_world = actor.get_velocity()\n vel_in_ev = vec_global_to_ref(vel_in_world, ev_transform.rotation)\n absolute_velocity.append([vel_in_ev.x, vel_in_ev.y, vel_in_ev.z])\n if get_acceleration:\n # acceleration\n acc_in_world = actor.get_acceleration()\n acc_in_ev = vec_global_to_ref(acc_in_world, ev_transform.rotation)\n absolute_acceleration.append([acc_in_ev.x, acc_in_ev.y, acc_in_ev.z])\n if origin:\n origin_velocity.append([vel_in_world.x, vel_in_world.y, vel_in_world.z])\n origin_acceleration.append([acc_in_world.x, acc_in_world.y, acc_in_world.z])\n if get_acceleration:\n if origin:\n return location, rotation, absolute_velocity, absolute_acceleration, origin_velocity, origin_acceleration\n return location, rotation, absolute_velocity, absolute_acceleration\n return np.array(location), np.array(rotation), np.array(absolute_velocity)\n\ndef get_loc_rot_in_global(actor_list):\n location, rotation = [], []\n for actor in actor_list:\n location_in_world = actor.get_transform().location\n location.append([location_in_world.x, location_in_world.y, location_in_world.z])\n rotation_in_world = actor.get_transform().rotation\n rotation.append([rotation_in_world.roll, rotation_in_world.pitch, rotation_in_world.yaw])\n return np.array(location), np.array(rotation)\n\ndef cast_angle(x):\n # cast angle to [-180, +180)\n return (x+180.0)%360.0-180.0" }, { "path": "roach/utils/wandb_callback.py", "content": "import numpy as np\nimport time\nfrom pathlib import Path\nimport wandb\nfrom stable_baselines3.common.callbacks import BaseCallback\nfrom gym.wrappers.monitoring.video_recorder import ImageEncoder\nfrom omegaconf import OmegaConf\n\n\nclass WandbCallback(BaseCallback):\n def __init__(self, cfg, vec_env):\n super(WandbCallback, self).__init__(verbose=1)\n\n # save_dir = Path.cwd()\n # self._save_dir = save_dir\n self._video_path = Path('video')\n self._video_path.mkdir(parents=True, exist_ok=True)\n self._ckpt_dir = Path('ckpt')\n self._ckpt_dir.mkdir(parents=True, exist_ok=True)\n\n # wandb.init(project=cfg.wb_project, dir=save_dir, name=cfg.wb_runname)\n wandb.init(project=cfg.wb_project, name=cfg.wb_name, notes=cfg.wb_notes, tags=cfg.wb_tags)\n wandb.config.update(OmegaConf.to_container(cfg))\n\n wandb.save('./config_agent.yaml')\n wandb.save('.hydra/*')\n\n self.vec_env = vec_env\n\n self._eval_step = int(1e5)\n self._buffer_step = int(1e5)\n\n def _init_callback(self):\n self.n_epoch = 0\n self._last_time_buffer = self.model.num_timesteps\n self._last_time_eval = self.model.num_timesteps\n\n def _on_step(self) -> bool:\n return True\n\n def _on_training_start(self) -> None:\n pass\n\n def _on_rollout_start(self):\n # self.model._last_obs = self.model.env.reset()\n pass\n\n def _on_training_end(self) -> None:\n print(f'n_epoch: {self.n_epoch}, num_timesteps: {self.model.num_timesteps}')\n # save time\n time_elapsed = time.time() - self.model.start_time\n wandb.log({\n 'time/n_epoch': self.n_epoch,\n 'time/sec_per_epoch': time_elapsed / (self.n_epoch+1),\n 'time/fps': (self.model.num_timesteps-self.model.start_num_timesteps) / time_elapsed,\n 'time/train': self.model.t_train,\n 'time/train_values': self.model.t_train_values,\n 'time/rollout': self.model.t_rollout\n }, step=self.model.num_timesteps)\n wandb.log(self.model.train_debug, step=self.model.num_timesteps)\n\n # evaluate and save checkpoint\n if (self.model.num_timesteps - self._last_time_eval) >= self._eval_step:\n self._last_time_eval = self.model.num_timesteps\n # evaluate\n eval_video_path = (self._video_path / f'eval_{self.model.num_timesteps}.mp4').as_posix()\n avg_ep_stat, ep_events = self.evaluate_policy(self.vec_env, self.model.policy, eval_video_path)\n # log to wandb\n wandb.log({f'video/{self.model.num_timesteps}': wandb.Video(eval_video_path)},\n step=self.model.num_timesteps)\n wandb.log(avg_ep_stat, step=self.model.num_timesteps)\n # save events\n # eval_json_path = (video_path / f'event_{self.model.num_timesteps}.json').as_posix()\n # with open(eval_json_path, 'w') as fd:\n # json.dump(ep_events, fd, indent=4, sort_keys=False)\n # wandb.save(eval_json_path)\n\n ckpt_path = (self._ckpt_dir / f'ckpt_{self.model.num_timesteps}.pth').as_posix()\n self.model.save(ckpt_path)\n wandb.save(f'./{ckpt_path}')\n self.n_epoch += 1\n\n # CONFIGHACK: curriculum\n # num_zombies = {}\n # for i in range(self.vec_env.num_envs):\n # env_all_tasks = self.vec_env.get_attr('all_tasks',indices=i)[0]\n # num_zombies[f'train/n_veh/{i}'] = env_all_tasks[0]['num_zombie_vehicles']\n # num_zombies[f'train/n_ped/{i}'] = env_all_tasks[0]['num_zombie_walkers']\n # if wandb.config['curriculum']:\n # if avg_ep_stat['eval/route_completed_in_km'] > 1.0:\n # # and avg_ep_stat['eval/red_light']>0:\n # for env_task in env_all_tasks:\n # env_task['num_zombie_vehicles'] += 10\n # env_task['num_zombie_walkers'] += 10\n # self.vec_env.set_attr('all_tasks', env_all_tasks, indices=i)\n\n # wandb.log(num_zombies, step=self.model.num_timesteps)\n\n def _on_rollout_end(self):\n wandb.log({'time/rollout': self.model.t_rollout}, step=self.model.num_timesteps)\n\n # save rollout statistics\n avg_ep_stat = self.get_avg_ep_stat(self.model.ep_stat_buffer, prefix='rollout/')\n wandb.log(avg_ep_stat, step=self.model.num_timesteps)\n\n # action, mu, sigma histogram\n action_statistics = np.array(self.model.action_statistics)\n mu_statistics = np.array(self.model.mu_statistics)\n sigma_statistics = np.array(self.model.sigma_statistics)\n n_action = action_statistics.shape[-1]\n action_statistics = action_statistics.reshape(-1, n_action)\n mu_statistics = mu_statistics.reshape(-1, n_action)\n sigma_statistics = sigma_statistics.reshape(-1, n_action)\n\n for i in range(n_action):\n # path_str = (self._save_dir/f'action{i}.csv').as_posix()\n # np.savetxt(path_str, action_statistics[:, i], delimiter=',')\n # wandb.save(path_str)\n wandb.log({f'action[{i}]': wandb.Histogram(action_statistics[:, i])}, step=self.model.num_timesteps)\n wandb.log({f'alpha[{i}]': wandb.Histogram(mu_statistics[:, i])}, step=self.model.num_timesteps)\n wandb.log({f'beta[{i}]': wandb.Histogram(sigma_statistics[:, i])}, step=self.model.num_timesteps)\n\n # render buffer\n if (self.model.num_timesteps - self._last_time_buffer) >= self._buffer_step:\n self._last_time_buffer = self.model.num_timesteps\n buffer_video_path = (self._video_path / f'buffer_{self.model.num_timesteps}.mp4').as_posix()\n\n list_buffer_im = self.model.buffer.render()\n encoder = ImageEncoder(buffer_video_path, list_buffer_im[0].shape, 30, 30)\n for im in list_buffer_im:\n encoder.capture_frame(im)\n encoder.close()\n encoder = None\n\n wandb.log({f'buffer/{self.model.num_timesteps}': wandb.Video(buffer_video_path)},\n step=self.model.num_timesteps)\n\n @staticmethod\n def evaluate_policy(env, policy, video_path, min_eval_steps=3000):\n policy = policy.eval()\n t0 = time.time()\n for i in range(env.num_envs):\n env.set_attr('eval_mode', True, indices=i)\n obs = env.reset()\n\n list_render = []\n ep_stat_buffer = []\n ep_events = {}\n for i in range(env.num_envs):\n ep_events[f'venv_{i}'] = []\n\n n_step = 0\n n_timeout = 0\n env_done = np.array([False]*env.num_envs)\n # while n_step < min_eval_steps:\n while n_step < min_eval_steps or not np.all(env_done):\n actions, values, log_probs, mu, sigma, _ = policy.forward(obs, deterministic=True, clip_action=True)\n obs, reward, done, info = env.step(actions)\n\n for i in range(env.num_envs):\n env.set_attr('action_value', values[i], indices=i)\n env.set_attr('action_log_probs', log_probs[i], indices=i)\n env.set_attr('action_mu', mu[i], indices=i)\n env.set_attr('action_sigma', sigma[i], indices=i)\n\n list_render.append(env.render(mode='rgb_array'))\n\n n_step += 1\n env_done |= done\n\n for i in np.where(done)[0]:\n ep_stat_buffer.append(info[i]['episode_stat'])\n ep_events[f'venv_{i}'].append(info[i]['episode_event'])\n n_timeout += int(info[i]['timeout'])\n\n # conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge\n encoder = ImageEncoder(video_path, list_render[0].shape, 30, 30)\n for im in list_render:\n encoder.capture_frame(im)\n encoder.close()\n\n avg_ep_stat = WandbCallback.get_avg_ep_stat(ep_stat_buffer, prefix='eval/')\n avg_ep_stat['eval/eval_timeout'] = n_timeout\n\n duration = time.time() - t0\n avg_ep_stat['time/t_eval'] = duration\n avg_ep_stat['time/fps_eval'] = n_step * env.num_envs / duration\n\n for i in range(env.num_envs):\n env.set_attr('eval_mode', False, indices=i)\n obs = env.reset()\n return avg_ep_stat, ep_events\n\n @staticmethod\n def get_avg_ep_stat(ep_stat_buffer, prefix=''):\n avg_ep_stat = {}\n if len(ep_stat_buffer) > 0:\n for ep_info in ep_stat_buffer:\n for k, v in ep_info.items():\n k_avg = f'{prefix}{k}'\n if k_avg in avg_ep_stat:\n avg_ep_stat[k_avg] += v\n else:\n avg_ep_stat[k_avg] = v\n\n n_episodes = float(len(ep_stat_buffer))\n for k in avg_ep_stat.keys():\n avg_ep_stat[k] /= n_episodes\n avg_ep_stat[f'{prefix}n_episodes'] = n_episodes\n\n return avg_ep_stat\n" }, { "path": "scenario_runner/CARLA_VER", "content": "HOST = https://carla-releases.s3.eu-west-3.amazonaws.com/Linux\nRELEASE=CARLA_0.9.9\n" }, { "path": "scenario_runner/Dockerfile", "content": "from ubuntu:18.04\n\n# Install base libs\nrun apt-get update && apt-get install --no-install-recommends -y libpng16-16=1.6.34-1ubuntu0.18.04.2 \\\nlibtiff5=4.0.9-5ubuntu0.3 libjpeg8=8c-2ubuntu8 build-essential=12.4ubuntu1 wget=1.19.4-1ubuntu2.2 git=1:2.17.1-1ubuntu0.7 \\\n python3.6=3.6.9-1~18.04ubuntu1 python3.6-dev=3.6.9-1~18.04ubuntu1 python3-pip=9.0.1-2.3~ubuntu1.18.04.1 \\\n && rm -rf /var/lib/apt/lists/*\n\n# Install python requirements\nrun pip3 install --user setuptools==46.3.0 wheel==0.34.2 && pip3 install py_trees==0.8.3 networkx==2.2 pygame==1.9.6 \\\n six==1.14.0 numpy==1.18.4 psutil==5.7.0 shapely==1.7.0 xmlschema==1.1.3 ephem==3.7.6.0 tabulate==0.8.7\\\n&& mkdir -p /app/scenario_runner\n\n# Install scenario_runner \ncopy . /app/scenario_runner\n\n# setup environment :\n# \n# CARLA_HOST : uri for carla package without trailing slash. \n# For example, \"https://carla-releases.s3.eu-west-3.amazonaws.com/Linux\".\n# If this environment is not passed to docker build, the value\n#\t\t is taken from CARLA_VER file inside the repository.\n#\n# CARLA_RELEASE : Name of the package to be used. For example, \"CARLA_0.9.9\".\n# If this environment is not passed to docker build, the value\n# is taken from CARLA_VER file inside the repository.\n# \n#\n# It's expected that $(CARLA_HOST)/$(CARLA_RELEASE).tar.gz is a downloadable resource.\n#\n\nenv CARLA_HOST \"\"\nenv CARLA_RELEASE \"\"\n\n# Extract and install python API and resources from CARLA\nrun export DEFAULT_CARLA_HOST=\"$(sed -e 's/^\\s*HOST\\s*=\\s*//;t;d' /app/scenario_runner/CARLA_VER)\" && \\\n echo \"$DEFAULT_CARLA_HOST\" && \\\n export CARLA_HOST=\"${CARLA_HOST:-$DEFAULT_CARLA_HOST}\" && \\\n export DEFAULT_CARLA_RELEASE=\"$(sed -e 's/^\\s*RELEASE\\s*=\\s*//;t;d' /app/scenario_runner/CARLA_VER)\" && \\\n export CARLA_RELEASE=\"${CARLA_RELEASE:-$DEFAULT_CARLA_RELEASE}\" && \\\n echo \"$CARLA_HOST/$CARLA_RELEASE.tar.gz\" && \\\n wget -qO- \"$CARLA_HOST/$CARLA_RELEASE.tar.gz\" | tar -xzv PythonAPI/carla -C / && \\\n mv /PythonAPI/carla /app/ && \\\n python3 -m easy_install --no-find-links --no-deps \"$(find /app/carla/ -iname '*py3.*.egg' )\"\n\n\n# Setup working environment\nworkdir /app/scenario_runner\nenv PYTHONPATH \"${PYTHONPATH}:/app/carla/agents:/app/carla\"\nentrypoint [\"/bin/sh\" ]\n\n" }, { "path": "scenario_runner/Docs/CHANGELOG.md", "content": "## Table of Contents\n* [Latest Changes](#latest-changes)\n* [CARLA ScenarioRunner 0.9.10](#carla-scenariorunner-0910)\n* [CARLA ScenarioRunner 0.9.9](#carla-scenariorunner-099)\n* [CARLA ScenarioRunner 0.9.8](#carla-scenariorunner-098)\n* [CARLA ScenarioRunner 0.9.7](#carla-scenariorunner-097)\n* [CARLA ScenarioRunner 0.9.6](#carla-scenariorunner-096)\n* [CARLA ScenarioRunner 0.9.5.1](#carla-scenariorunner-0951)\n* [CARLA ScenarioRunner 0.9.5](#carla-scenariorunner-095)\n* [CARLA ScenarioRunner 0.9.2](#carla-scenariorunner-092)\n\n## Latest Changes\n\n## CARLA ScenarioRunner 0.9.10\n### :rocket: New Features\n* Renamed some agent labels inside Jenkins CI pipelines for new standard proposals.\n* Added support for Jenkins CI pipelines doing automated testing and docker images creation.\n* **Very important:** CarlaActorPool has been removed and all its functions moved to the CarlaDataProvider:\n - The spawning functions have been refactored. All the *setup* functions have been removed, and its functionalities moved to their *request* counterparts. For example, previously *request_new_actor* just called *setup_actor*, but now *setup_actor* no longer exists, and the spawning is done via *request_new_actor*. They have also been unified and are now more consistent.\n - Changed *ActorConfiguration* to *ActorConfigurationData.parse_from_node*\n - Renamed the _map_ element at routes to _town_, matching the scenario configuration files\n\n* Added new environment variables needed. They can be seen at (Docs/getting_scenariorunner.md).\n* Improved the visual display of the information from the *output* and *file* arguments.\n* Routes are now deterministic in regards to the spawning scenarios when more than one are at the same location\n* The BackgroundActivity functionality has been unchanged but some tweaks have been made, fixing a previous patch. As a result, the *amount* parameter at *ActorConfigurationData* has been removed.\n* Remade how ScenarioRunner reads the scenarios files. It now reads all scenarios inside the *srunner/scenarios* folder without needing to import them. Scenarios outside that folder will still need the *--additionalScenario* argument.\n* The new weather parameters (related to fog) are now correctly read when running scenarios outside routes.\n* Enable weather animation during scenario execution (requires ephem pip package)\n* Changed manual control to be in par with the CARLA version. Among others, added vehicle lights, recording and some new sensors\n* Removed unsupported scenarios (ChallengeBasic and BackgroundActivity, VehicleTurnLeftAtJunction)\n* Added a new metrics module, which gives access to all the information about a scenario in order to allow the user to extract any desired information about the simulation. More information [here](metrics_module.md)\n* Removed the default randomness at the ControlLoss scenario\n* OpenSCENARIO support:\n - Added support for controllers and provided default implementations for vehicles and pedestrians. This required changing the handling of actors, which results in that now all actors are controlled by an OSC controller. Supported controllers:\n - Pedestrian controller\n - NPC vehicle controller (based on CARLA LocalPlanner)\n - Simple vehicle controller to set velocities not brake/throttle, and consider obstacles in the forward-facing region.\n - External controller (to forward control to external entities)\n - Added initial speed support for pedestrians for OpenSCENARIO\n - Support for EnvironmentActions within Story (before only within Init). This allows changing weather conditions during scenario execution\n - Added support for RelativeSpeedCondition\n - Added support for AccelerationCondition\n - Added support for TimeOfDayCondition\n - Added support for OffroadCondition\n - Added support for CollisionCondition\n - Added support for EndOfRoadCondition\n - Added support for TimeHeadwayCondition\n - Added support for TrafficSignalCondition\n - Added support for AcquirePositionAction\n - Extended FollowLeadingVehicle example to illustrate weather changes\n - Created example scenarios to illustrate usage of controllers and weather changes\n - Reworked the handling of Catalogs to make it compliant to the 1.0 version (relative paths have to be relative to the scenario file)\n - The RoadNetwork can be defined as global Parameter\n - Fixed handling of relative positions with negative offset\n - Added support for local ParamaterDeclarations\n - Added support for Parameters within Catalogs\n - Added support for ParameterAssignments for CatalogReferences\n - Fixed name handling of Parameters: Parameter declerations must not start with a leading '$', but when the parameter is used a leading '$' is required.\n - Fixed use of Parameters for multiple instances of the same Catalog element\n - Fixed use of relative initial positions for any actor\n - Added possibility to use synchronous execution mode with OpenSCENARIO\n - Fixed use of relative paths in CustomCommandAction\n - Fixed use of ControllerCatalogs\n* Atomics:\n - Several new atomics to enable usage of OSC controllers\n - WeatherBehavior to simulate weather over time\n - UpdateWeather to update weather to a new setting, e.g. sun to rain\n - UpdateRoadFriction to update the road friction while running\n - new RelativeVelocityToOtherActor trigger condition, used to compare velocities of two actors\n - new TriggerAcceleration trigger condition which compares a reference acceleration with the actor's one.\n - new TimeOfDayComparison trigger condition, comparing the simulation time (set up by the new weather system) with a given *datetime*.\n - Added new *OffRoadTest* criteria.\n - Added new *EndofRoadTest* criteria, to detect when a vehicle changes between OpenDRIVE roads.\n - CollisionTest criterion can now filter the collisions for a specific actor, or actor type_id.\n - Added a *duration* argument to *OnSidewalkTest* criteria, which makes the criteria fail after a certain time has passed, instead of doing so immediately. The default behavior has been unchanged.\n - InTimeToArrivalToVehicle has had its two actor arguments swapped, to match all the other behaviors.\n - Added *along_route* flag to InTimeToArrivalToVehicle, to take into account the topology of the road\n - Changed the inputs to TrafficLightStateSetter to match the other atomics, but the functionality remains unchanged\n\n### :bug: Bug Fixes\n* Fixed bug causing parsing RelativeTargetSpeed tag to fail. \n* Fixed missing 'six' in requirements.txt\n* Support OpenSCENARIO parameters also if they're only part of a string value\n* Support Routes in Catalogs\n* Fix parsing of properties within ControllerCatalogs\n* Add cleanup of instantiated OpenSCENARIO controllers\n* Do not register SIGHUP signal in windows\n* Fixed initial speed of vehicles using OpenSCENARIO\n* Fixed bug causing an exception when calling BasicScenario's *_initialize_actors* with no other_actors.\n* Fixed bug causing the route to be downsampled (introduced by mistake at 0.9.9)\n* Fixed bug causing _output_ argument to not display the correct number with _InRouteTest_ and _RouteCompletionTest_ criterias (the succces and failure was correctly displayed)\n* Fixed bug causing OpenSCENARIO's SpeedCondition to not work as intended\n* Fixed bug causing CollisionConditions not to work properly in OpenSCENARIO\n* Fixed bug causing the *group:* functionality to behave incorrectly when moving scenarios around.\n* Fixed bug causing FollowLeadingVehicle and FollowLeadingVehicleWithObstacle scenarios to not properly end\n* Fixed bug causing CollisionTest to ignore multiple collisions with scene objects\n* Fixed bug causing NoSignalJunctionCrossing to not output the results of the scenario\n* Fixed bug causing SyncArrival to fail when the actor was destroyed after the behavior ended\n* Fixed bug with ending roads near stop signals to break the simulation\n* Fixed exception bug in spawn function of CarlaDataProvider\n* Fixed access to private member of CARLA LocalPlanner inside OSC NpcVehicleControl\n* Fixed handling of OSC LanePosition (#625)\n* Fixed bug causing the route repetitions to spawn different background activity\n* Fixed bug causing the rotate_point function inside RunningRedLightTest to not function properly.\n### :ghost: Maintenance\n* Exposed traffic manager port flag to enable the execution of multiple scenarios on a single machine.\n\n## CARLA ScenarioRunner 0.9.9\n### :rocket: New Features\n* OpenSCENARIO support:\n - Support for OpenSCENARIO 1.0 (a converter for old scenarios is available)\n - Added support for position with Lane information (roadId and laneId)\n - Added support to use a non-CARLA OpenDRIVE map (instead of CARLA towns)\n - Added support for TimeOfDay tag\n - Added support for scenarios with no actors\n* Scenario updates:\n - Scenarios that are part of RouteScenario have had their triggering condition modified. This will only activate when a certain parameter is set, and if not, the old trigger condition will still be applied.\n* Atomics:\n - ChangeAutopilot now calls a TM instance, and allows to change its parameters\n - Added WaitUntilInFront behavior and InTimeToArrivalToVehicleSideLane trigger condition, useful for cut ins\n - Added new trigger condition, AtRightestLane, which checks if the actor is at the rightmost driving lane\n - Added new criteria, ActorSpeedAboveThresholdTest, useful to check if the ego vehicle has been standing still for long periods of time.\n* Setting up actors in batch now also randomizes their colors\n* When running routes, the weather parameters of each route can now be changed at will. Check the first route at srunner/data/routes_training.xml to see the correct format to do so. By default the weather is now a sunny midday.\n* **Important** All challenge related content has been removed. This functionality has been improved and is now part of the [Leaderboard](https://github.com/carla-simulator/leaderboard). As a consequence:\n - The path to the autoagents has changed from .../challenge/autoagents to .../autoagents\n - The path to the route and scenario descriptions has changed from .../challenge to .../data\n### :bug: Bug Fixes\n* Fixed spawning bugs for scenario DynamicObjectCrossing when it is part of a route\n* Fixed spawning bugs for scenarios VehicleTurningRight, VehicleTurningLeft when they are part of a route\n* Fixed bug causing the GPS coordinates given to the agents to be wrongly calculated\n* Fixed bug when setting up actors in batch causing to ignore the spawn points given.\n* Fixed bug where CollisionTest was counting as multiple hits collisions that displaced the actor for a long distance.\n* Fixed bug causing the simulation to end after running in synchronous mode\n* Fixed bug when using the WaypointFollower atomic to create new LocalPlanners for on-the-fly created actors (#502)\n* Fixed bug causing the scenarios to run faster than real time.\n### :ghost: Maintenance\n* Removed perform_carla_tick() function at CarlaDataProvider, which was a workaround for world.tick()\n\n\n## CARLA ScenarioRunner 0.9.8\n### :rocket: New Features\n* Added \"--timeout\" command line parameter to set a user-defined timeout value\n* Scenario updates:\n - Changed traffic light behavior of scenarios 7, 8 and 9. The new sequence is meant to greatly improve the chances of the ego vehicle having to interact at junctions.\n* OpenSCENARIO support:\n - Added initial support for Catalogs (Vehicle, Pedestrian, Environment, Maneuver, and and MiscObject types only)\n### :bug: Bug Fixes\n* Fixed #471: Handling of weather parameter (cloudyness -> cloudiness adaption)\n* Fixed #472: Spawning issue of pedestrians in OpenSCENARIO\n* Fixed #374: Usage of evaluation critieria with multiple ego vehicles in OpenSCENARIO\n* Fixed #459: Add initial support for Catalogs (Vehicle, Pedestrian, Environment, Maneuver, and and MiscObject types only)\n* Fixed wrong StandStill behavior which return SUCCESS immediatly on a standing actor\n* Fixed scenario bug causing junction related scenarios (4, 7, 8 and 9) to not spawn due to lane changes.\n### :ghost: Maintenance\n* Added watchdog to ScenarioManager to handle timeouts and CARLA crashes\n* Added timeout for CARLA tick() calls to avoid blocking CARLA server calls\n\n\n## CARLA ScenarioRunner 0.9.7\n**This is the _first_ release to work with CARLA 0.9.7 (not the patch versions 0.9.7.x)**\n### :rocket: New Features\n* Challenge routes can be directly executed with the ScenarioRunner using the --route option\n* Agents can be used with the ScenarioRunner (currently only for route-based scenarios)\n* New scenarios:\n - Added example scenario for lane change\n - Added cut-in example scenario\n* Scenario updates:\n - Scenarios 7 to 10 are now visible when running routes (instead of being triggered in the background). Their\n methodology has remained unchanged\n* Scenario atomics:\n - Added new OutsideRouteLanesTest atomic criter that encompasses both SidewalkTest and WrongLaneTest, returning\n the percentage of route that has been traveled outside the lane.\n - InRouteTest is now more forgiving. The max distance has been increased, but staying above the previous one will eventually \n also cause failure\n - Changed SidewalkTest atomic criteria to also track other type of out of lane conditions\n - SidewalkTest and WrongLaneTest atomic criterias now track the amount of meters traversed\n - CollisionTest atomic criteria now correctly ignores multiple micro-collisions with the same object\n - Added LaneChange and TrafficLightSateSetter behavior atomics\n - Added AccelerateToCatchUp behavior atomic\n - Added get_transform() method for CarlaDataProvider\n - Added support for weather conditions\n - Added basic version check to ensure usage of correct CARLA version\n - WaypointFollower atomic can handle pedestrians\n - Extensions in WaypointFollower atomic for consecutive WaypointFollowers (one WF cancels the previous one)\n* Extended OpenScenario support:\n - Added support for UserDefinedActions (e.g. to run additional scripts)\n - Added init speed behavior for vehicles\n - Added support for relative velocities\n - Extended convert_position_to_transform with RelativeWorld, RelativeObject and RelativeLane osc_positions\n - Added new trigger atomics InTriggerDistanceToOSCPosition and InTimeToArrivalToOSCPosition to support relative osc_positions\n - Added new atomic behaviour ActorTransformSetterToOSCPosition\n - Workaround for relative osc_positions: World is started earlier to support relative osc_positions in story init\n - Added delay condition support in convert_condition_to_atomic\n - Added support for pedestrians\n - Full support for SimulationTime condition\n - Added weather support\n - Updated implementation to be closer to upcoming OpenSCENARIO standard\n - AfterTermination, AtStart conditions are supported\n - Added initial support for lateral action: LaneChange\n - Added initial support for OSCGlobalAction to set state of traffic signal\n - FollowRoute action is supported for vehicles and pedestrians, for global world positions.\n - Added support for RoadCondition: Friction\n - Redundant rolename object property is no longer required\n - Added support for global parameters\n - Fixed coordinate system to use right-hand as default. Left-hand CARLA system can be used by adding \"CARLA:\" at the start of the description in the FileHeader.\n - Added support to change actor color\n - Added support for a default actor model, in case the stated model is not available\n - Added support for MiscObjects (besides vehicles and pedestrians)\n - Reworked traffic signal handling: The name has to start now either with \"id=\" or \"pos=\" depending on whether the position or id is used as unique identifier\n - Actor physics can now be set via Object Properties ()\n### :bug: Bug Fixes\n* Fixed wrong handling of OpenSCENARIO ConditionGroups, which should be handled as parallel composites, not sequences\n* Fixed #443: Repetitions in OpenSCENARIO were not properly working\n* Fixed bug causing RunningStopTest atomic criteria to trigger when lane changing near a STOP signal\n* Fixed bug causing RunningRedLightTest atomic criteria to occasionally not trigger\n* Fixed bug causing occasional frame_errors\n* Fixed #426: Avoid underground vehicles fall forever by disabling physics when spawning underground.\n* Fixed #427: Removed unnecessary warnings when using get_next_traffic_light() with non-cached locations\n* Fixed missing ego_vehicle: compare actor IDs instead of object in CarlaDataProvider in get_velocity, get_transform and get_location\n* Avoided use of 'controller.ai.walker' as walker type in DynamicObjectCrossing scenario\n* Fixed WaypointFollower behavior to use m/s instead of km/h\n* Fixed starting position of VehicleTurnLeft/Right scenarios\n* Fixed spawn_point modification inside CarlaActorPool.setup_actor()\n* Fixed result of DrivenDistanceTest\n* Fixed exception in manual_control on fps visualization\n* Cleanup of pylint errors for all autonomous agents\n* Fixed randomness of route-based scenarios\n* Fixed usage of radians instead of degrees for OpenSCENARIO\n* Fixed ActorTransformSetter behavior to avoid vehicles not reaching the desired transform\n* Fixed spawning of debris for ControlLoss scenario (Scenario01)\n* Fixed CTRL+C termination of ScenarioRunner\n### :ghost: Maintenance\n* Increased speed of actor initialization by using CARLA batch mode and buffering CARLA blueprint library\n* Split of behaviors into behaviors and conditions\n* Moved atomics into new submodule scenarioatomics\n* Updated documentation for all behaviors, conditions and test criteria\n* Refactoring of scenario configurations and parsers\n* Extended WaypointFollower atomic behavior to be able to use the current actor speed\n* Removed usage of 'import *' to have cleaner Python imports\n* Removed broad-except and bare-except where possible\n* Python-Scenarios: Removed obsolete categories\n* ScenarioRunner: Removed scenario dictonary, use imports directly\n* CarlaDataProvider: Simplified update_light_states() to remove code duplication\n* Timer: class TimeOut() is derived from SimulationTimeCondition() to avoid code duplication\n* Moved backported py_trees classes and methods to tools/py_trees_port.py to avoid code duplication\n* Removed setup_environment.sh\n* Adaptions to CARLA API Changes\n - Renamed GnssEvent to GnssMeasurement\n\n## CARLA ScenarioRunner 0.9.6\n**This is the _first_ release to work with CARLA 0.9.6**\n### :ghost: Maintenance\n* Adapted to CARLA API changes\n - Frame rate is set now via Python\n - Renamed frame_count and frame_number to frame\n - Removed wait_for_tick() calls\n\n\n## CARLA ScenarioRunner 0.9.5.1\n**This is the _last_ release that works with CARLA 0.9.5**\n### :rocket: New Features\n* Added initial support for OpenScenario v0.9.1\n* Added support for multiple ego vehicles plus an example\n* Added commandline option for output directory\n* Added option to load external scenario implementations (in python)\n* Added option to scenario_runner to load external scenario XMLs\n* Atomic behaviors:\n - Extended KeepVelocity atomic behavior to support duration/distance\n based termination\n - Extended StandStill atomic behavior to support duration based\n termination\n - Added behavior to activate/deactivate autopilot\n### :bug: Bug Fixes\n* Fixed WaypointFollower initialization\n\n\n## CARLA ScenarioRunner 0.9.5\n**This is the _first_ release to work with CARLA 0.9.5**\n### :rocket: New Features\n* Added support for CARLA challenge\n - Added logging functionalities to challenge_evaluator_routes.py\n - Added wall clock timeout for the CARLA challenge\n - Added background scenario to generate dynamic traffic using autopilot\n - Updated compatibility with Python 2.7 for the challenge evaluator\n - Updated WaypointFollower behavior\n - Added detect_lane_obstacle() helper function which identifies if an obstacle is present in front of the reference actor\n - Added test to detect vehicles running a stop\n - Updated the reference position for a scenario is now called trigger_point\n - Added universal access to the map without re-calling get_map()\n - Added criteria_enable flag to enable/disable criteria tree\n - Added multiple helper methods for generic scenario execution.\n - Added pseudo-sensors for SceneLayoutMeasurements and ObjectMeasurements for Track4 of the CARLA AD challenge\n - Added track identification for autonomous_agent.py\n - Added HDMap pseudo-sensor\n - Added new traffic event logger\n - Added various helper methods to allow generic scenario execution\n - Added method to calculate distance along a route\n - In challenge mode spawn exception are caught and the corresponding scenario is removed\n* Added new atomic behaviors using py_trees behavior tree library\n - BasicAgentBehavior: drive to target location using CARLA's BasicAgent\n - StandStill: check if a vehicle stands still\n - InTriggerDistanceToNextIntersection: check if a vehicle is within certain distance with respect to the next intersection\n - WaypointFollower: follows auto-generated waypoints indefinitely or follows a given waypoint list\n - HandBrakeVehicle: sets the handbrake value for a given actor\n - ActorDestroy: destroys a given actor\n - ActorTransformSetter: sets transform of given actor\n - ActorSource: creates actors indefinitely around a location if no other vehicles are present within a threshold\n - ActorSink: indefinitely destroys vehicles that wander close to a location within a threshold\n - InTriggerDistanceToLocationAlongRoute: check if an actor is within a certain distance to a given location along a given route\n* Added new atomic evaluation criteria\n - Added running red light test\n - Added running stop test\n - Added wrong way test\n* Added NHTSA Traffic Scenarios\n - Updated all traffic scenarios to let the other actors appear upon scenario triggering and removal on scenario end\n - ManeuverOppositeDirection: hero vehicle must maneuver in the opposite lane to pass a leading vehicle.\n - OtherLeadingVehicle: hero vehicle must react to the deceleration of leading vehicle and change lane to avoid collision and follow the vehicle in changed lane\n - SignalizedJunctionRightTurn: hero vehicle must turn right into the same direction of another vehicle crossing straight initially from a lateral direction and avoid collision at a signalized intersection.\n - SignalizedJunctionLeftTurn : hero vehicle is turning left at signalized intersection, cuts across the path of another vehicle coming straight crossing from an opposite direction.\n### :bug: Bug Fixes\n* Fixed SteerVehicle atomic behavior to keep vehicle velocity \n### :ghost: Maintenance\n* Reworked scenario execution\n - Updated folder structure and naming convention in lowercase\n - Extended CarlaDataProvider with method to get next relevant traffic light\n - Every scenario has to have a configuration provided as XML file.\n Currently there is one XML file for each scenario class\n - The scenario runner is now responsible for spawning/destroying the ego vehicle\n - Added a CarlaActorPool to share scenario-related actors between scenarios and the scenario_runner\n - Renamed vehicle -> actor\n - If all scenarios in one configurations file should be executed, the scenario_runner can be started with --scenario group:\n - Generalized ControlLoss and FollowLeadingVehicle scenarios\n - Added randomization option to scenario_runner and scenarios\n - The scenario behavior always starts with a wait behavior until the ego vehicle reached the scenario starting position\n - Created method _initialize_actors in basic scenario that can be overridden for scenario specific actor initialization\n* Updated NHTSA Traffic Scenarios\n - OppositeVehicleRunningRedLight: Updated to allow execution at different locations \n\n\n## CARLA ScenarioRunner 0.9.2\n**This release is designed to work with CARLA 0.9.2**\n### :rocket: New Features\n* Added Traffic Scenarios engine to reproduce complex traffic situations for training and evaluating driving agents\n* Added NHTSA Traffic Scenarios\n - FollowLeadingVehicle: hero vehicle must react to the deceleration of a leading vehicle\n - FollowLeadingVehicleWithObstacle: hero vehicle must react to a leading vehicle due to an obstacle blocking the road\n - StationaryObjectCrossing: hero vehicle must react to a cyclist or pedestrian blocking the road\n - DynamicObjectCrossing: hero vehicle must react to a cyclist or pedestrian suddenly crossing in front of it\n - OppositeVehicleRunningRedLight: hero vehicle must avoid a collision at an intersection regulated by traffic lights when the crossing traffic runs a red light\n - NoSignalJunctionCrossing: hero vehicle must cross a non-signalized intersection\n - VehicleTurningRight: hero vehicle must react to a cyclist or pedestrian crossing ahead after a right turn\n - VehicleTurningLeft: hero vehicle must react to a cyclist or pedestrian crossing ahead after a left turn\n - ControlLoss: Hero vehicle must react to a control loss and regain its control\n* Added atomic behaviors using py_trees behavior trees library\n - InTriggerRegion: new behavior to check if an object is within a trigger region\n - InTriggerDistanceToVehicle: check if a vehicle is within certain distance with respect to a reference vehicle\n - InTriggerDistanceToLocation: check if a vehicle is within certain distance with respect to a reference location\n - TriggerVelocity: triggers if a velocity is met\n - InTimeToArrivalToLocation: check if a vehicle arrives within a given time budget to a reference location\n - InTimeToArrivalToVehicle: check if a vehicle arrives within a given time budget to a reference vehicle\n - AccelerateToVelocity: accelerate until reaching requested velocity\n - KeepVelocity: keep constant velocity\n - DriveDistance: drive certain distance\n - UseAutoPilot: enable autopilot\n - StopVehicle: stop vehicle\n - WaitForTrafficLightState: wait for the traffic light to have a given state\n - SyncArrival: sync the arrival of two vehicles to a given target\n - AddNoiseToVehicle: Add noise to steer as well as throttle of the vehicle\n" }, { "path": "scenario_runner/Docs/CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as\ncontributors and maintainers pledge to making participation in our project and\nour community a harassment-free experience for everyone, regardless of age, body\nsize, disability, ethnicity, gender identity and expression, level of experience,\neducation, socio-economic status, nationality, personal appearance, race,\nreligion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment\ninclude:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or\n advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic\n address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable\nbehavior and are expected to take appropriate and fair corrective action in\nresponse to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or\nreject comments, commits, code, wiki edits, issues, and other contributions\nthat are not aligned to this Code of Conduct, or to ban temporarily or\npermanently any contributor for other behaviors that they deem inappropriate,\nthreatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces\nwhen an individual is representing the project or its community. Examples of\nrepresenting a project or community include using an official project e-mail\naddress, posting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event. Representation of a project may be\nfurther defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported by contacting the project team at [INSERT EMAIL ADDRESS]. All\ncomplaints will be reviewed and investigated and will result in a response that\nis deemed necessary and appropriate to the circumstances. The project team is\nobligated to maintain confidentiality with regard to the reporter of an incident.\nFurther details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good\nfaith may face temporary or permanent repercussions as determined by other\nmembers of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,\navailable at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html\n\n[homepage]: https://www.contributor-covenant.org\n" }, { "path": "scenario_runner/Docs/CONTRIBUTING.md", "content": "Contributing to CARLA\n=====================\n\nWe are more than happy to accept contributions!\n\nHow can I contribute?\n\n * Reporting bugs\n * Feature requests\n * Improving documentation\n * Code contributions\n\nReporting bugs\n--------------\n\nUse our [issue section][issueslink] on GitHub. Please check before that the\nissue is not already reported, and make sure you have read our CARLA\n[Documentation][docslink] and [FAQ][faqlink].\n\n[issueslink]: https://github.com/carla-simulator/scenario_runner/issues\n[docslink]: http://carla.readthedocs.io\n[faqlink]: http://carla.readthedocs.io/en/latest/faq/\n\nFeature requests\n----------------\n\nPlease check first the list of [feature requests][frlink]. If it is not there\nand you think is a feature that might be interesting for users, please submit\nyour request as a new issue.\n\n[frlink]: https://github.com/carla-simulator/scenario_runner/issues?q=is%3Aissue+is%3Aopen+label%3A%22feature+request%22+sort%3Acomments-desc\n\nImproving documentation\n-----------------------\n\nIf you feel something is missing in the documentation, please don't hesitate to\nopen an issue to let us know. Even better, if you think you can improve it\nyourself, it would be a great contribution to the community!\n\nWe build our documentation with [MkDocs](http://www.mkdocs.org/) based on the\nMarkdown files inside the \"Docs\" folder. You can either directly modify them on\nGitHub or locally in your machine.\n\nOnce you are done with your changes, please submit a pull-request.\n\n**TIP:** You can build and serve it locally by running `mkdocs` in the project's\nmain folder\n\n $ sudo pip install mkdocs\n $ mkdocs serve\n\nCode contributions\n------------------\n\nSo you are considering making a code contribution, great! we love to have\ncontributions from the community.\n\nBefore starting hands-on on coding, please check out our\n[issue board][wafflelink] to see if we are already working on that, it would\nbe a pity putting an effort into something just to discover that someone else\nwas already working on that. In case of doubt or to discuss how to proceed,\nplease contact one of us (or send an email to carla.simulator@gmail.com).\n\n[wafflelink]: https://waffle.io/carla-simulator/scenario_runner\n\n#### What should I know before I get started?\n\nCheck out the [\"CARLA Documentation\"][docslink] to get an idea on CARLA. In\naddition you may want to check the [Getting started](getting_started.md) document.\n\n[docslink]: http://carla.readthedocs.io\n\n#### Coding standard\n\nPlease follow the current [coding standard](coding_standard.md) when submitting\nnew code.\n\n#### Pull-requests\n\nOnce you think your contribution is ready to be added to CARLA, please submit a\npull-request.\n\nTry to be as descriptive as possible when filling the pull-request description.\nAdding images and gifs may help people to understand your changes or new\nfeatures.\n\nPlease note that there are some checks that the new code is required to pass\nbefore we can do the merge. The checks are automatically run by the continuous\nintegration system, you will see a green tick mark if all the checks succeeded.\nIf you see a red mark, please correct your code accordingly.\n\n###### Checklist\n\n - [ ] Your branch is up-to-date with the `master` branch and tested with latest changes\n - [ ] Extended the README / documentation, if necessary\n - [ ] Code compiles correctly\n" }, { "path": "scenario_runner/Docs/FAQ.md", "content": "# Frequently Asked Questions\n\n## I receive the error \"TypeError: 'instancemethod' object has no attribute '__getitem__'\" in the agent navigation\n\nThis issue is most likely caused by an outdated version of the Python Networkx package. Please remove the current installation\n(e.g. sudo apt-get remove python-networkx) and install it using \"pip install --user networkx==2.2\".\n\n## No scenario visible and I receive the message \"No more scenarios .... Exiting\"\n\nIn case you receive the following output\n```\nPreparing scenario: FollowLeadingVehicle_1\nScenarioManager: Running scenario FollowVehicle\nResetting ego-vehicle!\nFailure!\nResetting ego-vehicle!\nERROR: failed to destroy actor 527 : unable to destroy actor: not found\nNo more scenarios .... Exiting\n```\nand you see nothing happening, it is most likely due to the fact, that you did not launch a program to control\nthe ego vehicle. Run for example manual_control.py, and you should now see something happening.\n\n\n## Scenario Runner exits with error when using --debug commandline parameter\n\nIn case you receive the following output\n```\nUnicodeEncodeError: 'ascii' codec can't encode character '\\u2713' in position 58: ordinal not in range(128)\n```\nPlease set environment variable\n```\nPYTHONIOENCODING=utf-8\n```\n\n" }, { "path": "scenario_runner/Docs/agent_evaluation.md", "content": "#### Setting up your agent for evaluation\n\n## Important: This information is outdated and the challenge files have been removed. This feature has now been moved to the [Leaderboard](https://github.com/carla-simulator/leaderboard). However, it can still be used with the route argument of scenario_runner.py, instead of the challenge_evaluator.py\n\nTo have your agent evaluated by the challenge evaluation system\nyou must define an Agent class that inherits the\n[AutonomousAgent](https://github.com/carla-simulator/scenario_runner/blob/master/srunner/autoagents/autonomous_agent.py) base class. In addition, you need to setup your environment as described in the Challenge evaluator tutorial.\n\nOn your agent class there are three main functions to be overwritten\nthat need to be defined in order to set your agent to run.\nFurther you also should consider the route to the goal that is\ninitially set as a variable.\n\n\n##### The \"setup\" function:\nThis is the function where you should make all the necessary setup\nfor the your agent.\n\nThis function receives as a parameter the path to a configuration\nfile to be parsed by the user.\n\nWhen executing the \"challenge_evaluator.py\" you should pass the\nconfiguration file path as a parameter. For example:\n\n```\npython srunner/challenge/challenge_evaluator_routes.py --agent= --config=myconfigfilename.format\n```\n\n\n##### The \"sensors\" function:\n\nThis function is where you set all the sensors required by your agent.\nFor instance, on the dummy agent sample class the following sensors are defined:\n\n```Python\ndef sensors(self):\n sensors = [{'type': 'sensor.camera.rgb', 'x':0.7, 'y':0.0, 'z':1.60, 'roll':0.0, 'pitch':0.0, 'yaw':0.0, 'width':800, 'height': 600, 'fov':100, 'id': 'Center'},\n {'type': 'sensor.camera.rgb', 'x':0.7, 'y':-0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': -45.0, 'width': 800, 'height': 600, 'fov': 100, 'id': 'Left'},\n {'type': 'sensor.camera.rgb', 'x':0.7, 'y':0.4, 'z':1.60, 'roll':0.0, 'pitch':0.0, 'yaw':45.0, 'width':800, 'height':600, 'fov':100, 'id': 'Right'},\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': -45.0, 'id': 'LIDAR'},\n {'type': 'sensor.other.gnss', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'id': 'GPS'},\n {'type': 'sensor.speedometer','reading_frequency': 25, 'id': 'speed'}\n ]\n return sensors\n```\n\n\nEvery sensor is a dictionary where you should\nspecify:\n\n* type: basically which is the sensor to be added, for example: 'sensor.camera.rgb' for an rgb camera or 'sensor.lidar.ray_cast' for a ray casting lidar.\n* id: the label that will be given to the sensor in order for it to be accessed later.\n* other parameters: these are sensor dependent, such as position, 'x' and 'y', or the field of view for a camera, 'fov'\n\n\n\n\n##### The \"run_step\" function:\n\nThis function is called on every step of the simulation from the challenge evaluation\nan receives some input data as parameter.\n\nThis input data is a dictionary with all the sensors specified on the \"sensors\" function.\n\nThis function should return a [vehicle control](https://carla.readthedocs.io/en/latest/python_api_tutorial/#vehicles)\n to be applied into the ego vehicle.\n\n\n\n\n##### The initial route:\n\nOn the beginning of the execution, the entire route that the hero agent\nshould travel is set on the \"self.global_plan\" variable:\n\n```\n[({'z': 0.0, 'lat': 48.99822669411668, 'lon': 8.002271601998707}, ),\n ({'z': 0.0, 'lat': 48.99822669411668, 'lon': 8.002709765148996}, ),\n ...\n ({'z': 0.0, 'lat': 48.99822679980298, 'lon': 8.002735250105061}, )]`\n ```\n\n It is represented as a list of tuples, containing the route waypoints, expressed in latitude\n and longitude and the current road option recommended. For an intersection, the option can\n be go straight, turn left or turn right. For the rest of the route the recommended option\n is lane follow.\n" }, { "path": "scenario_runner/Docs/coding_standard.md", "content": "

Coding standard

\n\n> _This document is a work in progress and might be incomplete._\n\nGeneral\n-------\n\n * Use spaces, not tabs.\n * Avoid adding trailing whitespace as it creates noise in the diffs.\n * Comments should not exceed 120 columns, code may exceed this limit a bit in\n rare occasions if it results in clearer code.\n\nPython\n------\n\n * All code must be compatible with Python 2.7, 3.5, and 3.6.\n * [Pylint][pylintlink] should not give any error or warning (few exceptions\n apply with external classes like `numpy`, see our `.pylintrc`).\n * Python code follows [PEP8 style guide][pep8link] (use `autopep8` whenever\n possible).\n\n[pylintlink]: https://www.pylint.org/\n[pep8link]: https://www.python.org/dev/peps/pep-0008/\n\nC++\n---\n\n * Compilation should not give any error or warning\n (`clang++ -Wall -Wextra -std=C++14 -Wno-missing-braces`).\n * Unreal C++ code (CarlaUE4 and Carla plugin) follow the\n [Unreal Engine's Coding Standard][ue4link] with the exception of using\n spaces instead of tabs.\n * LibCarla uses a variation of [Google's style guide][googlelink].\n\n[ue4link]: https://docs.unrealengine.com/latest/INT/Programming/Development/CodingStandard/\n[googlelink]: https://google.github.io/styleguide/cppguide.html\n" }, { "path": "scenario_runner/Docs/creating_new_scenario.md", "content": "# Create a new scenario tutorial\n\nThis tutorial describes how you can create and run a new scenario using the\nScenarioRunner and the ScenarioManager suite.\n\nLet us call the new scenario _NewScenario_. To create it, there are only few\nsteps required.\n\n## Creating an empty Python class\nGo to the Scenarios folder and create a new Python class with the name\n_NewScenario_ in a new Python file (_new_scenario.py_). The class should be\nderived from the _BasicScenario_ class. As a result, the class should look as\nfollows:\n\n ```\n class NewScenario(BasicScenario):\n \"\"\"\n Some documentation on NewScenario\n :param world is the CARLA world\n :param ego_vehicles is a list of ego vehicles for this scenario\n :param config is the scenario configuration (ScenarioConfiguration)\n :param randomize can be used to select parameters randomly (optional, default=False)\n :param debug_mode can be used to provide more comprehensive console output (optional, default=False)\n :param criteria_enable can be used to disable/enable scenario evaluation based on test criteria (optional, default=True)\n :param timeout is the overall scenario timeout (optional, default=60 seconds)\n \"\"\"\n\n # some ego vehicle parameters\n # some parameters for the other vehicles\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Initialize all parameters required for NewScenario\n \"\"\"\n\n # Call constructor of BasicScenario\n super(NewScenario, self).__init__(\n name=\"NewScenario\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable))\n\n\n def create_behavior(self):\n \"\"\"\n Setup the behavior for NewScenario\n \"\"\"\n\n def create_test_criteria(self):\n \"\"\"\n Setup the evaluation criteria for NewScenario\n \"\"\"\n ```\n\n## Filling the Python class\n\nIn the NewScenario class, you have to define the following methods mentioned\nin the code example.\n\n### Initialize Method\nThe initialize method is intended to setup all parameters required\nfor the scenario and all vehicles. This includes selecting the correct vehicles,\nspawning them at the correct location, etc. To simplify this, you may want to\nuse the _setup_vehicle()_ function defined in basic_scenario.py\n\n### CreateBehavior method\nThis method should setup the behavior tree that contains the behavior of all\nnon-ego vehicles during the scenario. The behavior tree should use py_trees and\nthe atomic behaviors defined in _atomic_scenario_behavior.py_\n\n### CreateTestCriteria method\nThis method should setup a list with all evaluation criteria for the scenario.\nThe criteria should be based on the atomic criteria defined in\n_atomic_scenario_criteria.py_.\n\nNote: From this list a parallel py_tree will be created automatically!\n\n## Adding the scenario configuration\nFinally the scenario configuration should be added to the examples/ folder. If you\nextend an already existing scenario module, you can simply extend the corresponding\nXML, otherwise add a new XML file. In this case you can use any of the existing\nXML files as blueprint.\n\nIf you want to add multiple ego vehicles for a scenario, make sure that they use different\nrole names, e.g.\n```\n \n \n \n \n```\n" }, { "path": "scenario_runner/Docs/extra.css", "content": ".build-buttons{\n text-align: center;\n}\n\n.build-buttons > p {\n display: inline-block;\n vertical-align: top;\n padding: 5px;\n}\n\n.vector-zero {\n text-align: center;\n}\n\n\n/************************* DEFAULT TABLES **************************/\n\ntable.defTable {\n border: 1px solid #242424;\n background-color: #f3f6f6;\n text-align: left;\n border-collapse: collapse;\n}\n\ntable.defTable thead {\n background: #ffffff; \n border-bottom: 1px solid #444444;\n}\n\ntable.defTable tr:nth-child(even) {\n background: #ffffff;\n}\n\ntable.defTable thead th {\n padding: 7px 13px;\n}\n\ntable.defTable tbody td{\n padding: 7px 13px;\n}\n\n/************************* TOWN SLIDER **************************/\n\n * {box-sizing:border-box}\n\n/* Container */\n.townslider-container {\n max-width: 1000px;\n position: relative;\n margin: auto;\n}\n\n/* Hide the images by default */\n.townslide {\n display: none;\n text-align: center; \n\n}\n\n/* Fading animation for slides */\n.fade {\n -webkit-animation-name: fade;\n -webkit-animation-duration: 1.5s;\n animation-name: fade;\n animation-duration: 1.5s;\n}\n\n@-webkit-keyframes fade {\n from {opacity: .4}\n to {opacity: 1}\n}\n\n@keyframes fade {\n from {opacity: .4}\n to {opacity: 1}\n}\n\n/* \"next\" and \"previous\" buttons */\n.prev, .next {\n cursor: pointer;\n position: absolute;\n top: 50%;\n width: auto;\n margin-top: -22px;\n padding: 16px;\n color: white;\n font-weight: bold;\n font-size: 18px;\n transition: 0.6s ease;\n border-radius: 0 3px 3px 0;\n user-select: none;\n}\n\n/* Position the \"next\" button*/\n.next {\n right: 0;\n border-radius: 3px 0 0 3px;\n}\n\n/* Black background color to buttons when hovering*/\n.prev:hover, .next:hover {\n background-color: rgba(0,0,0,0.8);\n}\n\n/* Caption text for towns */\n.text {\n color: #f2f2f2;\n font-size: 15px;\n padding: 8px 12px;\n position: absolute;\n bottom: 8px;\n width: 100%;\n text-align: center;\n /*background-color:rgba(0,0,0,0.5);*/\n}\n\n/* The dot indicators for slides */\n.dot {\n cursor: pointer;\n height: 15px;\n width: 15px;\n margin: 0 2px;\n background-color: #bbb;\n border-radius: 50%;\n display: inline-block;\n transition: background-color 0.6s ease;\n}\n\n.active, .dot:hover {\n background-color: #717171;\n}\n\n" }, { "path": "scenario_runner/Docs/getting_scenariorunner.md", "content": "# Get ScenarioRunner\n\nThis tutorial explains how to download ScenarioRunner and run a simple example to test it. ScenarioRunner needs CARLA in order to run, and must match the CARLA version being used. If the CARLA being used is a build from source, download ScenarioRunner from source. If the CARLA being used is a package, download the corresponding version of ScenarioRunner. \n\n* __[Installation summary](#installation-summary)__ \n* __[A. Download a ScenarioRunner release](#a.-download-a-scenariorunner-release)__ \n\t* [Update the release](#update-the-release) \n* __[B. Download ScenarioRunner from source](#b.-build-scenariorunner-from-source)__ \n\t* [Update the build from source](#update-the-build-from-source) \n* __[Run a test](#run-a-test)__ \n\n---\n## Installation summary\n\n
\n \n Show command line summary for the quick start installation\n \n\n```sh\n# Decide whether to use a package or make the build from source\n\n\n# Option A) Use a ScenarioRunner package\n # 1. Install a CARLA package: \n https://carla.readthedocs.io/en/latest/start_quickstart/\n # 2. Download the matching ScenarioRunner package: \n https://github.com/carla-simulator/scenario_runner/releases\n # 3. Extract the content wherever needed. \n\n # Update the release: \n # 1. Delete previous CARLA and ScenarioRunner versions.\n # 2. Download the latest CARLA release. \n # 3. Download the matching ScenarioRunner release.\n\n\n# Option B) Download ScenarioRunner from source\n # 1. Build CARLA from source:\n https://carla.readthedocs.io/en/latest/build_linux/\n # 2. Clone the ScenarioRunner repository: \ngit clone https://github.com/carla-simulator/scenario_runner.git\n # 3. Install requirements according to the Python version to be used: \n # For Python 2.x:\nsudo apt remove python-networkx #if installed, remove old version of networkx\npip2 install --user -r requirements.txt\n # For Python 3.x: \nsudo apt remove python3-networkx #if installed, remove old version of networkx\npip3 install --user -r requirements.txt\n\n # To update ScenarioRunner from source:\n # 1. Update CARLA: \n https://carla.readthedocs.io/en/latest/build_update/\n # 2. Go to the ScenarioRunner repository, master branch\ncd ~/scenario_runner\ngit branch master\n # 3. Pull the latest changes from the repository\ngit pull \n\n```\n
\n\n\n---\n## A. Download a ScenarioRunner release\n\nThe releases of ScenarioRunner are packages containing: \n* A ScenarioRunner version tied to a specific CARLA release. \n* A few example scenarios written in Python. \n\nThe process to run a ScenarioRunner release is quite straightforward. \n\n__1. Download a CARLA release.__ Follow the process in the [CARLA quick start](https://github.com/carla-simulator/carla/releases). \n__2. Download the matching ScenarioRunner release.__ All of the releases are listed [here](https://github.com/carla-simulator/scenario_runner/releases).\n\n!!! Important\n Both versions have to match. If the CARLA release is *0.9.9*, use also ScenarioRunner *0.9.9*. [Here](https://github.com/carla-simulator/scenario_runner) is a brief list of compatibilities between CARLA and ScenarioRunner. \n\n__3. Extract the content.__ The directory does not matter. \n\n\n### Update the release\n\nThe packaged version requires no updates. The content is bundled and thus, tied to a specific release of CARLA. Everytime there is a new CARLA release, there will be a matching one for ScenarioRunner. All the releases are listed here: \n\n* [CARLA releases](https://github.com/carla-simulator/carla/releases) \n* [Scenario Runner releases](https://github.com/carla-simulator/scenario_runner/releases) \n\nTo run the latest or any other release, delete the previous and install the one desired. \n\n---\n## B. Download ScenarioRunner from source\n\nThe ScenarioRunner source repository contains the most experimental features that run with the latest development version of CARLA. It requires no build, as it only contains Python code for the ScenarioRunner module. \n\n__1. Build CARLA from source.__ Follow the docs to build on [Linux](https://carla.readthedocs.io/en/latest/build_linux/) or [Windows](https://carla.readthedocs.io/en/latest/build_windows/). \n\n!!! Important\n ScenarioRunner needs CARLA to run, so the minimum requirements for CARLA stated in the docs are also necessary to run ScenarioRunner. \n\n__2. Clone the ScenarioRunner repository.__\n\n```sh\ngit clone https://github.com/carla-simulator/scenario_runner.git\n```\n\n__3. Install the requirements according to the Python version to be used.__ First go to the main ScenarioRunner directory\n\n```sh\ncd ~/scenario_runner/\n```\n\n* __For Python 2.x.__ \n\n```sh\nsudo apt remove python-networkx #if installed, remove old version of networkx\npip2 install --user -r requirements.txt\n```\n\n* __For Python 3.x__ \n```sh\nsudo apt remove python3-networkx #if installed, remove old version of networkx\npip3 install --user -r requirements.txt\n```\n\n!!! Warning\n py-trees newer than v0.8 are __not__ supported.\n\n\n### Update from source \n\n__1. Update the CARLA build__ Follow the [docs](https://carla.readthedocs.io/en/latest/build_update/) to update CARLA. \n\n__2. Go to the main ScenarioRunner directory.__ Make sure to be in the local master branch. \n\n```sh\ncd ~/scenario_runner\ngit branch master\n```\n__3. Pull the latest changes from the repository.__ \n\n```sh\ngit pull\n```\n\n__4. Add environment variables and Python paths__ These are necessary for the system to find CARLA, and add the PythonAPI to the Python path.\n\n* __For Linux__\n\n```sh\n# ${CARLA_ROOT} is the CARLA installation directory\n# ${SCENARIO_RUNNER} is the ScenarioRunner installation directory\n# is the correct string for the Python version being used\n# In a build from source, the .egg files may be in: ${CARLA_ROOT}/PythonAPI/dist/ instead of ${CARLA_ROOT}/PythonAPI\nexport CARLA_ROOT=/path/to/your/carla/installation\nexport SCENARIO_RUNNER_ROOT=/path/to/your/scenario/runner/installation\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-.egg\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/agents\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI\n```\n\n!!! Note\n Change the command lines with the proper paths, according to the comments. \n\n* __For Windows__\n\n\n```sh\n# %CARLA_ROOT% is the CARLA installation directory\n# %SCENARIO_RUNNER% is the ScenarioRunner installation directory\n# is the correct string for the Python version being used\n# In a build from source, the .egg files may be in: ${CARLA_ROOT}/PythonAPI/dist/ instead of ${CARLA_ROOT}/PythonAPI\nset CARLA_ROOT=\\path\\to\\your\\carla\\installation\nset SCENARIO_RUNNER_ROOT=\\path\\to\\your\\scenario\\runner\\installation\nset PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\\PythonAPI\\carla\\dist\\carla-.egg\nset PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\\PythonAPI\\carla\\agents\nset PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\\PythonAPI\\carla\nset PYTHONPATH=%PYTHONPATH%:%CARLA_ROOT%\\PythonAPI\n```\n\n!!! Note\n Change the command lines with the proper paths, according to the comments. \n\nTo permanently set the environment variables, go to *edit the environment variables of this account*. This can be quickly accessed by writing *env* on the Windows' search panel.\n\n---\n## Run a test \n\nRunning the follow vehicle example\nFirst of all, you need to get latest master branch from CARLA. Then you have to\ninclude CARLA Python API to the Python path:\n\n!!! Important\n If working with builds from source, make sure to upload these. Download the latest content in the master branches. \n\n\n__1. Run the CARLA server.__\n\n* __A) In a build from source__ go to the CARLA directory and launch the server in the editor. \n\n```sh\ncd ~/carla # Change the path accordingly\nmake launch\n# Press Play in the UE Editor\n```\n\n* __B) In a CARLA package__ run the server directly. \n\n```sh\n./CarlaUE4.sh\n```\n\n__2. Start an example scenario.__ Open another terminal and go to the directory where ScenarioRunner is downloaded. For the sake of this test, the follow leading vehicle scenario will be used. \n\n```sh\n# Inside the ScenarioRunner root directory\npython scenario_runner.py --scenario FollowLeadingVehicle_1 --reloadWorld\n```\n\n!!! Note\n If using a Python 3.x version, run the command with `python3`. \n\n__3. Test the scenario with manual control.__ Open a new terminal and run the `manual_control.py`. A new window should pop up, with an ego vehicle in the middle of the street. Move forward and the leading vehicle will appear. \n\n```sh\n# Inside the ScenarioRunner root directory\npython manual_control.py\n```\n\nThe scenarios have a timeout of one minute approximately, for the agent to be launched. If the timeout appears, the follow leading vehicle example should be launched again. \n\n!!! Warning\n Run the `manual_control.py` found in the ScenarioRunner package/repository, __not CARLA__. \n\n__4. Explore other options.__ Run the Scenario Runner with the flag `--help` to explore other command line parameters and some basic descriptions. For example, to avoid automatically (re-)load the CARLA world, skip the command line option `--reloadWorld`.\n\n```sh\npython scenario_runner.py --help\n```\n\n---\n\nThus concludes the installation process for ScenarioRunner. In case any unexpected error or issue occurs, the [CARLA forum](https://forum.carla.org/c/using-carla/scenario-runner) is open to everybody. There is an _ScenarioRunner_ category to post problems and doubts regarding this module. \n\n" }, { "path": "scenario_runner/Docs/getting_started.md", "content": "# Getting Started Tutorial\n\n!!! important\n This tutorial refers to the latest versions of CARLA (at least 0.9.5)\n\nWelcome to the ScenarioRunner for CARLA! This tutorial provides the basic steps\nfor getting started using the ScenarioRunner for CARLA.\n\nDownload the latest release from our GitHub page and extract all the contents of\nthe package in a folder of your choice.\n\nThe release package contains the following\n\n * The ScenarioRunner for CARLA\n * A few example scenarios written in Python.\n\n## Installing prerequisites\nThe current version is designed to be used with Ubuntu 16.04, Python 2.7 or\nPython 3.5. Depending on your Python version, execute:\n```\n\n\n#Python 2.x\nsudo apt remove python-networkx #if installed, remove old version of networkx\npip2 install --user -r requirements.txt\n#Python 3.x\nsudo apt remove python3-networkx #if installed, remove old version of networkx\npip3 install --user -r requirements.txt\n```\nNote: py-trees newer than v0.8 is *NOT* supported.\n\n\n\n## Running the follow vehicle example\nFirst of all, you need to get latest master branch from CARLA. Then you have to\ninclude CARLA Python API to the Python path:\n```Bash\nexport CARLA_ROOT=/path/to/your/carla/installation\nexport PYTHONPATH=$PYTHONPATH:${CARLA_ROOT}/PythonAPI/carla/dist/carla-.egg:${CARLA_ROOT}/PythonAPI/carla/agents:${CARLA_ROOT}/PythonAPI/carla\n```\nNOTE: ${CARLA_ROOT} needs to be replaced with your CARLA installation directory,\n and needs to be replaced with the correct string.\n If you build CARLA from source, the egg files maybe located in:\n ${CARLA_ROOT}/PythonAPI/dist/ instead of ${CARLA_ROOT}/PythonAPI.\n\nNow, you can start the CARLA server from ${CARLA_ROOT}\n```\n./CarlaUE4.sh\n```\n\nStart the example scenario (follow a leading vehicle) in an extra terminal:\n```\npython scenario_runner.py --scenario FollowLeadingVehicle_1 --reloadWorld\n```\n\nIf you require help or want to explore other command line parameters, start the scenario\nrunner as follows:\n```\npython scenario_runner.py --help\n```\n\nTo control the ego vehicle within the scenario, open another terminal and run:\n```\npython manual_control.py\n```\n\nNote: If you do not wish to automatically (re-)load the CARLA world, you can\nskip the command line option _--reloadWorld_\n\n## Running all scenarios of one scenario class\nSimilar to the previous example, it is also possible to execute a sequence of scenarios,\nthat belong to the same class, e.g. the \"FollowLeadingVehicle\" class.\n\nThe only difference is, that you start the scenario_runner as follows:\n```\npython scenario_runner.py --scenario group:FollowLeadingVehicle\n```\n\n## Running other scenarios\nA list of supported scenarios is provided in\n[List of Supported Scenarios](list_of_scenarios.md). Please note that\ndifferent scenarios may take place in different CARLA towns. This has to be\nrespected when launching the CARLA server.\n\n## Running scenarios using the OpenSCENARIO format\nTo run a scenario, which is based on the OpenSCENARIO format, please run the ScenarioRunner as follows:\n```\npython scenario_runner.py --openscenario \n```\nPlease note that the OpenSCENARIO support and the OpenSCENARIO format itself are still work in progress.\nMore information you can find in [OpenSCENARIO support](openscenario_support.md)\n\n## Running route-based scenario (similar to the CARLA AD Challenge)\nTo run a route-based scenario, please run the ScenarioRunner as follows:\n```\npython scenario_runner.py --route [route id] --agent \n```\nExample:\n```\npython scenario_runner.py /scenario_runner/srunner/routes_debug.xml /scenario_runner/srunner/data/all_towns_traffic_scenarios1_3_4.json 0 --agent srunner/autoagents/npc_agent.py\n```\n\nIf no route id is provided, all routes within the given file will be executed.\n\n\nBy doing so, ScenarioRunner will match the scenarios to the route, and they'll activate when the ego vehicle is nearby. However, routes need an autonomous agent to control the ego vehicle. Several examples are provided in srunner/autoagents/. For more information about agents, please have a look into the [agent documentation](agent_evaluation.md)\n\n" }, { "path": "scenario_runner/Docs/index.md", "content": "[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n![GitHub tag (latest SemVer)](https://img.shields.io/github/tag/carla-simulator/scenario_runner.svg)\n[![Build Status](https://travis-ci.com/carla-simulator/scenario_runner.svg?branch=master)](https://travis-ci.com/carla/scenario_runner)\n\n# ScenarioRunner\n\nScenarioRunner is a module that allows traffic scenario definition and execution for the [CARLA](http://carla.org/ ) simulator. The scenarios can be defined through a Python interface or using the [OpenSCENARIO](http://www.openscenario.org/) standard. \n \nScenarioRunner can also be used to prepare AD agents for their evaluation, by easily creating complex traffic scenarios and routes for the agents to navigate through. These results can be validated and shared in the [CARLA Leaderboard](https://leaderboard.carla.org/), an open platform for the community to fairly compare their progress, evaluating agents in realistic traffic situatoins.\n\n\nThe CARLA forum has a specific section regarding ScenarioRunner, for users to post any doubts or suggestions that may arise during the reading of this documentation. \n
\n\nCARLA forum\n
\n\n---\n\n## Quick start\n

\n [__Get ScenarioRunner__](getting_scenariorunner.md)\n — Tutorial on how to download and launch ScenarioRunner. \n [__First steps__](getting_started.md)\n — Brief tutorials on how to run different types of scenarios. \n [__Create a new scenario__](creating_new_scenario.md)\n — Tutorial on how to create a new scenario using ScenarioRunner. \n [__Metrics module__](metrics_module.md)\n — Explanation of the metrics module. \n [__F.A.Q.__](FAQ.md)\n — Some of the most frequent installation issues. \n [__Release notes__](CHANGELOG.md)\n — Features, fixes and other changes listed per release. \n

\n\n## References\n

\n [__List of scenarios__](list_of_scenarios.md)\n — Example scenarios available in ScenarioRunner. \n [__OpenScenario support__](getting_started.md)\n — Support status of OpenSCENARIO features. \n

\n\n## Contributing\n

\n [__Code of conduct__](CODE_OF_CONDUCT.md)\n — Standard rights and duties for contributors.\n [__Coding standard__](coding_standard.md)\n — Guidelines to write proper code. \n [__Contribution guidelines__](CONTRIBUTING.md)\n — The different ways to contribute to ScenarioRunner.\n

\n\n" }, { "path": "scenario_runner/Docs/list_of_scenarios.md", "content": "# List of Supported Scenarios\n\nWelcome to the ScenarioRunner for CARLA! This document provides a list of all\ncurrently supported scenarios, and a short description for each one.\n\n### FollowLeadingVehicle\nThe scenario realizes a common driving behavior, in which the user-controlled\nego vehicle follows a leading car driving down a given road in Town01. At some\npoint the leading car slows down and finally stops. The ego vehicle has to react\naccordingly to avoid a collision. The scenario ends either via a timeout, or if\nthe ego vehicle stopped close enough to the leading vehicle\n\n### FollowLeadingVehicleWithObstacle\nThis scenario is very similar to 'FollowLeadingVehicle'. The only difference is,\nthat in front of the leading vehicle is a (hidden) obstacle that blocks the way.\n\n### VehicleTurningRight\nIn this scenario the ego vehicle takes a right turn from an intersection where\na cyclist suddenly drives into the way of the ego vehicle,which has to stop\naccordingly. After some time, the cyclist clears the road, such that ego vehicle\ncan continue driving.\n\n### VehicleTurningLeft\nThis scenario is similar to 'VehicleTurningRight'. The difference is that the ego\nvehicle takes a left turn from an intersection.\n\n### OppositeVehicleRunningRedLight\nIn this scenario an illegal behavior at an intersection is tested. An other\nvehicle waits at an intersection, but illegally runs a red traffic light. The\napproaching ego vehicle has to handle this situation correctly, i.e. despite of\na green traffic light, it has to stop and wait until the intersection is clear\nagain. Afterwards, it should continue driving.\n\n### StationaryObjectCrossing\nIn this scenario a cyclist is stationary waiting in the middle of the road and\nblocking the way for the ego vehicle. Hence, the ego vehicle has to stop in\nfront of the cyclist.\n\n### DynamicObjectCrossing\nThis is similar to 'StationaryObjectCrossing', but with the difference that the\ncyclist is dynamic. It suddenly drives into the way of the ego vehicle, which\nhas to stop accordingly. After some time, the cyclist will clear the road, such\nthat the ego vehicle can continue driving.\n\n### NoSignalJunctionCrossing\nThis scenario tests negotiation between two vehicles crossing cross each other\nthrough a junction without signal.\nThe ego vehicle is passing through a junction without traffic lights\nAnd encounters another vehicle passing across the junction. The ego vehicle has\nto avoid collision and navigate across the junction to succeed.\n\n### ControlLoss\nIn this scenario control loss of a vehicle is tested due to bad road conditions, etc\nand it checks whether the vehicle is regained its control and corrected its course.\n\n### ManeuverOppositeDirection\nIn this scenario vehicle is passing another vehicle in a rural area, in daylight, under clear\nweather conditions, at a non-junction and encroaches into another\nvehicle traveling in the opposite direction.\n\n### OtherLeadingVehicle\nThe scenario realizes a common driving behavior, in which the user-controlled ego\nvehicle follows a leading car driving down a given road.\nAt some point the leading car has to decelerate. The ego vehicle has to react\naccordingly by changing lane to avoid a collision and follow the leading car in\nother lane. The scenario ends via timeout, or if the ego vehicle drives certain\ndistance.\n\n### SignalizedJunctionRightTurn\nIn this scenario right turn of hero actor without collision at signalized intersection\nis tested. Hero Vehicle is turning right in an urban area, at a signalized intersection and\nturns into the same direction of another vehicle crossing straight initially from\na lateral direction.\n\n### SignalizedJunctionLeftTurn\nIn this scenario hero vehicle is turning left in an urban area,\nat a signalized intersection and cuts across the path of another vehicle\ncoming straight crossing from an opposite direction.\n" }, { "path": "scenario_runner/Docs/metrics_module.md", "content": "# Metrics module\n\n\nThe metrics module relies on the [CARLA recorder](https://carla.readthedocs.io/en/latest/adv_recorder/) to facilitate the easy calculus and monitoring of any kind of parameter. After the scenario is finished, the recorder stores the simulation information. Users can then define their own metrics, and check the corresponding results with no need to play the simulation again and again. \n\nThis section covers an explanation of the different elements that form the module, a step by step tutorial of how to create a new metric, and a reference of the functions that query the recording and define the metrics. \n\n* [__Structure of the module__](#structure-of-the-module) \n* [__How to use the metrics module__](#how-to-use-the-metrics-module) \n\t* [1. Record a scenario](#1.-record-a-scenario) \n\t* [2. Define the metrics](#2.-run-the-metrics-manager) \n\t* [3. Run the metrics manager](#3.-run-the-metrics-manager) \n* [__Recording queries reference__](#recording-queries-reference) \n\t* [Generic actor data](#generic-actor-data) \n\t* [Generic simulation data](#generic-simulation-data) \n\t* [Actor accelerations](#actor-accelerations) \n\t* [Actor angular velocities](#actor-angular-velocities) \n\t* [Actor controls](#actor-controls) \n\t* [Actor transforms](#actor-transforms) \n\t* [Actor velocities](#actor-velocities) \n\t* [Scene lights](#scene-lights) \n\t* [Traffic lights](#traffic-lights) \n\t* [Vehicle lights](#vehicle-lights) \n\n---\n## Structure of the module\n\nSimilarly to the main ScenarioRunner module, the Metrics module is run using a main script, `metrics_manager.py`, and the rest of the information is contained inside a folder structure. Here is a brief introduction to the main script. \n\n* __`metrics_manager.py`__ — The main script of the module. Run this to show the results of the set of metrics. The script has the usual `host` and `port` arguments, and some more to set the metrics and recording to be used. \n\t* `host` *(string)* – IP address where a CARLA simulation is running. Default is `(127.0.0.1)`. \n\t* `port` *(int)* – TCP port where the CARLA simulation is running. Default are `2000` and `2001`. \n\t* `metrics` — Path to the metrics to be used. \n\t* `log` — Path to the `.log` file containing the recording (relative to the environment variable `SCENARIO_RUNNER_ROOT`). \n\t* `criteria` *(optional)* — Path to a JSON file with the criteria of the scenario. \n\nThe rest of the elements that shape the module can be found in the `srunner/metrics` folder. These folder has been divided in three subfolders.\n\n* __`srunner/metrics/data`__ — Stores information about the scenarios. By default, it has six files, which are part of the examples metric. \n\n* __`srunner/metrics/examples`__ — Contains some example metrics, and the metric containing the base class `BaseMetric` to create new metrics. \n\t* `basic_metric.py` – Contains the base class `BaseMetric`. All the metrics are inherited from it. \n\t* `criteria_filter.py` – Returns a JSON with the most important attributes of the criteria. \n\t* `distance_between_vehicles.py` – Returns the distance betwen two vehicles. Useful to show how to query the recording. \n\t* `distance_to_lane_center.py` – Calculates the distance between the vehicle location and the center of the lane. Useful to show how to access the map API information.. \n\n* __`srunner/metrics/tools`__ — Contains two key scripts that allow to query the recording. \n\t* `metrics_parser.py` – Transforms the string provided by the recording to a dictionary. \n\t* `metrics_log.py` – Provides with several functions to query the dictionary created with `metrics_parser.py`. These functions are the easiest way to access information of a scenario. They listed in a [reference](#recording-queries-reference) in the last segment of this page. \n\n---\n## How to use the metrics module\n\n### 1. Record a scenario\n\nThe metrics module needs for a recording of a simulation in order to work. Otherwise, it has no data to make the calculations of the metrics. \n\nUse the `record` argument. Add the path where the information should be saved should be saved. The path must be relative to the environment variable `SCENARIO_RUNNER_ROOT`. \n\n```sh\npython scenario_runner.py --scenario --record \n```\n\nBy recording the scenario two files will be created in the desired path. These files will later be used as the `log` and `criteria` arguments in the `metrics_manager.py`. \n__1. A CARLA recording__ *(`.log`)* — Contains simulation data per frame. To know more about this, read the [recorder docs](https://carla.readthedocs.io/en/latest/adv_recorder/). \n__2. A criteria file__ *(.json)* — The criteria of the scenario parsed as a dictionary, and stored in a JSON file. The keys of the dictionary are the names of the criteria. The values are the attributes of each criteria.\n\n!!! Note\n Only the JSON serializable attributes will be parsed, the rest will be ignored.\n\n By default, both files are named after the scenario that is run. If the scenario is named `example.py`, the files will be `example.log` and `example.json`. \n\n\n### 2. Define the metrics\n\nIt is time to create the desired metrics that will be used for the scenario. The possibilities are endless, but for the sake of this tutorial, the metric described in `srunner/metrics/examples/distance_between_vehicles.py` will be used as an example. Let's dig a little bit into the code itself. \n\n class DistanceBetweenVehicles(BasicMetric):\n \"\"\"\n Metric class DistanceBetweenVehicles\n \"\"\"\n\n def _create_metric(self, town_map, log, criteria):\n \"\"\"\n Implementation of the metric. This is an example to show how to use the recorder,\n accessed via the log.\n \"\"\"\n\n # Get the ID of the two vehicles\n ego_id = log.get_ego_vehicle_id()\n adv_id = log.get_actor_ids_with_role_name(\"scenario\")[0] # Could have also used its type_id\n\n dist_list = []\n frames_list = []\n\n # Get the frames both actors were alive\n start_ego, end_ego = log.get_actor_alive_frames(ego_id)\n start_adv, end_adv = log.get_actor_alive_frames(adv_id)\n start = max(start_ego, start_adv)\n end = min(end_ego, end_adv)\n\n # Get the distance between the two\n for i in range(start, end):\n\n # Get the transforms\n ego_location = log.get_actor_transform(ego_id, i).location\n adv_location = log.get_actor_transform(adv_id, i).location\n\n # Filter some points for a better graph\n if adv_location.z < -10:\n continue\n\n dist_v = ego_location - adv_location\n dist = math.sqrt(dist_v.x * dist_v.x + dist_v.y * dist_v.y + dist_v.z * dist_v.z)\n\n dist_list.append(dist)\n frames_list.append(i)\n\n # Use matplotlib to show the results\n plt.plot(frames_list, dist_list)\n plt.ylabel('Distance [m]')\n plt.xlabel('Frame number')\n plt.title('Distance between the ego vehicle and the adversary over time')\n plt.show()\n\n__2.1. Name the metric__. First of all, as it was previously mentioned, all metrics are childs of the `BasicMetric` class.\n\n```py\n class DistanceBetweenVehicles(BasicMetric):\n```\n\n__2.2. Name the main method__. A metric only requires one method in order to run, `_create_metric()`, which has three arguments. \n\n* __`town_map`__ — Instance of the [carla.Map()](https://carla.readthedocs.io/en/latest/python_api/#carlamap) where the scenario took place in. \n* __`log`__ — Instance to the `MetricsLog` class, with all the functions needed to access the recorder dictionary. \n* __`criteria`__ — A JSOn with the criteria dictionary. The file provided when recording a scenario that is later provided to the `metrics_manager.py`. \n\n```py\n def _create_metric(self, town_map, log, criteria):\n```\n\n__2.3. Implement the metric__. The code will vary depending on the metric itself. \n\nThe example metric `DistanceBetweenVehicles` calculates the distance between the ego vehicle, and the car it follows (adversary). To do so, it needs the `id` of the two vehicles. These can be retrieved from the log using the `get_ego_vehicle_id()` and `get_actor_ids_with_role_name(\"scenario\")[0]` functions. \n\n```py\n # Get the ID of the two vehicles\n ego_id = log.get_ego_vehicle_id()\n adv_id = log.get_actor_ids_with_role_name(\"scenario\")[0] # Could have also used its type_id\n```\n\nThe `id` are used to retrieve the frames where the vehicles were alive using `get_actor_alive_frames(actor_id)`. \n```py\n # Get the frames both actors were alive\n start_ego, end_ego = log.get_actor_alive_frames(ego_id)\n start_adv, end_adv = log.get_actor_alive_frames(adv_id)\n start = max(start_ego, start_adv)\n end = min(end_ego, end_adv)\n```\n\nNow everything is ready to loop through those frames, get their transforms, and calculate the distance. To get the transform, `get_actor_transform(actor_id, frame)` is used. \n\n```py\n dist_list = []\n frames_list = []\n\n...\n\n # Get the distance between the two\n for i in range(start, end):\n\n # Get the transforms\n ego_location = log.get_actor_transform(ego_id, i).location\n adv_location = log.get_actor_transform(adv_id, i).location\n\n # Filter some points for a better graph\n if adv_location.z < -10:\n continue\n\n dist_v = ego_location - adv_location\n dist = math.sqrt(dist_v.x * dist_v.x + dist_v.y * dist_v.y + dist_v.z * dist_v.z)\n\n dist_list.append(dist)\n```\n!!! Note\n The vertical condition of the adversary is to only take into account the adversary when it is driving normally.\n\nLastly, use [matplotlib](https://matplotlib.org/) to define which should be the output of the metric when running `metrics_manager.py`. \n\n```py\n # Use matplotlib to show the results\n plt.plot(frames_list, dist_list)\n plt.ylabel('Distance [m]')\n plt.xlabel('Frame number')\n plt.title('Distance between the ego vehicle and the adversary over time')\n plt.show()\n```\n\n### 3. Run the metrics manager\n\nFinally it is time to used the information retrieve from the scenario, and the metrics that have been defined to return some metrics information. Let's run the module using the example metric that has been used so far, and an example log named after it. \n\n```sh\npython metrics_manager.py --metric srunner/metrics/examples/distance_between_vehicles.py --log srunner/metrics/data/DistanceBetweenVehicles.log\n```\n\n!!! Warning\n A simulation must be running. Otherwise, the module will not be able to acces the map API.\n\n\nThis will create a new window with the results plotted. The script will not finish until the ouput window is closed.\n\n![metrics_plot](img/metrics_example.jpg)\n\n---\n## Recording queries reference\n\nWhen defining a metric, all the information about the scenario is accessed via the `MetricsLog` class (the `log` argument at the `_create_metric()` function). This class is located at `srunner/metrics/tools/metrics_log.py`, and this reference is a following of the functions contained in it. \n\n### Generic actor data\n\n- __get_ego_vehicle_id__(__self__) \nReturns the `id` of the ego vehicle.\n - __Return —__ int\n\n- __get_actor_ids_with_role_name__(__self__, __role_name__) \nReturns a list of actor `id` that match the given `role_name`.\n - __Return —__ list\n - __Parameters__\n - `role_name` (_str_) — `role_name` of the actor.\n\n- __get_actor_ids_with_type_id__(__self__, __type_id__) \nReturns a list of actor `id` that match the given `type_id`, according to fnmatch standard.\n - __Return —__ list\n - __Parameters__\n - `type_id` (_str_) — `type_id` of the actor.\n\n- __get_actor_attributes__(__self__, __actor_id__) \nReturns a dictionary with all the attributes of an actor.\n - __Return —__ dict\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n\n- __get_actor_bounding_box__(__self__, __actor_id__) \nReturns the bounding box of the specified actor.\n - __Return —__ [carla.BoundingBox](https://carla.readthedocs.io/en/latest/python_api/#carlaboundingbox)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n\n- __get_traffic_light_trigger_volume__(__self__, __traffic_light_id__) \nReturns the trigger volume of the specified traffic light.\n - __Return —__ [carla.BoundingBox](https://carla.readthedocs.io/en/latest/python_api/#carlaboundingbox)\n - __Parameters__\n - `traffic_light_id` (_int_) — `id` of the traffic light.\n\n- __get_actor_alive_frames__(__self__, __actor_id__) \nReturns a tuple with the first and last frame an actor was alive. Note that frames start at 1, not 0.\n - __Return —__ tuple\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n\n\n### Generic simulation data\n\n- __get_collisions__(__self__,__actor_id__) \nReturns a list of dictionaries with two keys. `frame` is the frame number of the collision, and `other_id`, a list of the ids the actor collided with at that frame.\n - __Return —__ list\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n\n- __get_total_frame_count__(__self__) \nReturns an int with the total amount of frames the simulation lasted.\n - __Return —__ int\n\n- __get_elapsed_time__(__self__, __frame__) \nReturns a float with the elapsed time of a specific frame.\n - __Return —__ float\n - __Parameters__\n - `frame` (_int_) — Frame number.\n\n- __get_delta_time__(__self__, __frame__) \nReturns an float with the delta time of a specific frame.\n - __Return —__ float\n - __Parameters__\n - `frame` (_int_) — Frame number.\n\n- __get_platform_time__(__self__, __frame__) \nReturns a float with the platform time of a specific frame.\n - __Return —__ float\n - __Parameters__\n - `frame` (_int_) — Frame number.\n\n### Actor accelerations\n\n- __get_actor_acceleration__(__self__, __actor_id__, __frame__) \nReturns the acceleration of the actor at a given frame. Returns None if the actor `id` doesn't exist, the actor has no acceleration, or the actor wasn't alive at that frame.\n - __Return —__ [carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `frame` (_int_) — Frame number.\n\n- __get_all_actor_accelerations__(__self__, __actor_id__, __first_frame__=None, __last_frame__=None) \nReturns a list with all the accelerations of the actor at the frame interval. By default, the frame interval comprises all the recording.\n - __Return —__ list(carla.Vector3D)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `first_frame` (_int_) — Initial frame of the interval. By default, the start of the simulation.\n - `last_frame` (_int_) — Last frame of the interval. By default, the end of the simulation.\n\n- __get_actor_accelerations_at_frame__(__self__, __frame__, __actor_list__=None) \nReturns a dict where the keys are the frame number, and the values are the carla.Vector3D of the actor at the given frame. By default, all actors are considered but if *actor_list* is passed, only actors in the list will be checked.\n - __Return —__ list(carla.Vector3D)\n - __Parameters__\n - `frame` (_int_) — Frame number.\n - `actor_list` (_int_) — List of actor `id`.\n\n### Actor angular velocities\n\n- __get_actor_angular_velocity__(__self__, __actor_id__, __frame__) \nReturns the angular velocity of the actor at a given frame. Returns None if the actor id doesn't exist, the actor has no angular velocity, or the actor wasn't alive at that frame.\n - __Return —__ carla.Vector3D\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `frame` (_int_) — Frame number.\n\n- __get_all_actor_angular_velocities__(__self__, __actor_id__, __first_frame__=None, __last_frame__=None) \nReturns a list with all the angular velocities of the actor at the frame interval. By default, the frame interval comprises all the recording.\n - __Return —__ list(carla.Vector3D)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `first_frame` (_int_) — Initial frame of the interval. By default, the start of the simulation.\n - `last_frame` (_int_) — Last frame of the interval. By default, the end of the simulation.\n\n- __get_actor_angular_velocities_at_frame__(__self__, __frame__, __actor_list__=None) \nReturns a dictionary where the keys are the frame number, and the values are the [carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d) of the actor at the given frame. By default, all actors are considered but if `actor_list` is passed, only actors in the list will be checked.\n - __Return —__ list([carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d))\n - __Parameters__\n - `frame` (_int_) — frame number.\n - `actor_list` (_int_) — List of actor ids.\n\n### Actor controls\n\n- __get_vehicle_control__(__self__, __vehicle_id__, __frame__) \nReturns the control of a vehicle at a given frame. The `manual_gear_shift` attribute will always be False.\n - __Return —__ [carla.VehicleCotnrol](https://carla.readthedocs.io/en/latest/python_api/#carlavehiclecontrol)\n - __Parameters__\n - `vehicle_id` (_int_) — `id` of the vehicle.\n - `frame` (_int_) — Frame number.\n\n- __get_vehicle_physics_control__(__self__, __vehicle_id__, __frame__) \nReturns the physics control of a vehicle at a given frame.\n - __Return —__ [carla.VehiclePhysicsControl](https://carla.readthedocs.io/en/latest/python_api/#carlavehiclephysicscontrol)\n - __Parameters__\n - `vehicle_id` (_int_) — `id` of the vehicle.\n - `frame` (_int_) — Frame number.\n\n- __get_walker_speed__(__self__, __walker_id__, __frame__) \nReturns the speed of a walker at a given frame.\n - __Return —__ [carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d)\n - __Parameters__\n - `walker_id` (_int_) — `id` of the walker.\n - `frame` (_int_) — Frame number.\n\n\n### Actor transforms\n\n- __get_actor_transform__(__self__, __actor_id__, __frame__) \nReturns the transform of the actor at a given frame. Returns None if the actor id doesn't exist, the actor has no transform, or the actor wasn't alive at that frame.\n - __Return —__ [carla.Transform](https://carla.readthedocs.io/en/latest/python_api/#carlatransform)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `frame` (_int_) — Frame number.\n\n- __get_all_actor_transforms__(__self__, __actor_id__, __first_frame__=None, __last_frame__=None) \nReturns a list with all the transforms of the actor at the frame interval. By default, the frame interval comprises all the recording.\n - __Return —__ list([carla.Transform](https://carla.readthedocs.io/en/latest/python_api/#carlatransform))\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `first_frame` (_int_) — Initial frame of the interval. By default, the start of the simulation.\n - `last_frame` (_int_) — Last frame of the interval. By default, the end of the simulation.\n\n- __get_actor_transforms_at_frame__(__self__, __frame__, __actor_list__=None) \nReturns a dictionary where the keys are the frame number, and the values are the [carla.Transform](https://carla.readthedocs.io/en/latest/python_api/#carlatransform) of the actor at the given frame. By default, all actors are considered but if `actor_list` is passed, only actors in the list will be checked.\n - __Return —__ list([carla.Transform](https://carla.readthedocs.io/en/latest/python_api/#carlatransform))\n - __Parameters__\n - `frame` (_int_) — Frame number.\n - `actor_list` (_int_) — List of actor `id`. \n\n### Actor velocities\n\n- __get_actor_velocity__(__self__, __actor_id__, __frame__) \nReturns the velocity of the actor at a given frame. Returns None if the actor id doesn't exist, the actor has no velocity, or the actor wasn't alive at that frame.\n - __Return —__ [carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d)\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `frame` (_int_) — Frame number.\n\n- __get_all_actor_velocities__(__self__, __actor_id__, __first_frame__=None, __last_frame__=None) \nReturns a list with all the velocities of the actor at the frame interval. By default, the frame interval comprises all the recording.\n - __Return —__ list([carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d))\n - __Parameters__\n - `actor_id` (_int_) — `id` of the actor.\n - `first_frame` (_int_) — Initial frame of the interval. By default, the start of the simulation.\n - `last_frame` (_int_) — Last frame of the interval. By default, the end of the simulation.\n\n- __get_actor_velocities_at_frame__(__self__, __frame__, __actor_list__=None) \nReturns a dict where the keys are the frame number, and the values are the carla.Vector3D of the actor at the given frame. By default, all actors are considered but if *actor_list* is passed, only actors in the list will be checked.\n - __Return —__ list([carla.Vector3D](https://carla.readthedocs.io/en/latest/python_api/#carlavector3d)\n - __Parameters__\n - `frame` (_int_) — Frame number.\n - `actor_list` (_int_) — List of actor `id`. \n\n### Scene lights\n\n- __get_scene_light_state__(__self__, __light__, __vehicle_id__, __frame__) \nReturns the state of a scene light for a given frame. The light state group will always be [carla.LightGroup.None](https://carla.readthedocs.io/en/latest/python_api/#carlalightgroup).\n - __Return —__ carla.LightState\n - __Parameters__\n - `light_id` (_int_) — `id` of the scene light.\n - `frame` (_int_) — Frame number.\n\n\n### Traffic lights\n\n- __get_traffic_light_state__(__self__, __traffic_light_id__, __frame__) \nReturns the state of a traffic light at a given frame.\n - __Return —__ [carla.TrafficLightState](https://carla.readthedocs.io/en/latest/python_api/#carlatrafficlightstate).\n - __Parameters__\n - `traffic_light_id` (_int_) — `id` of the traffic light.\n - `frame` (_int_) — Frame number.\n\n- __is_traffic_light_frozen__(__self__, __traffic_light_id__, __frame__) \nReturns whether or not a traffic light is frozen at a given frame.\n - __Return —__ bool\n - __Parameters__\n - `traffic_light_id` (_int_) — `id` of the traffic light.\n - `frame` (_int_) — Frame number.\n\n- __get_traffic_light_elapsed_time__(__self__, __traffic_light_id__, __frame__) \nReturns the elapsed time of a traffic light at a given frame.\n - __Return —__ float\n - __Parameters__\n - `traffic_light_id` (_int_) — `id` of the traffic light.\n - `frame` (_int_) — Frame number.\n\n- __get_traffic_light_state_time__(__self__, __traffic_light_id__, __state__, __frame__) \nReturns the maximum time of a specific state of a traffic light at a given frame.\n - __Return —__ float\n - __Parameters__\n - `traffic_light_id` (_int_) — `id` of the traffic light.\n - `state` ([carla.TrafficLightState](https://carla.readthedocs.io/en/latest/python_api/#carlatrafficlightstate)) — `id` of the actor.\n - `frame` (_int_) — Frame number.\n\n### Vehicle lights\n\n- __get_vehicle_lights__(__self__, __vehicle_id__, __frame__) \nReturns a list with the active lights of a specific vehicle at a given frame.\n - __Return —__ list([carla.VehicleLightState](https://carla.readthedocs.io/en/latest/python_api/#carlavehiclelightstate))\n - __Parameters__\n - `vehicle_id` (_int_) — `id` of the vehicle.\n - `frame` (_int_) — Frame number.\n\n- __is_vehicle_light_active__(__self__, __light__, __vehicle_id__, __frame__) \nChecks whether or not a given vehicle light is active for a vehicle at a specific frame.\n - __Return —__ bool\n - __Parameters__\n - `light` ([carla.VehicleLightState](https://carla.readthedocs.io/en/latest/python_api/#carlavehiclelightstate)) — The light state to be compared with the vehicle.\n - `vehicle_id` (_int_) — `id` of the vehicle.\n - `frame` (_int_) — Frame number.\n" }, { "path": "scenario_runner/Docs/openscenario_support.md", "content": "## OpenSCENARIO Support\r\n\r\nThe scenario_runner provides support for the [OpenSCENARIO](http://www.openscenario.org/) 1.0 standard.\r\nThe current implementation covers initial support for maneuver Actions, Conditions, Stories and the Storyboard.\r\nIf you would like to use evaluation criteria for a scenario to evaluate pass/fail results, these can be implemented\r\nas StopTriggers (see below). However, not all features for these elements are yet available. If in doubt, please see the\r\nmodule documentation in srunner/tools/openscenario_parser.py\r\n\r\nAn example for a supported scenario based on OpenSCENARIO is available [here](https://github.com/carla-simulator/scenario_runner/blob/master/srunner/examples/FollowLeadingVehicle.xosc)\r\n\r\nIn addition, it is recommended to take a look into the official documentation available [here](https://releases.asam.net/OpenSCENARIO/1.0.0/Model-Documentation/index.html) and [here](https://releases.asam.net/OpenSCENARIO/1.0.0/ASAM_OpenSCENARIO_BS-1-2_User-Guide_V1-0-0.html#_foreword).\r\n\r\n### Migrating OpenSCENARIO 0.9.x to 1.0\r\nThe easiest way to convert old OpenSCENARIO samples to the official standard 1.0 is to use _xsltproc_ and the migration scheme located in the openscenario folder.\r\nExample:\r\n\r\n```bash\r\nxsltproc -o newScenario.xosc migration0_9_1to1_0.xslt oldScenario.xosc\r\n```\r\n\r\n\r\n### Level of support\r\nIn the following the OpenSCENARIO attributes are listed with their current support status.\r\n\r\n#### General OpenSCENARIO setup\r\n\r\nThis covers all part that are defined outside the OpenSCENARIO Storyboard\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
AttributeSupportNotes
\r\nFileHeader✅Use \"CARLA:\" at the beginning of the description to use the CARLA coordinate system.
CatalogLocations
ControllerCatalog		✅While the catalog is supported, the reference/usage may not work.
CatalogLocations
EnvironmentCatalog		✅
CatalogLocations
ManeuverCatalog		✅
CatalogLocations
MiscObjectCatalog		✅
CatalogLocations
PedestrianCatalog		✅
CatalogLocations
RouteCatalog		✅While the catalog is supported, the reference/usage may not work.
CatalogLocations
TrajectoryCatalog		✅While the catalog is supported, the reference/usage may not work.
CatalogLocations
VehicleCatalog		✅
ParameterDeclarations✅
RoadNetwork
LogicFile		✅The CARLA level can be used directly (e.g. LogicFile=Town01). Also any OpenDRIVE path can be provided.
RoadNetwork
SceneGraphFile		❌The provided information is not used.
RoadNetwork
TafficSignals		❌The provided information is not used.
Entities
EntitySelection		❌The provided information is not used.
EntitiesScenarioObject
CatalogReference		✅The provided information is not used.
EntitiesScenarioObject
MiscObject		✅The name should match a CARLA vehicle model, otherwise a default vehicle based on the vehicleCategory is used. BoundingBox entries are ignored.
EntitiesScenarioObject
ObjectController		❌The provided information is not used.
EntitiesScenarioObject
Pedestrian		✅The name should match a CARLA vehicle model, otherwise a default vehicle based on the vehicleCategory is used. BoundingBox entries are ignored.
EntitiesScenarioObject
Vehicle		✅The name should match a CARLA vehicle model, otherwise a default vehicle based on the vehicleCategory is used. The color can be set via properties ('Property name=\"color\" value=\"0,0,255\"'). Axles, Performance, BoundingBox entries are ignored.
\r\n
\r\n\r\n#### OpenSCENARIO Storyboard\r\n\r\n##### OpenSCENARIO Actions\r\n\r\nThe OpenSCENARIO Actions can be used for two different purposes. First, Actions can be used to\r\ndefine the initial behavior of something, e.g. a traffic participant. Therefore, Actions can be\r\nused within the OpenSCENARIO Init. In addition, Actions are also used within the OpenSCENARIO\r\nstory. In the following, the support status for both application areas is listed. If an action\r\ncontains of submodules, which are not listed, the support status applies to all submodules.\r\n\r\n###### GlobalAction\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
GlobalActionInit
support		Story
support		Notes
EntityAction❌❌
EnvironmentAction✅❌
ParameterAction❌❌
InfrastructureActionTrafficSignalAction
TrafficAction		❌❌
InfrastructureActionTrafficSignalAction
TrafficSignalControllerAction		❌❌
InfrastructureActionTrafficSignalAction
TrafficSignalStateAction		❌✅As traffic signals in CARLA towns have no unique ID, they have to be set by providing their position (Example: TrafficSignalStateAction name=\"pos=x,y\" state=\"green\"). The id can also be used for none CARLA town (Example: TrafficSignalStateAction name=\"id=n\" state=\"green\")
\r\n
\r\n\r\n###### UserDefinedAction\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
UserDefinedActionInit
support		Story
support		Notes
CustomCommandAction❌✅This action is currently used to trigger the execution of an additional script. Example: type=\"python /path/to/script args\".
\r\n
\r\n\r\n###### PrivateAction\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
PrivateActionInit
support		Story
support		Notes
ActivateControllerAction❌✅Can be used to activate/deactive the CARLA autopilot.
ControllerAction✅✅AssignControllerAction is supported, but a Python module has to be provided for the controller implementation, and in OverrideControllerValueAction all values need to be False.
LateralAction
LaneChangeAction		❌✅Currently only lane change by one lane to the left or right is supported (RelativeTargetLane).
LateralAction
LaneOffsetAction		❌❌
LateralAction
LateralDistanceAction		❌❌
LongitudinalAction
LongitudinalDistanceAction		❌❌
LongitudinalAction
SpeedAction		✅✅
SynchronizeAction❌❌
TeleportAction✅✅
VisibilityAction❌❌
RoutingAction
AcquirePositionAction		❌✅
RoutingAction
AssignRouteAction		❌✅
RoutingAction
FollowTrajectoryAction		❌❌
\r\n
\r\n\r\n\r\n\r\n##### OpenSCENARIO Conditions\r\n\r\nConditions in OpenSCENARIO can be defined either as ByEntityCondition or as ByValueCondition.\r\nBoth can be used for StartTrigger and StopTrigger conditions.\r\nThe following two tables list the support status for each.\r\n\r\n###### ByEntityCondition\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
EntityConditionSupportNotes
AccelerationCondition✅
CollisionCondition✅
DistanceCondition✅*freespace* attribute is still not supported
EndOfRoadCondition✅
OffroadCondition✅
ReachPositionCondition✅
RelativeDistanceCondition✅*freespace* attribute is still not supported
RelativeSpeedCondition✅
SpeedCondition✅
StandStillCondition✅
TimeHeadwayCondition✅*freespace* attribute is still not supported
TimeToCollisionCondition✅*freespace* attribute is still not supported
TraveledDistanceCondition✅
\r\n
\r\n\r\n###### ByValueCondition\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
ValueConditionSupportNotes
ParameterCondition✅The level of support depends on the parameter. It is recommended to use other conditions if possible. Please also consider the note below.
SimulationTimeCondition✅
StoryboardElementStateCondition✅startTransition, stopTransition, endTransition and completeState are currently supported.
TimeOfDayCondition✅
TrafficSignalCondition✅As traffic signals in CARLA towns have no unique ID, they have to be set by providing their position (Example: TrafficSignalCondition name=\"pos=x,y\" state=\"green\"). The id can also be used for none CARLA town (Example: TrafficSignalCondition name=\"id=n\" state=\"green\")
TrafficSignalControllerCondition❌
UserDefinedValueCondition❌
\r\n
\r\n\r\n!!! Note\r\n In the OpenSCENARIO 1.0 standard, a definition of test / evaluation criteria is not\r\n defined. For this purpose, you can re-use StopTrigger conditions with CARLA. The following\r\n StopTrigger conditions for evaluation criteria are supported through ParameterConditions by\r\n providing the criteria name for the condition:\r\n\r\n * criteria_RunningStopTest\r\n * criteria_RunningRedLightTest\r\n * criteria_WrongLaneTest\r\n * criteria_OnSideWalkTest\r\n * criteria_KeepLaneTest\r\n * criteria_CollisionTest\r\n * criteria_DrivenDistanceTest\r\n\r\n##### OpenSCENARIO Positions\r\n\r\nThere are several ways of defining positions in OpenSCENARIO. In the following we list the\r\ncurrent support status for each definition format.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
PositionSupportNotes
LanePosition✅
RelativeLanePosition✅
RelativeObjectPosition✅
RelativeRoadPosition❌
RelativeWorldPosition✅
RoadPosition❌
RoutePosition❌
WorldPosition✅
\r\n
" }, { "path": "scenario_runner/Docs/requirements.txt", "content": "mkdocs >= 1.0\nmarkdown-include\nmkdocs-redirects\n" }, { "path": "scenario_runner/Docs/ros_agent.md", "content": "# ROS-based Challenge Agent\n\nInterfacing CARLA from ROS is normally done via [CARLA ROS Bridge](https://github.com/carla-simulator/ros-bridge).\nIn Challenge Mode this bridging functionality is provided by a RosAgent. It uses the same topics and message-types for the sensors but does not publish tf-transformations.\n \n# Requirements\n\n* `roscore` is expected to be running in the docker container. Please adapt your entrypoint.\n\n## Setup\n\nTo enable your stack within challenge mode, the following steps need to be taken:\n\n1. Define Sensor Setup\n2. Define Startup\n\n### Define Sensor Setup\n\nDerive from RosAgent and implement the sensors() method.\n\n from srunner.autoagents.ros_agent import RosAgent\n\n class MyRosAgent(RosAgent):\n\n def sensors(self):\n return [ ]\n\nAs an example for the sensor definition, see [HumanAgent.py](../srunner/autoagents/human_agent.py).\n\n\n### Define Startup\n\nThe startup of the stack is done within the shell script `$TEAM_CODE_ROOT/start.sh`.\nTherefore the environment variable `TEAM_CODE_ROOT` must be set.\n\nRosAgent takes care of executing and monitoring. The script shall remain running as long as the stack is active.\n\nExample for start.sh\n\n #!/bin/bash -e\n roslaunch $TEAM_CODE_ROOT/challenge.launch\n\n\n## Testing\n\nIn general, the challenge execution is headless. For diagnosis you're still able to use ros-tools like rviz or rqt. Additionally you\ncan use [carla_manual_control](https://github.com/carla-simulator/ros-bridge/tree/master/carla_manual_control) from the carla_ros_bridge for visualization (and also controlling the vehicle).\n\n" }, { "path": "scenario_runner/Jenkinsfile", "content": "#!/usr/bin/env groovy\n\n\nString CARLA_HOST \nString CARLA_RELEASE\nString TEST_HOST\nString COMMIT\nString ECR_REPOSITORY = \"456841689987.dkr.ecr.eu-west-3.amazonaws.com/scenario_runner\"\nboolean CARLA_RUNNING = false\nboolean CONCURRENCY = true\n\n// V3 - include detection of concurrent builds\n\npipeline\n{\n agent none\n\n options\n {\n buildDiscarder(logRotator(numToKeepStr: '3', artifactNumToKeepStr: '3'))\n skipDefaultCheckout()\n }\n\n stages\n {\n stage('setup')\n {\n agent { label \"master\" }\n steps\n {\n checkout scm\n script\n {\n jenkinsLib = load(\"/home/jenkins/scenario_runner.groovy\")\n TEST_HOST = jenkinsLib.getUbuntuTestNodeHost()\n CARLA_HOST= sh(\n script: \"cat ./CARLA_VER | grep HOST | sed 's/HOST\\\\s*=\\\\s*//g'\",\n returnStdout: true).trim()\n CARLA_RELEASE = sh(\n script: \"cat ./CARLA_VER | grep RELEASE | sed 's/RELEASE\\\\s*=\\\\s*//g'\",\n returnStdout: true).trim()\n COMMIT = sh(returnStdout: true, script: \"git log -n 1 --pretty=format:'%h'\").trim()\n }\n println \"using CARLA version ${CARLA_RELEASE} from ${TEST_HOST}\"\n }\n }\n stage('get concurrency status')\n {\n options\n {\n lock resource: 'ubuntu_gpu', skipIfLocked: true\n }\n agent { label \"master\" }\n steps\n {\n script\n {\n CONCURRENCY = false\n println \"no concurrent builds detected.\"\n }\n }\n }\n stage('act on concurrency')\n {\n agent { label \"master\" }\n steps\n {\n script\n {\n if ( CONCURRENCY == true )\n {\n println \"concurrent builds detected, prebuilding SR image.\"\n stage('prebuild SR docker image')\n {\n //checkout scm\n sh \"docker build -t jenkins/scenario_runner:${COMMIT} .\"\n sh \"docker tag jenkins/scenario_runner:${COMMIT} ${ECR_REPOSITORY}:${COMMIT}\"\n sh '$(aws ecr get-login | sed \\'s/ -e none//g\\' )' \n sh \"docker push ${ECR_REPOSITORY}\"\n\t\t\t sh \"docker image rmi -f \\\"\\$(docker images -q ${ECR_REPOSITORY}:${COMMIT})\\\"\"\n }\n }\n }\n }\n }\n stage('lock ubuntu_gpu instance')\n {\n options\n {\n lock resource: \"ubuntu_gpu\"\n }\n stages\n {\n stage('start server')\n {\n agent { label \"master\" }\n steps\n {\n script\n {\n jenkinsLib = load(\"/home/jenkins/scenario_runner.groovy\")\n jenkinsLib.StartUbuntuTestNode()\n }\n }\n }\n stage('deploy')\n {\n parallel\n {\n stage('build SR docker image')\n {\n agent { label \"master\" }\n steps\n {\n script\n {\n if ( CONCURRENCY == false )\n {\n //checkout scm\n sh \"docker build -t jenkins/scenario_runner:${COMMIT} .\"\n sh \"docker tag jenkins/scenario_runner:${COMMIT} ${ECR_REPOSITORY}:${COMMIT}\"\n sh '$(aws ecr get-login | sed \\'s/ -e none//g\\' )'\n sh \"docker push ${ECR_REPOSITORY}\"\n\t\t\t\t\t\tsh \"docker image rmi -f \\\"\\$(docker images -q ${ECR_REPOSITORY}:${COMMIT})\\\"\"\n }\n else\n {\n println \"SR docker image already built due concurrency\"\n }\n }\n }\n }\n stage('deploy CARLA')\n {\n stages\n {\n stage('install CARLA')\n {\n agent { label \"secondary && ubuntu && gpu && sr\" }\n steps\n {\n println \"using CARLA version ${CARLA_RELEASE}\"\n sh \"wget -qO- ${CARLA_HOST}/${CARLA_RELEASE}.tar.gz | tar -xzv -C .\"\n }\n }\n }\n }\n }\n }\n stage('run test')\n {\n agent { label \"secondary && ubuntu && gpu && sr\" }\n steps\n {\n sh 'DISPLAY= ./CarlaUE4.sh -opengl -nosound > CarlaUE4.log&'\n sleep 10\n script\n {\n sh '$(aws ecr get-login | sed \\'s/ -e none//g\\' )' \n sh \"docker pull ${ECR_REPOSITORY}:${COMMIT}\"\n sh \"docker container run --rm --network host -e LANG=C.UTF-8 \\\"${ECR_REPOSITORY}:${COMMIT}\\\" -c \\\"python3 scenario_runner.py --scenario FollowLeadingVehicle_1 --debug --output --reloadWorld \\\"\"\n deleteDir()\n }\n }\n }\n }\n post\n {\n always\n {\n node('master')\n {\n script \n {\n jenkinsLib = load(\"/home/jenkins/scenario_runner.groovy\")\n jenkinsLib.StopUbuntuTestNode()\n echo 'test node stopped'\n sh 'docker system prune --volumes -f'\n }\n deleteDir()\n }\n }\n }\n }\n }\n}\n" }, { "path": "scenario_runner/LICENSE", "content": "MIT License\n\nCopyright (c) 2018 CARLA\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "scenario_runner/README.md", "content": "## Note by ThinkTwice\nWe have changed/added some log related functions of the srunner for easier debug. For example, each simulated route would have a index in its folder name. For all infractions, the location and timestep would be recorded in the json file.\n\nOriginal README.md\n========================\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n![GitHub tag (latest SemVer)](https://img.shields.io/github/tag/carla-simulator/scenario_runner.svg)\n[![Build Status](https://travis-ci.com/carla-simulator/scenario_runner.svg?branch=master)](https://travis-ci.com/carla/scenario_runner)\n\nScenarioRunner for CARLA\n========================\nThis repository contains traffic scenario definition and an execution engine\nfor CARLA. It also allows the execution of a simulation of the CARLA Challenge.\nYou can use this system to prepare your agent for the CARLA Challenge.\n\nScenarios can be defined through a Python interface, and with the newest version\nthe scenario_runner also the upcoming [OpenSCENARIO](http://www.openscenario.org/) standard is supported.\n\n[![Scenario_Runner for CARLA](Docs/img/scenario_runner_video.png)](https://youtu.be/ChmF8IFagpo?t=68)\n\nGetting the ScenarioRunner\n---------------------------\n\nUse `git clone` or download the project from this page. Note that the master\nbranch contains the latest fixes and features, and may be required to use the latest features from CARLA.\n\nIt is important to also consider the release version that has to match the CARLA version.\n\n* [Version 0.9.10](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.10) and the 0.9.10 Branch: Compatible with [CARLA 0.9.10](https://github.com/carla-simulator/carla/releases/tag/0.9.10)\n* [Version 0.9.9](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.9) and the 0.9.9 Branch: Compatible with [CARLA 0.9.9](https://github.com/carla-simulator/carla/releases/tag/0.9.9). Use the 0.9.9 branch, if you use CARLA 0.9.9.4.\n* [Version 0.9.8](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.8) and the 0.9.8 Branch: Compatible with [CARLA 0.9.8](https://github.com/carla-simulator/carla/releases/tag/0.9.8)\n* [Version 0.9.7](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.7) and the 0.9.7 Branch: Compatible with [CARLA 0.9.7](https://github.com/carla-simulator/carla/releases/tag/0.9.7) but not with the later release patch versions.\n* [Version 0.9.6](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.6) and the 0.9.6 Branch: Compatible with [CARLA 0.9.6](https://github.com/carla-simulator/carla/releases/tag/0.9.6)\n* [Version 0.9.5](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.5) and [Version 0.9.5.1](https://github.com/carla-simulator/scenario_runner/releases/tag/v0.9.5.1): Compatible with [CARLA 0.9.5](https://github.com/carla-simulator/carla/releases/tag/0.9.5)\n* [Version 0.9.2](https://github.com/carla-simulator/scenario_runner/releases/tag/0.9.2): Compatible with [CARLA 0.9.2](https://github.com/carla-simulator/carla/releases/tag/0.9.2)\n\nTo use a particular version you can either download the corresponding tarball or simply checkout the version tag associated to the release (e.g. git checkout v0.9.5)\n\nCurrently no build is required, as all code is in Python.\n\nUsing the ScenarioRunner\n------------------------\n\nPlease take a look at our [Getting started](Docs/getting_scenariorunner.md)\ndocumentation.\n\nChallenge Evaluation\n---------------------\n\nYou can evaluate your own agents using a reproduction\nof the CARLA Challenge by following [this tutorial](Docs/challenge_evaluation.md)\n\nContributing\n------------\n\nPlease take a look at our [Contribution guidelines](https://carla.readthedocs.io/en/latest/#contributing).\n\nFAQ\n------\n\nIf you run into problems, check our\n[FAQ](http://carla.readthedocs.io/en/latest/faq/).\n\nLicense\n-------\n\nScenarioRunner specific code is distributed under MIT License.\n" }, { "path": "scenario_runner/manual_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n# Allows controlling a vehicle with a keyboard. For a simpler and more\n# documented example, please take a look at tutorial.py.\n\n\"\"\"\nWelcome to CARLA manual control.\n\nUse ARROWS or WASD keys for control.\n\n W : throttle\n S : brake\n A/D : steer left/right\n Q : toggle reverse\n Space : hand-brake\n P : toggle autopilot\n M : toggle manual transmission\n ,/. : gear up/down\n\n L : toggle next light type\n SHIFT + L : toggle high beam\n Z/X : toggle right/left blinker\n I : toggle interior light\n\n TAB : change sensor position\n ` or N : next sensor\n [1-9] : change to sensor [1-9]\n G : toggle radar visualization\n C : change weather (Shift+C reverse)\n\n R : toggle recording images to disk\n\n CTRL + R : toggle recording of simulation (replacing any previous)\n CTRL + P : start replaying last recorded simulation\n CTRL + + : increments the start time of the replay by 1 second (+SHIFT = 10 seconds)\n CTRL + - : decrements the start time of the replay by 1 second (+SHIFT = 10 seconds)\n\n F1 : toggle HUD\n H/? : toggle help\n ESC : quit\n\"\"\"\n\nfrom __future__ import print_function\n\n# ==============================================================================\n# -- imports -------------------------------------------------------------------\n# ==============================================================================\n\n\nimport carla\n\nfrom examples.manual_control import (World, HUD, KeyboardControl, CameraManager,\n CollisionSensor, LaneInvasionSensor, GnssSensor, IMUSensor)\n\nimport os\nimport argparse\nimport logging\nimport time\nimport pygame\n\n# ==============================================================================\n# -- Global functions ----------------------------------------------------------\n# ==============================================================================\n\n\ndef get_actor_display_name(actor, truncate=250):\n name = ' '.join(actor.type_id.replace('_', '.').title().split('.')[1:])\n return (name[:truncate - 1] + u'\\u2026') if len(name) > truncate else name\n\n\n# ==============================================================================\n# -- World ---------------------------------------------------------------------\n# ==============================================================================\n\nclass WorldSR(World):\n\n restarted = False\n\n def restart(self):\n\n if self.restarted:\n return\n self.restarted = True\n\n self.player_max_speed = 1.589\n self.player_max_speed_fast = 3.713\n\n # Keep same camera config if the camera manager exists.\n cam_index = self.camera_manager.index if self.camera_manager is not None else 0\n cam_pos_index = self.camera_manager.transform_index if self.camera_manager is not None else 0\n\n # Get the ego vehicle\n while self.player is None:\n print(\"Waiting for the ego vehicle...\")\n time.sleep(1)\n possible_vehicles = self.world.get_actors().filter('vehicle.*')\n for vehicle in possible_vehicles:\n if vehicle.attributes['role_name'] == \"hero\":\n print(\"Ego vehicle found\")\n self.player = vehicle\n break\n \n self.player_name = self.player.type_id\n\n # Set up the sensors.\n self.collision_sensor = CollisionSensor(self.player, self.hud)\n self.lane_invasion_sensor = LaneInvasionSensor(self.player, self.hud)\n self.gnss_sensor = GnssSensor(self.player)\n self.imu_sensor = IMUSensor(self.player)\n self.camera_manager = CameraManager(self.player, self.hud, self._gamma)\n self.camera_manager.transform_index = cam_pos_index\n self.camera_manager.set_sensor(cam_index, notify=False)\n actor_type = get_actor_display_name(self.player)\n self.hud.notification(actor_type)\n\n def tick(self, clock):\n if len(self.world.get_actors().filter(self.player_name)) < 1:\n return False\n\n self.hud.tick(self, clock)\n return True\n\n# ==============================================================================\n# -- game_loop() ---------------------------------------------------------------\n# ==============================================================================\n\ndef game_loop(args):\n pygame.init()\n pygame.font.init()\n world = None\n\n try:\n client = carla.Client(args.host, args.port)\n client.set_timeout(2.0)\n\n display = pygame.display.set_mode(\n (args.width, args.height),\n pygame.HWSURFACE | pygame.DOUBLEBUF)\n\n hud = HUD(args.width, args.height)\n world = WorldSR(client.get_world(), hud, args)\n controller = KeyboardControl(world, args.autopilot)\n\n clock = pygame.time.Clock()\n while True:\n clock.tick_busy_loop(60)\n if controller.parse_events(client, world, clock):\n return\n if not world.tick(clock):\n return\n world.render(display)\n pygame.display.flip()\n\n finally:\n\n if (world and world.recording_enabled):\n client.stop_recorder()\n\n if world is not None:\n world.destroy()\n\n pygame.quit()\n\n\n# ==============================================================================\n# -- main() --------------------------------------------------------------------\n# ==============================================================================\n\n\ndef main():\n argparser = argparse.ArgumentParser(\n description='CARLA Manual Control Client')\n argparser.add_argument(\n '-v', '--verbose',\n action='store_true',\n dest='debug',\n help='print debug information')\n argparser.add_argument(\n '--host',\n metavar='H',\n default='127.0.0.1',\n help='IP of the host server (default: 127.0.0.1)')\n argparser.add_argument(\n '-p', '--port',\n metavar='P',\n default=2000,\n type=int,\n help='TCP port to listen to (default: 2000)')\n argparser.add_argument(\n '-a', '--autopilot',\n action='store_true',\n help='enable autopilot')\n argparser.add_argument(\n '--res',\n metavar='WIDTHxHEIGHT',\n default='1280x720',\n help='window resolution (default: 1280x720)')\n args = argparser.parse_args()\n\n args.rolename = 'hero' # Needed for CARLA version\n args.filter = \"vehicle.*\" # Needed for CARLA version\n args.gamma = 2.2 # Needed for CARLA version\n args.width, args.height = [int(x) for x in args.res.split('x')]\n\n log_level = logging.DEBUG if args.debug else logging.INFO\n logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)\n\n logging.info('listening to server %s:%s', args.host, args.port)\n\n print(__doc__)\n\n try:\n\n game_loop(args)\n\n except KeyboardInterrupt:\n print('\\nCancelled by user. Bye!')\n except Exception as error:\n logging.exception(error)\n\n\nif __name__ == '__main__':\n\n main()\n" }, { "path": "scenario_runner/metrics_manager.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n# Allows the execution of user-implemented metrics\n\n\"\"\"\nWelcome to the ScenarioRunner's metric module\n\nThis is the main script to be executed when running a metric.\nIt is responsible of parsing all the information and executing\nthe metric specified by the user.\n\"\"\"\n\nimport os\nimport sys\nimport importlib\nimport inspect\nimport json\nimport argparse\nfrom argparse import RawTextHelpFormatter\n\nimport carla\nfrom srunner.metrics.tools.metrics_log import MetricsLog\n\n\nclass MetricsManager(object):\n \"\"\"\n Main class of the metrics module. Handles the parsing and execution of\n the metrics.\n \"\"\"\n\n def __init__(self, args):\n \"\"\"\n Initialization of the metrics manager. This creates the client, needed to parse\n the information from the recorder, extract the metrics class, and runs it\n \"\"\"\n self._args = args\n\n # Parse the arguments\n recorder_str = self._get_recorder(self._args.log)\n criteria_dict = self._get_criteria(self._args.criteria)\n\n # Get the correct world and load it\n map_name = self._get_recorder_map(recorder_str)\n world = self._client.load_world(map_name)\n town_map = world.get_map()\n\n # Instanciate the MetricsLog, used to querry the needed information\n log = MetricsLog(recorder_str)\n\n # Read and run the metric class\n metric_class = self._get_metric_class(self._args.metric)\n metric_class(town_map, log, criteria_dict)\n\n def _get_recorder(self, log):\n \"\"\"\n Parses the log argument into readable information\n \"\"\"\n\n # Get the log information.\n self._client = carla.Client(self._args.host, self._args.port)\n recorder_file = \"{}/{}\".format(os.getenv('SCENARIO_RUNNER_ROOT', \"./\"), log)\n\n # Check that the file is correct\n if recorder_file[-4:] != '.log':\n print(\"ERROR: The log argument has to point to a .log file\")\n sys.exit(-1)\n if not os.path.exists(recorder_file):\n print(\"ERROR: The specified log file does not exist\")\n sys.exit(-1)\n\n recorder_str = self._client.show_recorder_file_info(recorder_file, True)\n\n return recorder_str\n\n def _get_criteria(self, criteria_file):\n \"\"\"\n Parses the criteria argument into a dictionary\n \"\"\"\n if criteria_file:\n with open(criteria_file) as fd:\n criteria_dict = json.load(fd)\n else:\n criteria_dict = None\n\n return criteria_dict\n\n def _get_metric_class(self, metric_file):\n \"\"\"\n Function to extract the metrics class from the path given by the metrics\n argument. Returns the first class found that is a child of BasicMetric\n\n Args:\n metric_file (str): path to the metric's file.\n \"\"\"\n # Get their module\n module_name = os.path.basename(metric_file).split('.')[0]\n sys.path.insert(0, os.path.dirname(metric_file))\n metric_module = importlib.import_module(module_name)\n\n # And their members of type class\n for member in inspect.getmembers(metric_module, inspect.isclass):\n # Get the first one with parent BasicMetrics\n member_parent = member[1].__bases__[0]\n if 'BasicMetric' in str(member_parent):\n return member[1]\n\n print(\"No child class of BasicMetric was found ... Exiting\")\n sys.exit(-1)\n\n def _get_recorder_map(self, recorder_str):\n \"\"\"\n Returns the name of the map the simulation took place in\n \"\"\"\n\n header = recorder_str.split(\"\\n\")\n sim_map = header[1][5:]\n\n return sim_map\n\n\ndef main():\n \"\"\"\n main function\n \"\"\"\n\n # pylint: disable=line-too-long\n description = (\"Scenario Runner's metrics module. Evaluate the execution of a specific scenario by developing your own metric.\\n\")\n\n parser = argparse.ArgumentParser(description=description,\n formatter_class=RawTextHelpFormatter)\n parser.add_argument('--host', default='127.0.0.1',\n help='IP of the host server (default: localhost)')\n parser.add_argument('--port', '-p', default=2000,\n help='TCP port to listen to (default: 2000)')\n parser.add_argument('--log', required=True,\n help='Path to the CARLA recorder .log file (relative to SCENARIO_RUNNER_ROOT).\\nThis file is created by the record functionality at ScenarioRunner')\n parser.add_argument('--metric', required=True,\n help='Path to the .py file defining the used metric.\\nSome examples at srunner/metrics')\n parser.add_argument('--criteria', default=\"\",\n help='Path to the .json file with the criteria information.\\nThis file is created by the record functionality at ScenarioRunner')\n # pylint: enable=line-too-long\n\n args = parser.parse_args()\n\n MetricsManager(args)\n\nif __name__ == \"__main__\":\n sys.exit(main())\n" }, { "path": "scenario_runner/mkdocs.yml", "content": "site_name: CARLA ScenarioRunner\nrepo_url: https://github.com/carla-simulator/scenario_runner\ndocs_dir: Docs\nedit_uri: 'edit/master/Docs/'\ntheme: readthedocs\nextra_css: [extra.css]\n\nnav:\n- Home: index.md\n- Quick start:\n - Get ScenarioRunner: getting_scenariorunner.md\n - First steps: getting_started.md\n - Create a new scenario: creating_new_scenario.md\n - Metrics module: metrics_module.md\n - FAQ: FAQ.md\n - Release Notes: CHANGELOG.md\n- References:\n - List of scenarios: list_of_scenarios.md\n - OpenSCENARIO support: openscenario_support.md\n- Contributing:\n - Code of conduct: CODE_OF_CONDUCT.md\n - Coding standard: coding_standard.md\n - Contribution guidelines: CONTRIBUTING.md\n\nmarkdown_extensions:\n - admonition\n - markdown_include.include:\n base_path: '.'\n\n" }, { "path": "scenario_runner/no_rendering_mode.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n# Allows visualising a 2D map generated by vehicles.\n\n\"\"\"\nWelcome to CARLA No-Rendering Mode Visualizer\n\n TAB : toggle hero mode\n Mouse Wheel : zoom in / zoom out\n Mouse Drag : move map (map mode only)\n\n W : throttle\n S : brake\n AD : steer\n Q : toggle reverse\n Space : hand-brake\n P : toggle autopilot\n M : toggle manual transmission\n ,/. : gear up/down\n\n F1 : toggle HUD\n I : toggle actor ids\n H/? : toggle help\n ESC : quit\n\"\"\"\n\n# ==============================================================================\n# -- find carla module ---------------------------------------------------------\n# ==============================================================================\n\nimport glob\nimport os\nimport sys\n\ntry:\n sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (\n sys.version_info.major,\n sys.version_info.minor,\n 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])\nexcept IndexError:\n pass\n\n# ==============================================================================\n# -- imports -------------------------------------------------------------------\n# ==============================================================================\n\nimport carla\nfrom carla import TrafficLightState as tls\n\nimport argparse\nimport logging\nimport datetime\nimport weakref\nimport math\nimport random\n\ntry:\n import pygame\n from pygame.locals import KMOD_CTRL\n from pygame.locals import KMOD_SHIFT\n from pygame.locals import K_COMMA\n from pygame.locals import K_DOWN\n from pygame.locals import K_ESCAPE\n from pygame.locals import K_F1\n from pygame.locals import K_LEFT\n from pygame.locals import K_PERIOD\n from pygame.locals import K_RIGHT\n from pygame.locals import K_SLASH\n from pygame.locals import K_SPACE\n from pygame.locals import K_TAB\n from pygame.locals import K_UP\n from pygame.locals import K_a\n from pygame.locals import K_d\n from pygame.locals import K_h\n from pygame.locals import K_i\n from pygame.locals import K_m\n from pygame.locals import K_p\n from pygame.locals import K_q\n from pygame.locals import K_s\n from pygame.locals import K_w\nexcept ImportError:\n raise RuntimeError('cannot import pygame, make sure pygame package is installed')\n\n# ==============================================================================\n# -- Constants -----------------------------------------------------------------\n# ==============================================================================\n\n# Colors\n\n# We will use the color palette used in Tango Desktop Project (Each color is indexed depending on brightness level)\n# See: https://en.wikipedia.org/wiki/Tango_Desktop_Project\n\nCOLOR_BUTTER_0 = pygame.Color(252, 233, 79)\nCOLOR_BUTTER_1 = pygame.Color(237, 212, 0)\nCOLOR_BUTTER_2 = pygame.Color(196, 160, 0)\n\nCOLOR_ORANGE_0 = pygame.Color(252, 175, 62)\nCOLOR_ORANGE_1 = pygame.Color(245, 121, 0)\nCOLOR_ORANGE_2 = pygame.Color(209, 92, 0)\n\nCOLOR_CHOCOLATE_0 = pygame.Color(233, 185, 110)\nCOLOR_CHOCOLATE_1 = pygame.Color(193, 125, 17)\nCOLOR_CHOCOLATE_2 = pygame.Color(143, 89, 2)\n\nCOLOR_CHAMELEON_0 = pygame.Color(138, 226, 52)\nCOLOR_CHAMELEON_1 = pygame.Color(115, 210, 22)\nCOLOR_CHAMELEON_2 = pygame.Color(78, 154, 6)\n\nCOLOR_SKY_BLUE_0 = pygame.Color(114, 159, 207)\nCOLOR_SKY_BLUE_1 = pygame.Color(52, 101, 164)\nCOLOR_SKY_BLUE_2 = pygame.Color(32, 74, 135)\n\nCOLOR_PLUM_0 = pygame.Color(173, 127, 168)\nCOLOR_PLUM_1 = pygame.Color(117, 80, 123)\nCOLOR_PLUM_2 = pygame.Color(92, 53, 102)\n\nCOLOR_SCARLET_RED_0 = pygame.Color(239, 41, 41)\nCOLOR_SCARLET_RED_1 = pygame.Color(204, 0, 0)\nCOLOR_SCARLET_RED_2 = pygame.Color(164, 0, 0)\n\nCOLOR_ALUMINIUM_0 = pygame.Color(238, 238, 236)\nCOLOR_ALUMINIUM_1 = pygame.Color(211, 215, 207)\nCOLOR_ALUMINIUM_2 = pygame.Color(186, 189, 182)\nCOLOR_ALUMINIUM_3 = pygame.Color(136, 138, 133)\nCOLOR_ALUMINIUM_4 = pygame.Color(85, 87, 83)\nCOLOR_ALUMINIUM_4_5 = pygame.Color(66, 62, 64)\nCOLOR_ALUMINIUM_5 = pygame.Color(46, 52, 54)\n\n\nCOLOR_WHITE = pygame.Color(255, 255, 255)\nCOLOR_BLACK = pygame.Color(0, 0, 0)\n\n\n# Module Defines\nMODULE_WORLD = 'WORLD'\nMODULE_HUD = 'HUD'\nMODULE_INPUT = 'INPUT'\n\nPIXELS_PER_METER = 12\n\nMAP_DEFAULT_SCALE = 0.1\nHERO_DEFAULT_SCALE = 1.0\n\nPIXELS_AHEAD_VEHICLE = 150\n\n# ==============================================================================\n# -- Util -----------------------------------------------------------\n# ==============================================================================\n\n\ndef get_actor_display_name(actor, truncate=250):\n name = ' '.join(actor.type_id.replace('_', '.').title().split('.')[1:])\n return (name[:truncate - 1] + u'\\u2026') if len(name) > truncate else name\n\n\nclass Util(object):\n\n @staticmethod\n def blits(destination_surface, source_surfaces, rect=None, blend_mode=0):\n for surface in source_surfaces:\n destination_surface.blit(surface[0], surface[1], rect, blend_mode)\n\n @staticmethod\n def length(v):\n return math.sqrt(v.x**2 + v.y**2 + v.z**2)\n\n @staticmethod\n def get_bounding_box(actor):\n bb = actor.trigger_volume.extent\n corners = [carla.Location(x=-bb.x, y=-bb.y),\n carla.Location(x=bb.x, y=-bb.y),\n carla.Location(x=bb.x, y=bb.y),\n carla.Location(x=-bb.x, y=bb.y),\n carla.Location(x=-bb.x, y=-bb.y)]\n corners = [x + actor.trigger_volume.location for x in corners]\n t = actor.get_transform()\n t.transform(corners)\n return corners\n\n# ==============================================================================\n# -- ModuleManager -------------------------------------------------------------\n# ==============================================================================\n\n\nclass ModuleManager(object):\n def __init__(self):\n self.modules = []\n\n def register_module(self, module):\n self.modules.append(module)\n\n def clear_modules(self):\n del self.modules[:]\n\n def tick(self, clock):\n # Update all the modules\n for module in self.modules:\n module.tick(clock)\n\n def render(self, display):\n display.fill(COLOR_ALUMINIUM_4)\n for module in self.modules:\n module.render(display)\n\n def get_module(self, name):\n for module in self.modules:\n if module.name == name:\n return module\n\n def start_modules(self):\n for module in self.modules:\n module.start()\n\n\n# ==============================================================================\n# -- FadingText ----------------------------------------------------------------\n# ==============================================================================\n\n\nclass FadingText(object):\n def __init__(self, font, dim, pos):\n self.font = font\n self.dim = dim\n self.pos = pos\n self.seconds_left = 0\n self.surface = pygame.Surface(self.dim)\n\n def set_text(self, text, color=COLOR_WHITE, seconds=2.0):\n text_texture = self.font.render(text, True, color)\n self.surface = pygame.Surface(self.dim)\n self.seconds_left = seconds\n self.surface.fill(COLOR_BLACK)\n self.surface.blit(text_texture, (10, 11))\n\n def tick(self, clock):\n delta_seconds = 1e-3 * clock.get_time()\n self.seconds_left = max(0.0, self.seconds_left - delta_seconds)\n self.surface.set_alpha(500.0 * self.seconds_left)\n\n def render(self, display):\n display.blit(self.surface, self.pos)\n\n\n# ==============================================================================\n# -- HelpText ------------------------------------------------------------------\n# ==============================================================================\n\n\nclass HelpText(object):\n def __init__(self, font, width, height):\n lines = __doc__.split('\\n')\n self.font = font\n self.dim = (680, len(lines) * 22 + 12)\n self.pos = (0.5 * width - 0.5 * self.dim[0], 0.5 * height - 0.5 * self.dim[1])\n self.seconds_left = 0\n self.surface = pygame.Surface(self.dim)\n self.surface.fill(COLOR_BLACK)\n for n, line in enumerate(lines):\n text_texture = self.font.render(line, True, COLOR_WHITE)\n self.surface.blit(text_texture, (22, n * 22))\n self._render = False\n self.surface.set_alpha(220)\n\n def toggle(self):\n self._render = not self._render\n\n def render(self, display):\n if self._render:\n display.blit(self.surface, self.pos)\n\n\n# ==============================================================================\n# -- ModuleHUD -----------------------------------------------------------------\n# ==============================================================================\n\n\nclass ModuleHUD (object):\n\n def __init__(self, name, width, height):\n self.name = name\n self.dim = (width, height)\n self._init_hud_params()\n self._init_data_params()\n\n def start(self):\n pass\n\n def _init_hud_params(self):\n fonts = [x for x in pygame.font.get_fonts() if 'mono' in x]\n default_font = 'ubuntumono'\n mono = default_font if default_font in fonts else fonts[0]\n mono = pygame.font.match_font(mono)\n self._font_mono = pygame.font.Font(mono, 14)\n self._header_font = pygame.font.SysFont('Arial', 14, True)\n self.help = HelpText(pygame.font.Font(mono, 24), *self.dim)\n self._notifications = FadingText(\n pygame.font.Font(pygame.font.get_default_font(), 20),\n (self.dim[0], 40), (0, self.dim[1] - 40))\n\n def _init_data_params(self):\n self.show_info = True\n self.show_actor_ids = False\n self._info_text = {}\n\n def notification(self, text, seconds=2.0):\n self._notifications.set_text(text, seconds=seconds)\n\n def tick(self, clock):\n self._notifications.tick(clock)\n\n def add_info(self, module_name, info):\n self._info_text[module_name] = info\n\n def render_vehicles_ids(self, vehicle_id_surface, list_actors, world_to_pixel, hero_actor, hero_transform):\n vehicle_id_surface.fill(COLOR_BLACK)\n if self.show_actor_ids:\n vehicle_id_surface.set_alpha(150)\n for actor in list_actors:\n x, y = world_to_pixel(actor[1].location)\n\n angle = 0\n if hero_actor is not None:\n angle = -hero_transform.rotation.yaw - 90\n\n color = COLOR_SKY_BLUE_0\n if int(actor[0].attributes['number_of_wheels']) == 2:\n color = COLOR_CHOCOLATE_0\n if actor[0].attributes['role_name'] == 'hero':\n color = COLOR_CHAMELEON_0\n\n font_surface = self._header_font.render(str(actor[0].id), True, color)\n rotated_font_surface = pygame.transform.rotate(font_surface, angle)\n rect = rotated_font_surface.get_rect(center=(x, y))\n vehicle_id_surface.blit(rotated_font_surface, rect)\n\n return vehicle_id_surface\n\n def render(self, display):\n if self.show_info:\n info_surface = pygame.Surface((240, self.dim[1]))\n info_surface.set_alpha(100)\n display.blit(info_surface, (0, 0))\n v_offset = 4\n bar_h_offset = 100\n bar_width = 106\n i = 0\n for module_name, module_info in self._info_text.items():\n if not module_info:\n continue\n surface = self._header_font.render(module_name, True, COLOR_ALUMINIUM_0).convert_alpha()\n display.blit(surface, (8 + bar_width / 2, 18 * i + v_offset))\n v_offset += 12\n i += 1\n for item in module_info:\n if v_offset + 18 > self.dim[1]:\n break\n if isinstance(item, list):\n if len(item) > 1:\n points = [(x + 8, v_offset + 8 + (1.0 - y) * 30) for x, y in enumerate(item)]\n pygame.draw.lines(display, (255, 136, 0), False, points, 2)\n item = None\n elif isinstance(item, tuple):\n if isinstance(item[1], bool):\n rect = pygame.Rect((bar_h_offset, v_offset + 8), (6, 6))\n pygame.draw.rect(display, COLOR_ALUMINIUM_0, rect, 0 if item[1] else 1)\n else:\n rect_border = pygame.Rect((bar_h_offset, v_offset + 8), (bar_width, 6))\n pygame.draw.rect(display, COLOR_ALUMINIUM_0, rect_border, 1)\n f = (item[1] - item[2]) / (item[3] - item[2])\n if item[2] < 0.0:\n rect = pygame.Rect((bar_h_offset + f * (bar_width - 6), v_offset + 8), (6, 6))\n else:\n rect = pygame.Rect((bar_h_offset, v_offset + 8), (f * bar_width, 6))\n pygame.draw.rect(display, COLOR_ALUMINIUM_0, rect)\n item = item[0]\n if item: # At this point has to be a str.\n surface = self._font_mono.render(item, True, COLOR_ALUMINIUM_0).convert_alpha()\n display.blit(surface, (8, 18 * i + v_offset))\n v_offset += 18\n v_offset += 24\n self._notifications.render(display)\n self.help.render(display)\n\n\n# ==============================================================================\n# -- TrafficLightSurfaces ------------------------------------------------------\n# ==============================================================================\n\n\nclass TrafficLightSurfaces(object):\n \"\"\"Holds the surfaces (scaled and rotated) for painting traffic lights\"\"\"\n\n def __init__(self):\n def make_surface(tl):\n w = 40\n surface = pygame.Surface((w, 3 * w), pygame.SRCALPHA)\n surface.fill(COLOR_ALUMINIUM_5 if tl != 'h' else COLOR_ORANGE_2)\n if tl != 'h':\n hw = int(w / 2)\n off = COLOR_ALUMINIUM_4\n red = COLOR_SCARLET_RED_0\n yellow = COLOR_BUTTER_0\n green = COLOR_CHAMELEON_0\n pygame.draw.circle(surface, red if tl == tls.Red else off, (hw, hw), int(0.4 * w))\n pygame.draw.circle(surface, yellow if tl == tls.Yellow else off, (hw, w + hw), int(0.4 * w))\n pygame.draw.circle(surface, green if tl == tls.Green else off, (hw, 2 * w + hw), int(0.4 * w))\n return pygame.transform.smoothscale(surface, (15, 45) if tl != 'h' else (19, 49))\n self._original_surfaces = {\n 'h': make_surface('h'),\n tls.Red: make_surface(tls.Red),\n tls.Yellow: make_surface(tls.Yellow),\n tls.Green: make_surface(tls.Green),\n tls.Off: make_surface(tls.Off),\n tls.Unknown: make_surface(tls.Unknown)\n }\n self.surfaces = dict(self._original_surfaces)\n\n def rotozoom(self, angle, scale):\n for key, surface in self._original_surfaces.items():\n self.surfaces[key] = pygame.transform.rotozoom(surface, angle, scale)\n\n\n# ==============================================================================\n# -- World ---------------------------------------------------------------------\n# ==============================================================================\n\n\nclass MapImage(object):\n def __init__(self, carla_world, carla_map, pixels_per_meter, show_triggers, show_connections, show_spawn_points):\n self._pixels_per_meter = pixels_per_meter\n self.scale = 1.0\n self.show_triggers = show_triggers\n self.show_connections = show_connections\n self.show_spawn_points = show_spawn_points\n\n waypoints = carla_map.generate_waypoints(2)\n margin = 50\n max_x = max(waypoints, key=lambda x: x.transform.location.x).transform.location.x + margin\n max_y = max(waypoints, key=lambda x: x.transform.location.y).transform.location.y + margin\n min_x = min(waypoints, key=lambda x: x.transform.location.x).transform.location.x - margin\n min_y = min(waypoints, key=lambda x: x.transform.location.y).transform.location.y - margin\n\n self.width = max(max_x - min_x, max_y - min_y)\n self._world_offset = (min_x, min_y)\n\n width_in_pixels = int(self._pixels_per_meter * self.width)\n\n self.big_map_surface = pygame.Surface((width_in_pixels, width_in_pixels)).convert()\n self.draw_road_map(self.big_map_surface, carla_world, carla_map, self.world_to_pixel, self.world_to_pixel_width)\n self.surface = self.big_map_surface\n\n def draw_road_map(self, map_surface, carla_world, carla_map, world_to_pixel, world_to_pixel_width):\n map_surface.fill(COLOR_ALUMINIUM_4)\n precision = 0.05\n\n def lane_marking_color_to_tango(lane_marking_color):\n tango_color = COLOR_BLACK\n\n if lane_marking_color == carla.LaneMarkingColor.White:\n tango_color = COLOR_ALUMINIUM_2\n\n elif lane_marking_color == carla.LaneMarkingColor.Blue:\n tango_color = COLOR_SKY_BLUE_0\n\n elif lane_marking_color == carla.LaneMarkingColor.Green:\n tango_color = COLOR_CHAMELEON_0\n\n elif lane_marking_color == carla.LaneMarkingColor.Red:\n tango_color = COLOR_SCARLET_RED_0\n\n elif lane_marking_color == carla.LaneMarkingColor.Yellow:\n tango_color = COLOR_ORANGE_0\n\n return tango_color\n\n def draw_solid_line(surface, color, closed, points, width):\n if len(points) >= 2:\n pygame.draw.lines(surface, color, closed, points, width)\n\n def draw_broken_line(surface, color, closed, points, width):\n broken_lines = [x for n, x in enumerate(zip(*(iter(points),) * 20)) if n % 3 == 0]\n for line in broken_lines:\n pygame.draw.lines(surface, color, closed, line, width)\n\n def get_lane_markings(lane_marking_type, lane_marking_color, waypoints, sign):\n margin = 0.20\n if lane_marking_type == carla.LaneMarkingType.Broken or (lane_marking_type == carla.LaneMarkingType.Solid):\n marking_1 = [world_to_pixel(lateral_shift(w.transform, sign * w.lane_width * 0.5)) for w in waypoints]\n return [(lane_marking_type, lane_marking_color, marking_1)]\n elif lane_marking_type == carla.LaneMarkingType.SolidBroken or lane_marking_type == carla.LaneMarkingType.BrokenSolid:\n marking_1 = [world_to_pixel(lateral_shift(w.transform, sign * w.lane_width * 0.5)) for w in waypoints]\n marking_2 = [world_to_pixel(lateral_shift(w.transform,\n sign * (w.lane_width * 0.5 + margin * 2))) for w in waypoints]\n return [(carla.LaneMarkingType.Solid, lane_marking_color, marking_1),\n (carla.LaneMarkingType.Broken, lane_marking_color, marking_2)]\n elif lane_marking_type == carla.LaneMarkingType.BrokenBroken:\n marking = [world_to_pixel(lateral_shift(w.transform,\n sign * (w.lane_width * 0.5 - margin))) for w in waypoints]\n return [(carla.LaneMarkingType.Broken, lane_marking_color, marking)]\n elif lane_marking_type == carla.LaneMarkingType.SolidSolid:\n marking = [world_to_pixel(lateral_shift(w.transform,\n sign * ((w.lane_width * 0.5) - margin))) for w in waypoints]\n return [(carla.LaneMarkingType.Solid, lane_marking_color, marking)]\n\n return [(carla.LaneMarkingType.NONE, carla.LaneMarkingColor.Other, [])]\n\n def draw_lane_marking(surface, waypoints, is_left):\n sign = -1 if is_left else 1\n lane_marking = None\n\n marking_type = carla.LaneMarkingType.NONE\n previous_marking_type = carla.LaneMarkingType.NONE\n\n marking_color = carla.LaneMarkingColor.Other\n previous_marking_color = carla.LaneMarkingColor.Other\n\n waypoints_list = []\n temp_waypoints = []\n current_lane_marking = carla.LaneMarkingType.NONE\n for sample in waypoints:\n lane_marking = sample.left_lane_marking if sign < 0 else sample.right_lane_marking\n\n if lane_marking is None:\n continue\n\n marking_type = lane_marking.type\n marking_color = lane_marking.color\n\n if current_lane_marking != marking_type:\n markings = get_lane_markings(\n previous_marking_type,\n lane_marking_color_to_tango(previous_marking_color),\n temp_waypoints,\n sign)\n current_lane_marking = marking_type\n\n for marking in markings:\n waypoints_list.append(marking)\n\n temp_waypoints = temp_waypoints[-1:]\n\n else:\n temp_waypoints.append((sample))\n previous_marking_type = marking_type\n previous_marking_color = marking_color\n\n # Add last marking\n last_markings = get_lane_markings(\n previous_marking_type,\n lane_marking_color_to_tango(previous_marking_color),\n temp_waypoints,\n sign)\n for marking in last_markings:\n waypoints_list.append(marking)\n\n for markings in waypoints_list:\n if markings[0] == carla.LaneMarkingType.Solid:\n draw_solid_line(surface, markings[1], False, markings[2], 2)\n elif markings[0] == carla.LaneMarkingType.Broken:\n draw_broken_line(surface, markings[1], False, markings[2], 2)\n\n def draw_arrow(surface, transform, color=COLOR_ALUMINIUM_2):\n transform.rotation.yaw += 180\n forward = transform.get_forward_vector()\n transform.rotation.yaw += 90\n right_dir = transform.get_forward_vector()\n end = transform.location\n start = end - 2.0 * forward\n right = start + 0.8 * forward + 0.4 * right_dir\n left = start + 0.8 * forward - 0.4 * right_dir\n pygame.draw.lines(\n surface, color, False, [\n world_to_pixel(x) for x in [\n start, end]], 4)\n pygame.draw.lines(\n surface, color, False, [\n world_to_pixel(x) for x in [\n left, start, right]], 4)\n\n def draw_traffic_signs(surface, font_surface, actor, color=COLOR_ALUMINIUM_2, trigger_color=COLOR_PLUM_0):\n transform = actor.get_transform()\n waypoint = carla_map.get_waypoint(transform.location)\n\n angle = -waypoint.transform.rotation.yaw - 90.0\n font_surface = pygame.transform.rotate(font_surface, angle)\n pixel_pos = world_to_pixel(waypoint.transform.location)\n offset = font_surface.get_rect(center=(pixel_pos[0], pixel_pos[1]))\n surface.blit(font_surface, offset)\n\n # Draw line in front of stop\n forward_vector = carla.Location(waypoint.transform.get_forward_vector())\n left_vector = carla.Location(-forward_vector.y, forward_vector.x,\n forward_vector.z) * waypoint.lane_width / 2 * 0.7\n\n line = [(waypoint.transform.location + (forward_vector * 1.5) + (left_vector)),\n (waypoint.transform.location + (forward_vector * 1.5) - (left_vector))]\n\n line_pixel = [world_to_pixel(p) for p in line]\n pygame.draw.lines(surface, color, True, line_pixel, 2)\n\n # draw bounding box\n if self.show_triggers:\n corners = Util.get_bounding_box(actor)\n corners = [world_to_pixel(p) for p in corners]\n pygame.draw.lines(surface, trigger_color, True, corners, 2)\n\n def lateral_shift(transform, shift):\n transform.rotation.yaw += 90\n return transform.location + shift * transform.get_forward_vector()\n\n def draw_topology(carla_topology, index):\n topology = [x[index] for x in carla_topology]\n topology = sorted(topology, key=lambda w: w.transform.location.z)\n for waypoint in topology:\n # if waypoint.road_id == 150 or waypoint.road_id == 16:\n waypoints = [waypoint]\n\n nxt = waypoint.next(precision)\n if len(nxt) > 0:\n nxt = nxt[0]\n while nxt.road_id == waypoint.road_id:\n waypoints.append(nxt)\n nxt = nxt.next(precision)\n if len(nxt) > 0:\n nxt = nxt[0]\n else:\n break\n\n # Draw Road\n road_left_side = [lateral_shift(w.transform, -w.lane_width * 0.5) for w in waypoints]\n road_right_side = [lateral_shift(w.transform, w.lane_width * 0.5) for w in waypoints]\n\n polygon = road_left_side + [x for x in reversed(road_right_side)]\n polygon = [world_to_pixel(x) for x in polygon]\n\n if len(polygon) > 2:\n pygame.draw.polygon(map_surface, COLOR_ALUMINIUM_5, polygon, 5)\n pygame.draw.polygon(map_surface, COLOR_ALUMINIUM_5, polygon)\n\n # Draw Shoulders and Parkings\n PARKING_COLOR = COLOR_ALUMINIUM_4_5\n SHOULDER_COLOR = COLOR_ALUMINIUM_5\n\n final_color = SHOULDER_COLOR\n\n # Draw Right\n shoulder = []\n for w in waypoints:\n r = w.get_right_lane()\n if r is not None and (\n r.lane_type == carla.LaneType.Shoulder or r.lane_type == carla.LaneType.Parking):\n if r.lane_type == carla.LaneType.Parking:\n final_color = PARKING_COLOR\n shoulder.append(r)\n\n shoulder_left_side = [lateral_shift(w.transform, -w.lane_width * 0.5) for w in shoulder]\n shoulder_right_side = [lateral_shift(w.transform, w.lane_width * 0.5) for w in shoulder]\n\n polygon = shoulder_left_side + [x for x in reversed(shoulder_right_side)]\n polygon = [world_to_pixel(x) for x in polygon]\n\n if len(polygon) > 2:\n pygame.draw.polygon(map_surface, final_color, polygon, 5)\n pygame.draw.polygon(map_surface, final_color, polygon)\n\n draw_lane_marking(\n map_surface,\n shoulder,\n False)\n\n # Draw Left\n shoulder = []\n for w in waypoints:\n r = w.get_left_lane()\n if r is not None and (\n r.lane_type == carla.LaneType.Shoulder or r.lane_type == carla.LaneType.Parking):\n if r.lane_type == carla.LaneType.Parking:\n final_color = PARKING_COLOR\n shoulder.append(r)\n\n shoulder_left_side = [lateral_shift(w.transform, -w.lane_width * 0.5) for w in shoulder]\n shoulder_right_side = [lateral_shift(w.transform, w.lane_width * 0.5) for w in shoulder]\n\n polygon = shoulder_left_side + [x for x in reversed(shoulder_right_side)]\n polygon = [world_to_pixel(x) for x in polygon]\n\n if len(polygon) > 2:\n pygame.draw.polygon(map_surface, final_color, polygon, 5)\n pygame.draw.polygon(map_surface, final_color, polygon)\n\n draw_lane_marking(\n map_surface,\n shoulder,\n True)\n\n # Draw Lane Markings and Arrows\n if not waypoint.is_intersection:\n draw_lane_marking(\n map_surface,\n waypoints,\n True)\n draw_lane_marking(\n map_surface,\n waypoints,\n False)\n for n, wp in enumerate(waypoints):\n if ((n + 1) % 400) == 0:\n draw_arrow(map_surface, wp.transform)\n\n topology = carla_map.get_topology()\n draw_topology(topology, 0)\n draw_topology(topology, 1)\n\n if self.show_spawn_points:\n for sp in carla_map.get_spawn_points():\n draw_arrow(map_surface, sp, color=COLOR_CHOCOLATE_0)\n\n if self.show_connections:\n dist = 1.5\n to_pixel = lambda wp: world_to_pixel(wp.transform.location)\n for wp in carla_map.generate_waypoints(dist):\n col = (0, 255, 255) if wp.is_intersection else (0, 255, 0)\n for nxt in wp.next(dist):\n pygame.draw.line(map_surface, col, to_pixel(wp), to_pixel(nxt), 2)\n if wp.lane_change & carla.LaneChange.Right:\n r = wp.get_right_lane()\n if r and r.lane_type == carla.LaneType.Driving:\n pygame.draw.line(map_surface, col, to_pixel(wp), to_pixel(r), 2)\n if wp.lane_change & carla.LaneChange.Left:\n l = wp.get_left_lane()\n if l and l.lane_type == carla.LaneType.Driving:\n pygame.draw.line(map_surface, col, to_pixel(wp), to_pixel(l), 2)\n\n actors = carla_world.get_actors()\n\n # Draw Traffic Signs\n font_size = world_to_pixel_width(1)\n font = pygame.font.SysFont('Arial', font_size, True)\n\n stops = [actor for actor in actors if 'stop' in actor.type_id]\n yields = [actor for actor in actors if 'yield' in actor.type_id]\n\n stop_font_surface = font.render(\"STOP\", False, COLOR_ALUMINIUM_2)\n stop_font_surface = pygame.transform.scale(\n stop_font_surface, (stop_font_surface.get_width(), stop_font_surface.get_height() * 2))\n\n yield_font_surface = font.render(\"YIELD\", False, COLOR_ALUMINIUM_2)\n yield_font_surface = pygame.transform.scale(\n yield_font_surface, (yield_font_surface.get_width(), yield_font_surface.get_height() * 2))\n\n for ts_stop in stops:\n draw_traffic_signs(map_surface, stop_font_surface, ts_stop, trigger_color=COLOR_SCARLET_RED_1)\n\n for ts_yield in yields:\n draw_traffic_signs(map_surface, yield_font_surface, ts_yield, trigger_color=COLOR_ORANGE_1)\n\n def world_to_pixel(self, location, offset=(0, 0)):\n x = self.scale * self._pixels_per_meter * (location.x - self._world_offset[0])\n y = self.scale * self._pixels_per_meter * (location.y - self._world_offset[1])\n return [int(x - offset[0]), int(y - offset[1])]\n\n def world_to_pixel_width(self, width):\n return int(self.scale * self._pixels_per_meter * width)\n\n def scale_map(self, scale):\n if scale != self.scale:\n self.scale = scale\n width = int(self.big_map_surface.get_width() * self.scale)\n self.surface = pygame.transform.smoothscale(self.big_map_surface, (width, width))\n\n\nclass ModuleWorld(object):\n def __init__(self, name, args, timeout):\n self.client = None\n self.name = name\n self.args = args\n self.timeout = timeout\n self.server_fps = 0.0\n self.simulation_time = 0\n self.server_clock = pygame.time.Clock()\n\n # World data\n self.world = None\n self.town_map = None\n self.actors_with_transforms = []\n # Store necessary modules\n self.module_hud = None\n self.module_input = None\n\n self.surface_size = [0, 0]\n self.prev_scaled_size = 0\n self.scaled_size = 0\n # Hero actor\n self.hero_actor = None\n self.spawned_hero = None\n self.hero_transform = None\n\n self.scale_offset = [0, 0]\n\n self.vehicle_id_surface = None\n self.result_surface = None\n\n self.traffic_light_surfaces = TrafficLightSurfaces()\n self.affected_traffic_light = None\n\n # Map info\n self.map_image = None\n self.border_round_surface = None\n self.original_surface_size = None\n self.hero_surface = None\n self.actors_surface = None\n\n def _get_data_from_carla(self):\n try:\n self.client = carla.Client(self.args.host, self.args.port)\n self.client.set_timeout(self.timeout)\n\n if self.args.map is None:\n world = self.client.get_world()\n else:\n world = self.client.load_world(self.args.map)\n\n town_map = world.get_map()\n return (world, town_map)\n\n except RuntimeError as ex:\n logging.error(ex)\n exit_game()\n\n def start(self):\n self.world, self.town_map = self._get_data_from_carla()\n\n # Create Surfaces\n self.map_image = MapImage(\n carla_world=self.world,\n carla_map=self.town_map,\n pixels_per_meter=PIXELS_PER_METER,\n show_triggers=self.args.show_triggers,\n show_connections=self.args.show_connections,\n show_spawn_points=self.args.show_spawn_points)\n\n # Store necessary modules\n self.module_hud = module_manager.get_module(MODULE_HUD)\n self.module_input = module_manager.get_module(MODULE_INPUT)\n\n self.original_surface_size = min(self.module_hud.dim[0], self.module_hud.dim[1])\n self.surface_size = self.map_image.big_map_surface.get_width()\n\n self.scaled_size = int(self.surface_size)\n self.prev_scaled_size = int(self.surface_size)\n\n # Render Actors\n self.actors_surface = pygame.Surface((self.map_image.surface.get_width(), self.map_image.surface.get_height()))\n self.actors_surface.set_colorkey(COLOR_BLACK)\n\n self.vehicle_id_surface = pygame.Surface((self.surface_size, self.surface_size)).convert()\n self.vehicle_id_surface.set_colorkey(COLOR_BLACK)\n\n self.border_round_surface = pygame.Surface(self.module_hud.dim, pygame.SRCALPHA).convert()\n self.border_round_surface.set_colorkey(COLOR_WHITE)\n self.border_round_surface.fill(COLOR_BLACK)\n\n center_offset = (int(self.module_hud.dim[0] / 2), int(self.module_hud.dim[1] / 2))\n pygame.draw.circle(self.border_round_surface, COLOR_ALUMINIUM_1, center_offset, int(self.module_hud.dim[1] / 2))\n pygame.draw.circle(self.border_round_surface, COLOR_WHITE, center_offset, int((self.module_hud.dim[1] - 8) / 2))\n\n scaled_original_size = self.original_surface_size * (1.0 / 0.9)\n self.hero_surface = pygame.Surface((scaled_original_size, scaled_original_size)).convert()\n\n self.result_surface = pygame.Surface((self.surface_size, self.surface_size)).convert()\n self.result_surface.set_colorkey(COLOR_BLACK)\n\n # Start hero mode by default\n self.select_hero_actor()\n self.hero_actor.set_autopilot(False)\n self.module_input.wheel_offset = HERO_DEFAULT_SCALE\n self.module_input.control = carla.VehicleControl()\n\n weak_self = weakref.ref(self)\n self.world.on_tick(lambda timestamp: ModuleWorld.on_world_tick(weak_self, timestamp))\n\n def select_hero_actor(self):\n hero_vehicles = [actor for actor in self.world.get_actors(\n ) if 'vehicle' in actor.type_id and actor.attributes['role_name'] == 'hero']\n if len(hero_vehicles) > 0:\n self.hero_actor = random.choice(hero_vehicles)\n self.hero_transform = self.hero_actor.get_transform()\n else:\n self._spawn_hero()\n\n def _spawn_hero(self):\n # Get a random blueprint.\n blueprint = random.choice(self.world.get_blueprint_library().filter(self.args.filter))\n blueprint.set_attribute('role_name', 'hero')\n if blueprint.has_attribute('color'):\n color = random.choice(blueprint.get_attribute('color').recommended_values)\n blueprint.set_attribute('color', color)\n # Spawn the player.\n while self.hero_actor is None:\n spawn_points = self.world.get_map().get_spawn_points()\n spawn_point = random.choice(spawn_points) if spawn_points else carla.Transform()\n self.hero_actor = self.world.try_spawn_actor(blueprint, spawn_point)\n self.hero_transform = self.hero_actor.get_transform()\n\n # Save it in order to destroy it when closing program\n self.spawned_hero = self.hero_actor\n\n def tick(self, clock):\n actors = self.world.get_actors()\n self.actors_with_transforms = [(actor, actor.get_transform()) for actor in actors]\n if self.hero_actor is not None:\n self.hero_transform = self.hero_actor.get_transform()\n self.update_hud_info(clock)\n\n def update_hud_info(self, clock):\n hero_mode_text = []\n if self.hero_actor is not None:\n hero_speed = self.hero_actor.get_velocity()\n hero_speed_text = 3.6 * math.sqrt(hero_speed.x ** 2 + hero_speed.y ** 2 + hero_speed.z ** 2)\n\n affected_traffic_light_text = 'None'\n if self.affected_traffic_light is not None:\n state = self.affected_traffic_light.state\n if state == carla.TrafficLightState.Green:\n affected_traffic_light_text = 'GREEN'\n elif state == carla.TrafficLightState.Yellow:\n affected_traffic_light_text = 'YELLOW'\n else:\n affected_traffic_light_text = 'RED'\n\n affected_speed_limit_text = self.hero_actor.get_speed_limit()\n\n hero_mode_text = [\n 'Hero Mode: ON',\n 'Hero ID: %7d' % self.hero_actor.id,\n 'Hero Vehicle: %14s' % get_actor_display_name(self.hero_actor, truncate=14),\n 'Hero Speed: %3d km/h' % hero_speed_text,\n 'Hero Affected by:',\n ' Traffic Light: %12s' % affected_traffic_light_text,\n ' Speed Limit: %3d km/h' % affected_speed_limit_text\n ]\n else:\n hero_mode_text = ['Hero Mode: OFF']\n\n self.server_fps = self.server_clock.get_fps()\n self.server_fps = 'inf' if self.server_fps == float('inf') else round(self.server_fps)\n module_info_text = [\n 'Server: % 16s FPS' % self.server_fps,\n 'Client: % 16s FPS' % round(clock.get_fps()),\n 'Simulation Time: % 12s' % datetime.timedelta(seconds=int(self.simulation_time)),\n 'Map Name: %10s' % self.town_map.name,\n ]\n\n module_info_text = module_info_text\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.add_info(self.name, module_info_text)\n module_hud.add_info('HERO', hero_mode_text)\n\n @staticmethod\n def on_world_tick(weak_self, timestamp):\n self = weak_self()\n if not self:\n return\n\n self.server_clock.tick()\n self.server_fps = self.server_clock.get_fps()\n self.simulation_time = timestamp.elapsed_seconds\n\n def _split_actors(self):\n vehicles = []\n traffic_lights = []\n speed_limits = []\n walkers = []\n\n for actor_with_transform in self.actors_with_transforms:\n actor = actor_with_transform[0]\n if 'vehicle' in actor.type_id:\n vehicles.append(actor_with_transform)\n elif 'traffic_light' in actor.type_id:\n traffic_lights.append(actor_with_transform)\n elif 'speed_limit' in actor.type_id:\n speed_limits.append(actor_with_transform)\n elif 'walker' in actor.type_id:\n walkers.append(actor_with_transform)\n\n info_text = []\n if self.hero_actor is not None and len(vehicles) > 1:\n location = self.hero_transform.location\n vehicle_list = [x[0] for x in vehicles if x[0].id != self.hero_actor.id]\n\n def distance(v): return location.distance(v.get_location())\n for n, vehicle in enumerate(sorted(vehicle_list, key=distance)):\n if n > 15:\n break\n vehicle_type = get_actor_display_name(vehicle, truncate=22)\n info_text.append('% 5d %s' % (vehicle.id, vehicle_type))\n module_manager.get_module(MODULE_HUD).add_info(\n 'NEARBY VEHICLES',\n info_text)\n\n return (vehicles, traffic_lights, speed_limits, walkers)\n\n def _render_traffic_lights(self, surface, list_tl, world_to_pixel):\n self.affected_traffic_light = None\n\n for tl in list_tl:\n world_pos = tl.get_location()\n pos = world_to_pixel(world_pos)\n\n if self.args.show_triggers:\n corners = Util.get_bounding_box(tl)\n corners = [world_to_pixel(p) for p in corners]\n pygame.draw.lines(surface, COLOR_BUTTER_1, True, corners, 2)\n\n if self.hero_actor is not None:\n corners = Util.get_bounding_box(tl)\n corners = [world_to_pixel(p) for p in corners]\n tl_t = tl.get_transform()\n\n transformed_tv = tl_t.transform(tl.trigger_volume.location)\n hero_location = self.hero_actor.get_location()\n d = hero_location.distance(transformed_tv)\n s = Util.length(tl.trigger_volume.extent) + Util.length(self.hero_actor.bounding_box.extent)\n if (d <= s):\n # Highlight traffic light\n self.affected_traffic_light = tl\n srf = self.traffic_light_surfaces.surfaces['h']\n surface.blit(srf, srf.get_rect(center=pos))\n\n srf = self.traffic_light_surfaces.surfaces[tl.state]\n surface.blit(srf, srf.get_rect(center=pos))\n\n def _render_speed_limits(self, surface, list_sl, world_to_pixel, world_to_pixel_width):\n\n font_size = world_to_pixel_width(2)\n radius = world_to_pixel_width(2)\n font = pygame.font.SysFont('Arial', font_size)\n\n for sl in list_sl:\n\n x, y = world_to_pixel(sl.get_location())\n\n # Render speed limit\n white_circle_radius = int(radius * 0.75)\n\n pygame.draw.circle(surface, COLOR_SCARLET_RED_1, (x, y), radius)\n pygame.draw.circle(surface, COLOR_ALUMINIUM_0, (x, y), white_circle_radius)\n\n limit = sl.type_id.split('.')[2]\n font_surface = font.render(limit, True, COLOR_ALUMINIUM_5)\n\n if self.args.show_triggers:\n corners = Util.get_bounding_box(sl)\n corners = [world_to_pixel(p) for p in corners]\n pygame.draw.lines(surface, COLOR_PLUM_2, True, corners, 2)\n\n # Blit\n if self.hero_actor is not None:\n # Rotate font surface with respect to hero vehicle front\n angle = -self.hero_transform.rotation.yaw - 90.0\n font_surface = pygame.transform.rotate(font_surface, angle)\n offset = font_surface.get_rect(center=(x, y))\n surface.blit(font_surface, offset)\n\n else:\n surface.blit(font_surface, (x - radius / 2, y - radius / 2))\n\n def _render_walkers(self, surface, list_w, world_to_pixel):\n for w in list_w:\n color = COLOR_PLUM_0\n\n # Compute bounding box points\n bb = w[0].bounding_box.extent\n corners = [\n carla.Location(x=-bb.x, y=-bb.y),\n carla.Location(x=bb.x, y=-bb.y),\n carla.Location(x=bb.x, y=bb.y),\n carla.Location(x=-bb.x, y=bb.y)]\n\n w[1].transform(corners)\n corners = [world_to_pixel(p) for p in corners]\n pygame.draw.polygon(surface, color, corners)\n\n def _render_vehicles(self, surface, list_v, world_to_pixel):\n\n for v in list_v:\n color = COLOR_SKY_BLUE_0\n if int(v[0].attributes['number_of_wheels']) == 2:\n color = COLOR_CHOCOLATE_1\n if v[0].attributes['role_name'] == 'hero':\n color = COLOR_CHAMELEON_0\n # Compute bounding box points\n bb = v[0].bounding_box.extent\n corners = [carla.Location(x=-bb.x, y=-bb.y),\n carla.Location(x=bb.x - 0.8, y=-bb.y),\n carla.Location(x=bb.x, y=0),\n carla.Location(x=bb.x - 0.8, y=bb.y),\n carla.Location(x=-bb.x, y=bb.y),\n carla.Location(x=-bb.x, y=-bb.y)\n ]\n v[1].transform(corners)\n corners = [world_to_pixel(p) for p in corners]\n pygame.draw.lines(surface, color, False, corners, int(math.ceil(4.0 * self.map_image.scale)))\n\n def render_actors(self, surface, vehicles, traffic_lights, speed_limits, walkers):\n # Static actors\n self._render_traffic_lights(surface, [tl[0] for tl in traffic_lights], self.map_image.world_to_pixel)\n self._render_speed_limits(surface, [sl[0] for sl in speed_limits], self.map_image.world_to_pixel,\n self.map_image.world_to_pixel_width)\n\n # Dynamic actors\n self._render_vehicles(surface, vehicles, self.map_image.world_to_pixel)\n self._render_walkers(surface, walkers, self.map_image.world_to_pixel)\n\n def clip_surfaces(self, clipping_rect):\n self.actors_surface.set_clip(clipping_rect)\n self.vehicle_id_surface.set_clip(clipping_rect)\n self.result_surface.set_clip(clipping_rect)\n\n def _compute_scale(self, scale_factor):\n m = self.module_input.mouse_pos\n\n # Percentage of surface where mouse position is actually\n px = (m[0] - self.scale_offset[0]) / float(self.prev_scaled_size)\n py = (m[1] - self.scale_offset[1]) / float(self.prev_scaled_size)\n\n # Offset will be the previously accumulated offset added with the\n # difference of mouse positions in the old and new scales\n diff_between_scales = ((float(self.prev_scaled_size) * px) - (float(self.scaled_size) * px),\n (float(self.prev_scaled_size) * py) - (float(self.scaled_size) * py))\n\n self.scale_offset = (self.scale_offset[0] + diff_between_scales[0],\n self.scale_offset[1] + diff_between_scales[1])\n\n # Update previous scale\n self.prev_scaled_size = self.scaled_size\n\n # Scale performed\n self.map_image.scale_map(scale_factor)\n\n def render(self, display):\n if self.actors_with_transforms is None:\n return\n self.result_surface.fill(COLOR_BLACK)\n vehicles, traffic_lights, speed_limits, walkers = self._split_actors()\n\n scale_factor = self.module_input.wheel_offset\n self.scaled_size = int(self.map_image.width * scale_factor)\n if self.scaled_size != self.prev_scaled_size:\n self._compute_scale(scale_factor)\n\n # Render Actors\n\n self.actors_surface.fill(COLOR_BLACK)\n self.render_actors(\n self.actors_surface,\n vehicles,\n traffic_lights,\n speed_limits,\n walkers)\n\n # Render Ids\n self.module_hud.render_vehicles_ids(self.vehicle_id_surface, vehicles,\n self.map_image.world_to_pixel, self.hero_actor, self.hero_transform)\n\n # Blit surfaces\n surfaces = ((self.map_image.surface, (0, 0)),\n (self.actors_surface, (0, 0)),\n (self.vehicle_id_surface, (0, 0)),\n )\n\n angle = 0.0 if self.hero_actor is None else self.hero_transform.rotation.yaw + 90.0\n self.traffic_light_surfaces.rotozoom(-angle, self.map_image.scale)\n\n center_offset = (0, 0)\n if self.hero_actor is not None:\n\n hero_location_screen = self.map_image.world_to_pixel(self.hero_transform.location)\n hero_front = self.hero_transform.get_forward_vector()\n translation_offset = (\n hero_location_screen[0] -\n self.hero_surface.get_width() /\n 2 +\n hero_front.x *\n PIXELS_AHEAD_VEHICLE,\n (hero_location_screen[1] -\n self.hero_surface.get_height() /\n 2 +\n hero_front.y *\n PIXELS_AHEAD_VEHICLE))\n\n # Apply clipping rect\n clipping_rect = pygame.Rect(translation_offset[0],\n translation_offset[1],\n self.hero_surface.get_width(),\n self.hero_surface.get_height())\n self.clip_surfaces(clipping_rect)\n\n Util.blits(self.result_surface, surfaces)\n\n self.border_round_surface.set_clip(clipping_rect)\n\n self.hero_surface.fill(COLOR_ALUMINIUM_4)\n self.hero_surface.blit(self.result_surface, (-translation_offset[0],\n -translation_offset[1]))\n\n rotated_result_surface = pygame.transform.rotozoom(self.hero_surface, angle, 0.9).convert()\n\n center = (display.get_width() / 2, display.get_height() / 2)\n rotation_pivot = rotated_result_surface.get_rect(center=center)\n display.blit(rotated_result_surface, rotation_pivot)\n\n display.blit(self.border_round_surface, (0, 0))\n else:\n # Translation offset\n translation_offset = (self.module_input.mouse_offset[0] * scale_factor + self.scale_offset[0],\n self.module_input.mouse_offset[1] * scale_factor + self.scale_offset[1])\n center_offset = (abs(display.get_width() - self.surface_size) / 2 * scale_factor, 0)\n\n # Apply clipping rect\n clipping_rect = pygame.Rect(-translation_offset[0] - center_offset[0], -translation_offset[1],\n self.module_hud.dim[0], self.module_hud.dim[1])\n self.clip_surfaces(clipping_rect)\n Util.blits(self.result_surface, surfaces)\n\n display.blit(self.result_surface, (translation_offset[0] + center_offset[0],\n translation_offset[1]))\n\n def destroy(self):\n if self.spawned_hero is not None:\n self.spawned_hero.destroy()\n# ==============================================================================\n# -- Input -----------------------------------------------------------\n# ==============================================================================\n\n\nclass ModuleInput(object):\n def __init__(self, name):\n self.name = name\n self.mouse_pos = (0, 0)\n self.mouse_offset = [0.0, 0.0]\n self.wheel_offset = 0.1\n self.wheel_amount = 0.025\n self._steer_cache = 0.0\n self.control = None\n self._autopilot_enabled = False\n\n def start(self):\n hud = module_manager.get_module(MODULE_HUD)\n hud.notification(\"Press 'H' or '?' for help.\", seconds=4.0)\n\n def render(self, display):\n pass\n\n def tick(self, clock):\n self.parse_input(clock)\n\n def _parse_events(self):\n self.mouse_pos = pygame.mouse.get_pos()\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n exit_game()\n elif event.type == pygame.KEYUP:\n if self._is_quit_shortcut(event.key):\n exit_game()\n elif event.key == K_h or (event.key == K_SLASH and pygame.key.get_mods() & KMOD_SHIFT):\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.help.toggle()\n elif event.key == K_TAB:\n module_world = module_manager.get_module(MODULE_WORLD)\n module_hud = module_manager.get_module(MODULE_HUD)\n if module_world.hero_actor is None:\n module_world.select_hero_actor()\n self.wheel_offset = HERO_DEFAULT_SCALE\n self.control = carla.VehicleControl()\n module_hud.notification('Hero Mode')\n else:\n self.wheel_offset = MAP_DEFAULT_SCALE\n self.mouse_offset = [0, 0]\n self.mouse_pos = [0, 0]\n module_world.scale_offset = [0, 0]\n module_world.hero_actor = None\n module_hud.notification('Map Mode')\n elif event.key == K_F1:\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.show_info = not module_hud.show_info\n elif event.key == K_i:\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.show_actor_ids = not module_hud.show_actor_ids\n elif isinstance(self.control, carla.VehicleControl):\n if event.key == K_q:\n self.control.gear = 1 if self.control.reverse else -1\n elif event.key == K_m:\n self.control.manual_gear_shift = not self.control.manual_gear_shift\n world = module_manager.get_module(MODULE_WORLD)\n self.control.gear = world.hero_actor.get_control().gear\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.notification('%s Transmission' % (\n 'Manual' if self.control.manual_gear_shift else 'Automatic'))\n elif self.control.manual_gear_shift and event.key == K_COMMA:\n self.control.gear = max(-1, self.control.gear - 1)\n elif self.control.manual_gear_shift and event.key == K_PERIOD:\n self.control.gear = self.control.gear + 1\n elif event.key == K_p:\n world = module_manager.get_module(MODULE_WORLD)\n if world.hero_actor is not None:\n self._autopilot_enabled = not self._autopilot_enabled\n world.hero_actor.set_autopilot(self._autopilot_enabled)\n module_hud = module_manager.get_module(MODULE_HUD)\n module_hud.notification('Autopilot %s' % ('On' if self._autopilot_enabled else 'Off'))\n elif event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 4:\n self.wheel_offset += self.wheel_amount\n if self.wheel_offset >= 1.0:\n self.wheel_offset = 1.0\n elif event.button == 5:\n self.wheel_offset -= self.wheel_amount\n if self.wheel_offset <= 0.1:\n self.wheel_offset = 0.1\n\n def _parse_keys(self, milliseconds):\n keys = pygame.key.get_pressed()\n self.control.throttle = 1.0 if keys[K_UP] or keys[K_w] else 0.0\n steer_increment = 5e-4 * milliseconds\n if keys[K_LEFT] or keys[K_a]:\n self._steer_cache -= steer_increment\n elif keys[K_RIGHT] or keys[K_d]:\n self._steer_cache += steer_increment\n else:\n self._steer_cache = 0.0\n self._steer_cache = min(0.7, max(-0.7, self._steer_cache))\n self.control.steer = round(self._steer_cache, 1)\n self.control.brake = 1.0 if keys[K_DOWN] or keys[K_s] else 0.0\n self.control.hand_brake = keys[K_SPACE]\n\n def _parse_mouse(self):\n if pygame.mouse.get_pressed()[0]:\n x, y = pygame.mouse.get_pos()\n self.mouse_offset[0] += (1.0 / self.wheel_offset) * (x - self.mouse_pos[0])\n self.mouse_offset[1] += (1.0 / self.wheel_offset) * (y - self.mouse_pos[1])\n self.mouse_pos = (x, y)\n\n def parse_input(self, clock):\n self._parse_events()\n self._parse_mouse()\n if not self._autopilot_enabled:\n if isinstance(self.control, carla.VehicleControl):\n self._parse_keys(clock.get_time())\n self.control.reverse = self.control.gear < 0\n world = module_manager.get_module(MODULE_WORLD)\n if (world.hero_actor is not None):\n world.hero_actor.apply_control(self.control)\n\n @staticmethod\n def _is_quit_shortcut(key):\n return (key == K_ESCAPE) or (key == K_q and pygame.key.get_mods() & KMOD_CTRL)\n\n\n# ==============================================================================\n# -- Global Objects ------------------------------------------------------------\n# ==============================================================================\nmodule_manager = ModuleManager()\n\n\n# ==============================================================================\n# -- Game Loop ---------------------------------------------------------------\n# ==============================================================================\n\n\ndef game_loop(args):\n try:\n # Init Pygame\n pygame.init()\n display = pygame.display.set_mode(\n (args.width, args.height),\n pygame.HWSURFACE | pygame.DOUBLEBUF)\n pygame.display.set_caption(args.description)\n\n font = pygame.font.Font(pygame.font.get_default_font(), 20)\n text_surface = font.render('Rendering map...', True, COLOR_WHITE)\n display.blit(text_surface, text_surface.get_rect(center=(args.width / 2, args.height / 2)))\n pygame.display.flip()\n\n # Init modules\n input_module = ModuleInput(MODULE_INPUT)\n hud_module = ModuleHUD(MODULE_HUD, args.width, args.height)\n world_module = ModuleWorld(MODULE_WORLD, args, timeout=2.0)\n\n # Register Modules\n module_manager.register_module(world_module)\n module_manager.register_module(hud_module)\n module_manager.register_module(input_module)\n\n module_manager.start_modules()\n\n clock = pygame.time.Clock()\n while True:\n clock.tick_busy_loop(60)\n\n module_manager.tick(clock)\n module_manager.render(display)\n\n pygame.display.flip()\n\n except KeyboardInterrupt:\n print('\\nCancelled by user. Bye!')\n\n finally:\n if world_module is not None:\n world_module.destroy()\n\ndef exit_game():\n module_manager.clear_modules()\n pygame.quit()\n sys.exit()\n\n# ==============================================================================\n# -- Main --------------------------------------------------------------------\n# ==============================================================================\n\n\ndef main():\n # Parse arguments\n argparser = argparse.ArgumentParser(\n description='CARLA No Rendering Mode Visualizer')\n argparser.add_argument(\n '-v', '--verbose',\n action='store_true',\n dest='debug',\n help='print debug information')\n argparser.add_argument(\n '--host',\n metavar='H',\n default='127.0.0.1',\n help='IP of the host server (default: 127.0.0.1)')\n argparser.add_argument(\n '-p', '--port',\n metavar='P',\n default=2000,\n type=int,\n help='TCP port to listen to (default: 2000)')\n argparser.add_argument(\n '--res',\n metavar='WIDTHxHEIGHT',\n default='1280x720',\n help='window resolution (default: 1280x720)')\n argparser.add_argument(\n '--filter',\n metavar='PATTERN',\n default='vehicle.*',\n help='actor filter (default: \"vehicle.*\")')\n argparser.add_argument(\n '--map',\n metavar='TOWN',\n default=None,\n help='start a new episode at the given TOWN')\n argparser.add_argument(\n '--no-rendering',\n action='store_true',\n default=True,\n help='switch off server rendering')\n argparser.add_argument(\n '--show-triggers',\n action='store_true',\n help='show trigger boxes of traffic signs')\n argparser.add_argument(\n '--show-connections',\n action='store_true',\n help='show waypoint connections')\n argparser.add_argument(\n '--show-spawn-points',\n action='store_true',\n help='show recommended spawn points')\n\n args = argparser.parse_args()\n args.description = argparser.description\n args.width, args.height = [int(x) for x in args.res.split('x')]\n\n log_level = logging.DEBUG if args.debug else logging.INFO\n logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)\n\n logging.info('listening to server %s:%s', args.host, args.port)\n print(__doc__)\n\n game_loop(args)\n\nif __name__ == '__main__':\n main()\n" }, { "path": "scenario_runner/requirements.txt", "content": "py-trees==0.8.3\nnetworkx==2.2\nShapely==1.6.4.post2\npsutil\nxmlschema==1.0.18\ncarla\nephem\ntabulate\nopencv-python==4.2.0.32\nnumpy\nmatplotlib\nsix\n" }, { "path": "scenario_runner/scenario_runner.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nWelcome to CARLA scenario_runner\n\nThis is the main script to be executed when running a scenario.\nIt loads the scenario configuration, loads the scenario and manager,\nand finally triggers the scenario execution.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport glob\nimport traceback\nimport argparse\nfrom argparse import RawTextHelpFormatter\nfrom datetime import datetime\nfrom distutils.version import LooseVersion\nimport importlib\nimport inspect\nimport os\nimport signal\nimport sys\nimport time\nimport json\nimport pkg_resources\n\nimport carla\n\nfrom srunner.scenarioconfigs.openscenario_configuration import OpenScenarioConfiguration\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenario_manager import ScenarioManager\nfrom srunner.scenarios.open_scenario import OpenScenario\nfrom srunner.scenarios.route_scenario import RouteScenario\nfrom srunner.tools.scenario_parser import ScenarioConfigurationParser\nfrom srunner.tools.route_parser import RouteParser\n\n# Version of scenario_runner\nVERSION = '0.9.9'\n\n\nclass ScenarioRunner(object):\n\n \"\"\"\n This is the core scenario runner module. It is responsible for\n running (and repeating) a single scenario or a list of scenarios.\n\n Usage:\n scenario_runner = ScenarioRunner(args)\n scenario_runner.run()\n del scenario_runner\n \"\"\"\n\n ego_vehicles = []\n\n # Tunable parameters\n client_timeout = 10.0 # in seconds\n wait_for_world = 20.0 # in seconds\n frame_rate = 20.0 # in Hz\n\n # CARLA world and scenario handlers\n world = None\n manager = None\n\n additional_scenario_module = None\n\n agent_instance = None\n module_agent = None\n\n def __init__(self, args):\n \"\"\"\n Setup CARLA client and world\n Setup ScenarioManager\n \"\"\"\n self._args = args\n\n if args.timeout:\n self.client_timeout = float(args.timeout)\n\n # First of all, we need to create the client that will send the requests\n # to the simulator. Here we'll assume the simulator is accepting\n # requests in the localhost at port 2000.\n self.client = carla.Client(args.host, int(args.port))\n self.client.set_timeout(self.client_timeout)\n\n dist = pkg_resources.get_distribution(\"carla\")\n if LooseVersion(dist.version) < LooseVersion('0.9.8'):\n raise ImportError(\"CARLA version 0.9.8 or newer required. CARLA version found: {}\".format(dist))\n\n # Load agent if requested via command line args\n # If something goes wrong an exception will be thrown by importlib (ok here)\n if self._args.agent is not None:\n module_name = os.path.basename(args.agent).split('.')[0]\n sys.path.insert(0, os.path.dirname(args.agent))\n self.module_agent = importlib.import_module(module_name)\n\n # Create the ScenarioManager\n self.manager = ScenarioManager(self._args.debug, self._args.sync, self._args.timeout)\n\n # Create signal handler for SIGINT\n self._shutdown_requested = False\n if sys.platform != 'win32':\n signal.signal(signal.SIGHUP, self._signal_handler)\n signal.signal(signal.SIGINT, self._signal_handler)\n signal.signal(signal.SIGTERM, self._signal_handler)\n\n self._start_wall_time = datetime.now()\n\n def destroy(self):\n \"\"\"\n Cleanup and delete actors, ScenarioManager and CARLA world\n \"\"\"\n\n self._cleanup()\n if self.manager is not None:\n del self.manager\n if self.world is not None:\n del self.world\n if self.client is not None:\n del self.client\n\n def _signal_handler(self, signum, frame):\n \"\"\"\n Terminate scenario ticking when receiving a signal interrupt\n \"\"\"\n self._shutdown_requested = True\n if self.manager:\n self.manager.stop_scenario()\n self._cleanup()\n if not self.manager.get_running_status():\n raise RuntimeError(\"Timeout occured during scenario execution\")\n\n def _get_scenario_class_or_fail(self, scenario):\n \"\"\"\n Get scenario class by scenario name\n If scenario is not supported or not found, exit script\n \"\"\"\n\n # Path of all scenario at \"srunner/scenarios\" folder + the path of the additional scenario argument\n scenarios_list = glob.glob(\"{}/srunner/scenarios/*.py\".format(os.getenv('SCENARIO_RUNNER_ROOT', \"./\")))\n scenarios_list.append(self._args.additionalScenario)\n\n for scenario_file in scenarios_list:\n\n # Get their module\n module_name = os.path.basename(scenario_file).split('.')[0]\n sys.path.insert(0, os.path.dirname(scenario_file))\n scenario_module = importlib.import_module(module_name)\n\n # And their members of type class\n for member in inspect.getmembers(scenario_module, inspect.isclass):\n if scenario in member:\n return member[1]\n\n # Remove unused Python paths\n sys.path.pop(0)\n\n print(\"Scenario '{}' not supported ... Exiting\".format(scenario))\n sys.exit(-1)\n\n def _cleanup(self):\n \"\"\"\n Remove and destroy all actors\n \"\"\"\n # Simulation still running and in synchronous mode?\n if self.manager is not None and self.manager.get_running_status() \\\n and self.world is not None and self._args.sync:\n # Reset to asynchronous mode\n settings = self.world.get_settings()\n settings.synchronous_mode = False\n settings.fixed_delta_seconds = None\n self.world.apply_settings(settings)\n\n self.manager.cleanup()\n\n CarlaDataProvider.cleanup()\n\n for i, _ in enumerate(self.ego_vehicles):\n if self.ego_vehicles[i]:\n if not self._args.waitForEgo:\n print(\"Destroying ego vehicle {}\".format(self.ego_vehicles[i].id))\n self.ego_vehicles[i].destroy()\n self.ego_vehicles[i] = None\n self.ego_vehicles = []\n\n if self.agent_instance:\n self.agent_instance.destroy()\n self.agent_instance = None\n\n def _prepare_ego_vehicles(self, ego_vehicles):\n \"\"\"\n Spawn or update the ego vehicles\n \"\"\"\n\n if not self._args.waitForEgo:\n for vehicle in ego_vehicles:\n self.ego_vehicles.append(CarlaDataProvider.request_new_actor(vehicle.model,\n vehicle.transform,\n vehicle.rolename,\n color=vehicle.color,\n actor_category=vehicle.category))\n else:\n ego_vehicle_missing = True\n while ego_vehicle_missing:\n self.ego_vehicles = []\n ego_vehicle_missing = False\n for ego_vehicle in ego_vehicles:\n ego_vehicle_found = False\n carla_vehicles = CarlaDataProvider.get_world().get_actors().filter('vehicle.*')\n for carla_vehicle in carla_vehicles:\n if carla_vehicle.attributes['role_name'] == ego_vehicle.rolename:\n ego_vehicle_found = True\n self.ego_vehicles.append(carla_vehicle)\n break\n if not ego_vehicle_found:\n ego_vehicle_missing = True\n break\n\n for i, _ in enumerate(self.ego_vehicles):\n self.ego_vehicles[i].set_transform(ego_vehicles[i].transform)\n CarlaDataProvider.register_actor(self.ego_vehicles[i])\n\n # sync state\n if CarlaDataProvider.is_sync_mode():\n self.world.tick()\n else:\n self.world.wait_for_tick()\n\n def _analyze_scenario(self, config):\n \"\"\"\n Provide feedback about success/failure of a scenario\n \"\"\"\n\n # Create the filename\n current_time = str(datetime.now().strftime('%Y-%m-%d-%H-%M-%S'))\n junit_filename = None\n config_name = config.name\n if self._args.outputDir != '':\n config_name = os.path.join(self._args.outputDir, config_name)\n if self._args.junit:\n junit_filename = config_name + current_time + \".xml\"\n filename = None\n if self._args.file:\n filename = config_name + current_time + \".txt\"\n\n if not self.manager.analyze_scenario(self._args.output, filename, junit_filename):\n print(\"All scenario tests were passed successfully!\")\n else:\n print(\"Not all scenario tests were successful\")\n if not (self._args.output or filename or junit_filename):\n print(\"Please run with --output for further information\")\n\n def _record_criteria(self, criteria, name):\n \"\"\"\n Filter the JSON serializable attributes of the criterias and\n dumps them into a file. This will be used by the metrics manager,\n in case the user wants specific information about the criterias.\n \"\"\"\n file_name = name[:-4] + \".json\"\n\n # Filter the attributes that aren't JSON serializable\n with open('temp.json', 'w') as fp:\n\n criteria_dict = {}\n for criterion in criteria:\n\n criterion_dict = criterion.__dict__\n criteria_dict[criterion.name] = {}\n\n for key in criterion_dict:\n if key != \"name\":\n try:\n key_dict = {key: criterion_dict[key]}\n json.dump(key_dict, fp, sort_keys=False, indent=4)\n criteria_dict[criterion.name].update(key_dict)\n except TypeError:\n pass\n\n os.remove('temp.json')\n\n # Save the criteria dictionary into a .json file\n with open(file_name, 'w') as fp:\n json.dump(criteria_dict, fp, sort_keys=False, indent=4)\n\n def _load_and_wait_for_world(self, town, ego_vehicles=None):\n \"\"\"\n Load a new CARLA world and provide data to CarlaDataProvider\n \"\"\"\n\n if self._args.reloadWorld:\n self.world = self.client.load_world(town)\n else:\n # if the world should not be reloaded, wait at least until all ego vehicles are ready\n ego_vehicle_found = False\n if self._args.waitForEgo:\n while not ego_vehicle_found and not self._shutdown_requested:\n vehicles = self.client.get_world().get_actors().filter('vehicle.*')\n for ego_vehicle in ego_vehicles:\n ego_vehicle_found = False\n for vehicle in vehicles:\n if vehicle.attributes['role_name'] == ego_vehicle.rolename:\n ego_vehicle_found = True\n break\n if not ego_vehicle_found:\n print(\"Not all ego vehicles ready. Waiting ... \")\n time.sleep(1)\n break\n\n self.world = self.client.get_world()\n\n if self._args.sync:\n settings = self.world.get_settings()\n settings.synchronous_mode = True\n settings.fixed_delta_seconds = 1.0 / self.frame_rate\n self.world.apply_settings(settings)\n\n CarlaDataProvider.set_client(self.client)\n CarlaDataProvider.set_world(self.world)\n CarlaDataProvider.set_traffic_manager_port(int(self._args.trafficManagerPort))\n\n # Wait for the world to be ready\n if CarlaDataProvider.is_sync_mode():\n self.world.tick()\n else:\n self.world.wait_for_tick()\n if CarlaDataProvider.get_map().name != town and CarlaDataProvider.get_map().name != \"OpenDriveMap\":\n print(\"The CARLA server uses the wrong map: {}\".format(CarlaDataProvider.get_map().name))\n print(\"This scenario requires to use map: {}\".format(town))\n return False\n\n return True\n\n def _load_and_run_scenario(self, config):\n \"\"\"\n Load and run the scenario given by config\n \"\"\"\n result = False\n if not self._load_and_wait_for_world(config.town, config.ego_vehicles):\n self._cleanup()\n return False\n\n if self._args.agent:\n agent_class_name = self.module_agent.__name__.title().replace('_', '')\n try:\n self.agent_instance = getattr(self.module_agent, agent_class_name)(self._args.agentConfig)\n config.agent = self.agent_instance\n except Exception as e: # pylint: disable=broad-except\n traceback.print_exc()\n print(\"Could not setup required agent due to {}\".format(e))\n self._cleanup()\n return False\n\n # Prepare scenario\n print(\"Preparing scenario: \" + config.name)\n try:\n self._prepare_ego_vehicles(config.ego_vehicles)\n if self._args.openscenario:\n scenario = OpenScenario(world=self.world,\n ego_vehicles=self.ego_vehicles,\n config=config,\n config_file=self._args.openscenario,\n timeout=100000)\n elif self._args.route:\n scenario = RouteScenario(world=self.world,\n config=config,\n debug_mode=self._args.debug)\n else:\n scenario_class = self._get_scenario_class_or_fail(config.type)\n scenario = scenario_class(self.world,\n self.ego_vehicles,\n config,\n self._args.randomize,\n self._args.debug)\n except Exception as exception: # pylint: disable=broad-except\n print(\"The scenario cannot be loaded\")\n traceback.print_exc()\n print(exception)\n self._cleanup()\n return False\n\n try:\n if self._args.record:\n recorder_name = \"{}/{}/{}.log\".format(\n os.getenv('SCENARIO_RUNNER_ROOT', \"./\"), self._args.record, config.name)\n self.client.start_recorder(recorder_name, True)\n\n # Load scenario and run it\n self.manager.load_scenario(scenario, self.agent_instance)\n self.manager.run_scenario()\n\n # Provide outputs if required\n self._analyze_scenario(config)\n\n # Remove all actors, stop the recorder and save all criterias (if needed)\n scenario.remove_all_actors()\n if self._args.record:\n self.client.stop_recorder()\n self._record_criteria(self.manager.scenario.get_criteria(), recorder_name)\n\n result = True\n\n except Exception as e: # pylint: disable=broad-except\n traceback.print_exc()\n print(e)\n result = False\n\n self._cleanup()\n return result\n\n def _run_scenarios(self):\n \"\"\"\n Run conventional scenarios (e.g. implemented using the Python API of ScenarioRunner)\n \"\"\"\n result = False\n\n # Load the scenario configurations provided in the config file\n scenario_configurations = ScenarioConfigurationParser.parse_scenario_configuration(\n self._args.scenario,\n self._args.configFile)\n if not scenario_configurations:\n print(\"Configuration for scenario {} cannot be found!\".format(self._args.scenario))\n return result\n\n # Execute each configuration\n for config in scenario_configurations:\n for _ in range(self._args.repetitions):\n result = self._load_and_run_scenario(config)\n\n self._cleanup()\n return result\n\n def _run_route(self):\n \"\"\"\n Run the route scenario\n \"\"\"\n result = False\n\n if self._args.route:\n routes = self._args.route[0]\n scenario_file = self._args.route[1]\n single_route = None\n if len(self._args.route) > 2:\n single_route = self._args.route[2]\n\n # retrieve routes\n route_configurations = RouteParser.parse_routes_file(routes, scenario_file, single_route)\n\n for config in route_configurations:\n for _ in range(self._args.repetitions):\n result = self._load_and_run_scenario(config)\n\n self._cleanup()\n return result\n\n def _run_openscenario(self):\n \"\"\"\n Run a scenario based on OpenSCENARIO\n \"\"\"\n\n # Load the scenario configurations provided in the config file\n if not os.path.isfile(self._args.openscenario):\n print(\"File does not exist\")\n self._cleanup()\n return False\n\n config = OpenScenarioConfiguration(self._args.openscenario, self.client)\n\n result = self._load_and_run_scenario(config)\n self._cleanup()\n return result\n\n def run(self):\n \"\"\"\n Run all scenarios according to provided commandline args\n \"\"\"\n result = True\n if self._args.openscenario:\n result = self._run_openscenario()\n elif self._args.route:\n result = self._run_route()\n else:\n result = self._run_scenarios()\n\n print(\"No more scenarios .... Exiting\")\n return result\n\n\ndef main():\n \"\"\"\n main function\n \"\"\"\n description = (\"CARLA Scenario Runner: Setup, Run and Evaluate scenarios using CARLA\\n\"\n \"Current version: \" + VERSION)\n\n # pylint: disable=line-too-long\n parser = argparse.ArgumentParser(description=description,\n formatter_class=RawTextHelpFormatter)\n parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + VERSION)\n parser.add_argument('--host', default='127.0.0.1',\n help='IP of the host server (default: localhost)')\n parser.add_argument('--port', default='2000',\n help='TCP port to listen to (default: 2000)')\n parser.add_argument('--timeout', default=\"10.0\",\n help='Set the CARLA client timeout value in seconds')\n parser.add_argument('--trafficManagerPort', default='8000',\n help='Port to use for the TrafficManager (default: 8000)')\n parser.add_argument('--sync', action='store_true',\n help='Forces the simulation to run synchronously')\n parser.add_argument('--list', action=\"store_true\", help='List all supported scenarios and exit')\n\n parser.add_argument(\n '--scenario', help='Name of the scenario to be executed. Use the preposition \\'group:\\' to run all scenarios of one class, e.g. ControlLoss or FollowLeadingVehicle')\n parser.add_argument('--openscenario', help='Provide an OpenSCENARIO definition')\n parser.add_argument(\n '--route', help='Run a route as a scenario (input: (route_file,scenario_file,[route id]))', nargs='+', type=str)\n\n parser.add_argument(\n '--agent', help=\"Agent used to execute the scenario. Currently only compatible with route-based scenarios.\")\n parser.add_argument('--agentConfig', type=str, help=\"Path to Agent's configuration file\", default=\"\")\n\n parser.add_argument('--output', action=\"store_true\", help='Provide results on stdout')\n parser.add_argument('--file', action=\"store_true\", help='Write results into a txt file')\n parser.add_argument('--junit', action=\"store_true\", help='Write results into a junit file')\n parser.add_argument('--outputDir', default='', help='Directory for output files (default: this directory)')\n\n parser.add_argument('--configFile', default='', help='Provide an additional scenario configuration file (*.xml)')\n parser.add_argument('--additionalScenario', default='', help='Provide additional scenario implementations (*.py)')\n\n parser.add_argument('--debug', action=\"store_true\", help='Run with debug output')\n parser.add_argument('--reloadWorld', action=\"store_true\",\n help='Reload the CARLA world before starting a scenario (default=True)')\n parser.add_argument('--record', type=str, default='',\n help='Path were the files will be saved, relative to SCENARIO_RUNNER_ROOT.\\nActivates the CARLA recording feature and saves to file all the criteria information.')\n parser.add_argument('--randomize', action=\"store_true\", help='Scenario parameters are randomized')\n parser.add_argument('--repetitions', default=1, type=int, help='Number of scenario executions')\n parser.add_argument('--waitForEgo', action=\"store_true\", help='Connect the scenario to an existing ego vehicle')\n\n arguments = parser.parse_args()\n # pylint: enable=line-too-long\n\n if arguments.list:\n print(\"Currently the following scenarios are supported:\")\n print(*ScenarioConfigurationParser.get_list_of_scenarios(arguments.configFile), sep='\\n')\n return 1\n\n if not arguments.scenario and not arguments.openscenario and not arguments.route:\n print(\"Please specify either a scenario or use the route mode\\n\\n\")\n parser.print_help(sys.stdout)\n return 1\n\n if arguments.route and (arguments.openscenario or arguments.scenario):\n print(\"The route mode cannot be used together with a scenario (incl. OpenSCENARIO)'\\n\\n\")\n parser.print_help(sys.stdout)\n return 1\n\n if arguments.agent and (arguments.openscenario or arguments.scenario):\n print(\"Agents are currently only compatible with route scenarios'\\n\\n\")\n parser.print_help(sys.stdout)\n return 1\n\n if arguments.route:\n arguments.reloadWorld = True\n\n if arguments.agent:\n arguments.sync = True\n\n scenario_runner = None\n result = True\n try:\n scenario_runner = ScenarioRunner(arguments)\n result = scenario_runner.run()\n\n finally:\n if scenario_runner is not None:\n scenario_runner.destroy()\n del scenario_runner\n return not result\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n" }, { "path": "scenario_runner/srunner/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/autoagents/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/autoagents/agent_wrapper.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nWrapper for autonomous agents required for tracking and checking of used sensors\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\n\nfrom srunner.autoagents.sensor_interface import CallBack\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\n\nclass AgentWrapper(object):\n\n \"\"\"\n Wrapper for autonomous agents required for tracking and checking of used sensors\n \"\"\"\n\n _agent = None\n _sensors_list = []\n\n def __init__(self, agent):\n \"\"\"\n Set the autonomous agent\n \"\"\"\n self._agent = agent\n\n def __call__(self):\n \"\"\"\n Pass the call directly to the agent\n \"\"\"\n return self._agent()\n\n def setup_sensors(self, vehicle, debug_mode=False):\n \"\"\"\n Create the sensors defined by the user and attach them to the ego-vehicle\n :param vehicle: ego vehicle\n :return:\n \"\"\"\n bp_library = CarlaDataProvider.get_world().get_blueprint_library()\n for sensor_spec in self._agent.sensors():\n # These are the sensors spawned on the carla world\n bp = bp_library.find(str(sensor_spec['type']))\n if sensor_spec['type'].startswith('sensor.camera'):\n bp.set_attribute('image_size_x', str(sensor_spec['width']))\n bp.set_attribute('image_size_y', str(sensor_spec['height']))\n bp.set_attribute('fov', str(sensor_spec['fov']))\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.lidar'):\n bp.set_attribute('range', str(sensor_spec['range']))\n bp.set_attribute('rotation_frequency', str(sensor_spec['rotation_frequency']))\n bp.set_attribute('channels', str(sensor_spec['channels']))\n bp.set_attribute('upper_fov', str(sensor_spec['upper_fov']))\n bp.set_attribute('lower_fov', str(sensor_spec['lower_fov']))\n bp.set_attribute('points_per_second', str(sensor_spec['points_per_second']))\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation(pitch=sensor_spec['pitch'],\n roll=sensor_spec['roll'],\n yaw=sensor_spec['yaw'])\n elif sensor_spec['type'].startswith('sensor.other.gnss'):\n sensor_location = carla.Location(x=sensor_spec['x'], y=sensor_spec['y'],\n z=sensor_spec['z'])\n sensor_rotation = carla.Rotation()\n\n # create sensor\n sensor_transform = carla.Transform(sensor_location, sensor_rotation)\n sensor = CarlaDataProvider.get_world().spawn_actor(bp, sensor_transform, vehicle)\n # setup callback\n sensor.listen(CallBack(sensor_spec['id'], sensor, self._agent.sensor_interface))\n self._sensors_list.append(sensor)\n\n while not self._agent.all_sensors_ready():\n if debug_mode:\n print(\" waiting for one data reading from sensors...\")\n CarlaDataProvider.get_world().tick()\n\n def cleanup(self):\n \"\"\"\n Remove and destroy all sensors\n \"\"\"\n for i, _ in enumerate(self._sensors_list):\n if self._sensors_list[i] is not None:\n self._sensors_list[i].stop()\n self._sensors_list[i].destroy()\n self._sensors_list[i] = None\n self._sensors_list = []\n" }, { "path": "scenario_runner/srunner/autoagents/autonomous_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the base class for all autonomous agents\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\n\nfrom srunner.autoagents.sensor_interface import SensorInterface\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.tools.route_manipulation import downsample_route\n\n\nclass AutonomousAgent(object):\n\n \"\"\"\n Autonomous agent base class. All user agents have to be derived from this class\n \"\"\"\n\n def __init__(self, path_to_conf_file):\n # current global plans to reach a destination\n self._global_plan = None\n self._global_plan_world_coord = None\n\n # this data structure will contain all sensor data\n self.sensor_interface = SensorInterface()\n\n # agent's initialization\n self.setup(path_to_conf_file)\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Initialize everything needed by your agent and set the track attribute to the right type:\n \"\"\"\n pass\n\n def sensors(self): # pylint: disable=no-self-use\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n ]\n\n \"\"\"\n sensors = []\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n :return: control\n \"\"\"\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n return control\n\n def destroy(self):\n \"\"\"\n Destroy (clean-up) the agent\n :return:\n \"\"\"\n pass\n\n def __call__(self):\n \"\"\"\n Execute the agent call, e.g. agent()\n Returns the next vehicle controls\n \"\"\"\n input_data = self.sensor_interface.get_data()\n\n timestamp = GameTime.get_time()\n wallclock = GameTime.get_wallclocktime()\n print('======[Agent] Wallclock_time = {} / Sim_time = {}'.format(wallclock, timestamp))\n\n control = self.run_step(input_data, timestamp)\n control.manual_gear_shift = False\n\n return control\n\n def all_sensors_ready(self):\n \"\"\"\n Check if all sensors are ready\n Returns true if sensors are ready\n \"\"\"\n return self.sensor_interface.all_sensors_ready()\n\n def set_global_plan(self, global_plan_gps, global_plan_world_coord):\n \"\"\"\n Set the plan (route) for the agent\n \"\"\"\n\n ds_ids = downsample_route(global_plan_world_coord, 1)\n self._global_plan_world_coord = [(global_plan_world_coord[x][0], global_plan_world_coord[x][1])\n for x in ds_ids]\n self._global_plan = [global_plan_gps[x] for x in ds_ids]\n" }, { "path": "scenario_runner/srunner/autoagents/dummy_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a dummy agent to control the ego vehicle\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\n\nfrom srunner.autoagents.autonomous_agent import AutonomousAgent\n\n\nclass DummyAgent(AutonomousAgent):\n\n \"\"\"\n Dummy autonomous agent to control the ego vehicle\n \"\"\"\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n\n\n \"\"\"\n sensors = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Center'},\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'width': 800, 'height': 600, 'fov': 100, 'id': 'Left'},\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 45.0,\n 'width': 800, 'height': 600, 'fov': 100, 'id': 'Right'},\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'id': 'LIDAR'},\n {'type': 'sensor.other.gnss', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'id': 'GPS'},\n {'type': 'sensor.can_bus', 'reading_frequency': 25, 'id': 'can_bus'},\n {'type': 'sensor.hd_map', 'reading_frequency': 1, 'id': 'hdmap'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n print(\"=====================>\")\n for key, val in input_data.items():\n if hasattr(val[1], 'shape'):\n shape = val[1].shape\n print(\"[{} -- {:06d}] with shape {}\".format(key, val[0], shape))\n else:\n print(\"[{} -- {:06d}] \".format(key, val[0]))\n print(\"<=====================\")\n\n # DO SOMETHING SMART\n\n # RETURN CONTROL\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n return control\n" }, { "path": "scenario_runner/srunner/autoagents/human_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a human agent to control the ego vehicle via keyboard\n\"\"\"\n\nimport time\nfrom threading import Thread\nimport cv2\nimport numpy as np\n\ntry:\n import pygame\n from pygame.locals import K_DOWN\n from pygame.locals import K_LEFT\n from pygame.locals import K_RIGHT\n from pygame.locals import K_SPACE\n from pygame.locals import K_UP\n from pygame.locals import K_a\n from pygame.locals import K_d\n from pygame.locals import K_s\n from pygame.locals import K_w\nexcept ImportError:\n raise RuntimeError('cannot import pygame, make sure pygame package is installed')\n\nimport carla\n\nfrom srunner.autoagents.autonomous_agent import AutonomousAgent\n\n\nclass HumanInterface(object):\n\n \"\"\"\n Class to control a vehicle manually for debugging purposes\n \"\"\"\n\n def __init__(self, parent):\n self.quit = False\n self._parent = parent\n self._width = 800\n self._height = 600\n self._throttle_delta = 0.05\n self._steering_delta = 0.01\n self._surface = None\n\n pygame.init()\n pygame.font.init()\n self._clock = pygame.time.Clock()\n self._display = pygame.display.set_mode((self._width, self._height), pygame.HWSURFACE | pygame.DOUBLEBUF)\n pygame.display.set_caption(\"Human Agent\")\n\n def run(self):\n \"\"\"\n Run the GUI\n \"\"\"\n while not self._parent.agent_engaged and not self.quit:\n time.sleep(0.5)\n\n controller = KeyboardControl()\n while not self.quit:\n self._clock.tick_busy_loop(20)\n controller.parse_events(self._parent.current_control, self._clock)\n # Process events\n pygame.event.pump()\n\n # process sensor data\n input_data = self._parent.sensor_interface.get_data()\n image_center = input_data['Center'][1][:, :, -2::-1]\n image_left = input_data['Left'][1][:, :, -2::-1]\n image_right = input_data['Right'][1][:, :, -2::-1]\n image_rear = input_data['Rear'][1][:, :, -2::-1]\n\n top_row = np.hstack((image_left, image_center, image_right))\n bottom_row = np.hstack((0 * image_rear, image_rear, 0 * image_rear))\n comp_image = np.vstack((top_row, bottom_row))\n # resize image\n image_rescaled = cv2.resize(comp_image, dsize=(self._width, self._height), interpolation=cv2.INTER_CUBIC)\n\n # display image\n self._surface = pygame.surfarray.make_surface(image_rescaled.swapaxes(0, 1))\n if self._surface is not None:\n self._display.blit(self._surface, (0, 0))\n pygame.display.flip()\n\n pygame.quit()\n\n\nclass HumanAgent(AutonomousAgent):\n\n \"\"\"\n Human agent to control the ego vehicle via keyboard\n \"\"\"\n\n current_control = None\n agent_engaged = False\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n\n self.agent_engaged = False\n self.current_control = carla.VehicleControl()\n self.current_control.steer = 0.0\n self.current_control.throttle = 1.0\n self.current_control.brake = 0.0\n self.current_control.hand_brake = False\n self._hic = HumanInterface(self)\n self._thread = Thread(target=self._hic.run)\n self._thread.start()\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n ['sensor.camera.rgb', {'x':x_rel, 'y': y_rel, 'z': z_rel,\n 'yaw': yaw, 'pitch': pitch, 'roll': roll,\n 'width': width, 'height': height, 'fov': fov}, 'Sensor01'],\n ['sensor.camera.rgb', {'x':x_rel, 'y': y_rel, 'z': z_rel,\n 'yaw': yaw, 'pitch': pitch, 'roll': roll,\n 'width': width, 'height': height, 'fov': fov}, 'Sensor02'],\n\n ['sensor.lidar.ray_cast', {'x':x_rel, 'y': y_rel, 'z': z_rel,\n 'yaw': yaw, 'pitch': pitch, 'roll': roll}, 'Sensor03']\n ]\n\n \"\"\"\n sensors = [{'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Center'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': -45.0, 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 45.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.camera.rgb', 'x': -1.8, 'y': 0, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0,\n 'yaw': 180.0, 'width': 300, 'height': 200, 'fov': 130, 'id': 'Rear'},\n\n {'type': 'sensor.other.gnss', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'id': 'GPS'}\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n self.agent_engaged = True\n time.sleep(0.1)\n return self.current_control\n\n def destroy(self):\n \"\"\"\n Cleanup\n \"\"\"\n self._hic.quit = True\n self._thread.join()\n\n\nclass KeyboardControl(object):\n\n \"\"\"\n Keyboard control for the human agent\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Init\n \"\"\"\n self._control = carla.VehicleControl()\n self._steer_cache = 0.0\n\n def parse_events(self, control, clock):\n \"\"\"\n Parse the keyboard events and set the vehicle controls accordingly\n \"\"\"\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return\n\n self._parse_vehicle_keys(pygame.key.get_pressed(), clock.get_time())\n control.steer = self._control.steer\n control.throttle = self._control.throttle\n control.brake = self._control.brake\n control.hand_brake = self._control.hand_brake\n\n def _parse_vehicle_keys(self, keys, milliseconds):\n \"\"\"\n Calculate new vehicle controls based on input keys\n \"\"\"\n self._control.throttle = 0.6 if keys[K_UP] or keys[K_w] else 0.0\n steer_increment = 15.0 * 5e-4 * milliseconds\n if keys[K_LEFT] or keys[K_a]:\n self._steer_cache -= steer_increment\n elif keys[K_RIGHT] or keys[K_d]:\n self._steer_cache += steer_increment\n else:\n self._steer_cache = 0.0\n\n self._steer_cache = min(0.95, max(-0.95, self._steer_cache))\n self._control.steer = round(self._steer_cache, 1)\n self._control.brake = 1.0 if keys[K_DOWN] or keys[K_s] else 0.0\n self._control.hand_brake = keys[K_SPACE]\n" }, { "path": "scenario_runner/srunner/autoagents/npc_agent.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an NPC agent to control the ego vehicle\n\"\"\"\n\nfrom __future__ import print_function\n\nimport carla\nfrom agents.navigation.basic_agent import BasicAgent\n\nfrom srunner.autoagents.autonomous_agent import AutonomousAgent\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\n\nclass NpcAgent(AutonomousAgent):\n\n \"\"\"\n NPC autonomous agent to control the ego vehicle\n \"\"\"\n\n _agent = None\n _route_assigned = False\n\n def setup(self, path_to_conf_file):\n \"\"\"\n Setup the agent parameters\n \"\"\"\n\n self._route_assigned = False\n self._agent = None\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors in the following format:\n\n [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': 0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Right'},\n\n {'type': 'sensor.lidar.ray_cast', 'x': 0.7, 'y': 0.0, 'z': 1.60, 'yaw': 0.0, 'pitch': 0.0, 'roll': 0.0,\n 'id': 'LIDAR'}\n\n\n \"\"\"\n\n sensors = [\n {'type': 'sensor.camera.rgb', 'x': 0.7, 'y': -0.4, 'z': 1.60, 'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,\n 'width': 300, 'height': 200, 'fov': 100, 'id': 'Left'},\n ]\n\n return sensors\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n control = carla.VehicleControl()\n control.steer = 0.0\n control.throttle = 0.0\n control.brake = 0.0\n control.hand_brake = False\n\n if not self._agent:\n hero_actor = None\n for actor in CarlaDataProvider.get_world().get_actors():\n if 'role_name' in actor.attributes and actor.attributes['role_name'] == 'hero':\n hero_actor = actor\n break\n if hero_actor:\n self._agent = BasicAgent(hero_actor)\n\n return control\n\n if not self._route_assigned:\n if self._global_plan:\n plan = []\n\n for transform, road_option in self._global_plan_world_coord:\n wp = CarlaDataProvider.get_map().get_waypoint(transform.location)\n plan.append((wp, road_option))\n\n self._agent._local_planner.set_global_plan(plan) # pylint: disable=protected-access\n self._route_assigned = True\n\n else:\n control = self._agent.run_step()\n\n return control\n" }, { "path": "scenario_runner/srunner/autoagents/ros_agent.py", "content": "#!/usr/bin/env python\n#\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n#\n\n\"\"\"\nThis module provides a ROS autonomous agent interface to control the ego vehicle via a ROS stack\n\"\"\"\n\nimport math\nimport os\nimport subprocess\nimport signal\nimport threading\nimport time\n\nimport numpy\n\nimport carla\n\nimport rospy\nfrom cv_bridge import CvBridge\nfrom geometry_msgs.msg import PoseStamped\nfrom nav_msgs.msg import Odometry, Path\nfrom rosgraph_msgs.msg import Clock\nfrom sensor_msgs.msg import Image, PointCloud2, NavSatFix, NavSatStatus, CameraInfo\nfrom sensor_msgs.point_cloud2 import create_cloud_xyz32\nfrom std_msgs.msg import Header, String\nimport tf\n# pylint: disable=line-too-long\nfrom carla_msgs.msg import CarlaEgoVehicleStatus, CarlaEgoVehicleInfo, CarlaEgoVehicleInfoWheel, CarlaEgoVehicleControl, CarlaWorldInfo\n# pylint: enable=line-too-long\n\nfrom srunner.autoagents.autonomous_agent import AutonomousAgent\n\n\nclass RosAgent(AutonomousAgent):\n\n \"\"\"\n Base class for ROS-based stacks.\n\n Derive from it and implement the sensors() method.\n\n Please define TEAM_CODE_ROOT in your environment.\n The stack is started by executing $TEAM_CODE_ROOT/start.sh\n\n The sensor data is published on similar topics as with the carla-ros-bridge. You can find details about\n the utilized datatypes there.\n\n This agent expects a roscore to be running.\n \"\"\"\n\n speed = None\n current_control = None\n stack_process = None\n timestamp = None\n current_map_name = None\n step_mode_possible = None\n vehicle_info_publisher = None\n global_plan_published = None\n\n def setup(self, path_to_conf_file):\n \"\"\"\n setup agent\n \"\"\"\n self.stack_thread = None\n\n # get start_script from environment\n team_code_path = os.environ['TEAM_CODE_ROOT']\n if not team_code_path or not os.path.exists(team_code_path):\n raise IOError(\"Path '{}' defined by TEAM_CODE_ROOT invalid\".format(team_code_path))\n start_script = \"{}/start.sh\".format(team_code_path)\n if not os.path.exists(start_script):\n raise IOError(\"File '{}' defined by TEAM_CODE_ROOT invalid\".format(start_script))\n\n # set use_sim_time via commandline before init-node\n process = subprocess.Popen(\n \"rosparam set use_sim_time true\", shell=True, stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\n process.wait()\n if process.returncode:\n raise RuntimeError(\"Could not set use_sim_time\")\n\n # initialize ros node\n rospy.init_node('ros_agent', anonymous=True)\n\n # publish first clock value '0'\n self.clock_publisher = rospy.Publisher('clock', Clock, queue_size=10, latch=True)\n self.clock_publisher.publish(Clock(rospy.Time.from_sec(0)))\n\n # execute script that starts the ad stack (remains running)\n rospy.loginfo(\"Executing stack...\")\n self.stack_process = subprocess.Popen(start_script, shell=True, preexec_fn=os.setpgrp)\n\n self.vehicle_control_event = threading.Event()\n self.timestamp = None\n self.speed = 0\n self.global_plan_published = False\n\n self.vehicle_info_publisher = None\n self.vehicle_status_publisher = None\n self.odometry_publisher = None\n self.world_info_publisher = None\n self.map_file_publisher = None\n self.current_map_name = None\n self.tf_broadcaster = None\n self.step_mode_possible = False\n\n self.vehicle_control_subscriber = rospy.Subscriber(\n '/carla/ego_vehicle/vehicle_control_cmd', CarlaEgoVehicleControl, self.on_vehicle_control)\n\n self.current_control = carla.VehicleControl()\n\n self.waypoint_publisher = rospy.Publisher(\n '/carla/ego_vehicle/waypoints', Path, queue_size=1, latch=True)\n\n self.publisher_map = {}\n self.id_to_sensor_type_map = {}\n self.id_to_camera_info_map = {}\n self.cv_bridge = CvBridge()\n\n # setup ros publishers for sensors\n # pylint: disable=line-too-long\n for sensor in self.sensors():\n self.id_to_sensor_type_map[sensor['id']] = sensor['type']\n if sensor['type'] == 'sensor.camera.rgb':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/camera/rgb/' + sensor['id'] + \"/image_color\", Image, queue_size=1, latch=True)\n self.id_to_camera_info_map[sensor['id']] = self.build_camera_info(sensor)\n self.publisher_map[sensor['id'] + '_info'] = rospy.Publisher(\n '/carla/ego_vehicle/camera/rgb/' + sensor['id'] + \"/camera_info\", CameraInfo, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.lidar.ray_cast':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/lidar/' + sensor['id'] + \"/point_cloud\", PointCloud2, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.other.gnss':\n self.publisher_map[sensor['id']] = rospy.Publisher(\n '/carla/ego_vehicle/gnss/' + sensor['id'] + \"/fix\", NavSatFix, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.can_bus':\n if not self.vehicle_info_publisher:\n self.vehicle_info_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_info', CarlaEgoVehicleInfo, queue_size=1, latch=True)\n if not self.vehicle_status_publisher:\n self.vehicle_status_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_status', CarlaEgoVehicleStatus, queue_size=1, latch=True)\n elif sensor['type'] == 'sensor.hd_map':\n if not self.odometry_publisher:\n self.odometry_publisher = rospy.Publisher(\n '/carla/ego_vehicle/odometry', Odometry, queue_size=1, latch=True)\n if not self.world_info_publisher:\n self.world_info_publisher = rospy.Publisher(\n '/carla/world_info', CarlaWorldInfo, queue_size=1, latch=True)\n if not self.map_file_publisher:\n self.map_file_publisher = rospy.Publisher('/carla/map_file', String, queue_size=1, latch=True)\n if not self.tf_broadcaster:\n self.tf_broadcaster = tf.TransformBroadcaster()\n else:\n raise TypeError(\"Invalid sensor type: {}\".format(sensor['type']))\n # pylint: enable=line-too-long\n\n def destroy(self):\n \"\"\"\n Cleanup of all ROS publishers\n \"\"\"\n if self.stack_process and self.stack_process.poll() is None:\n rospy.loginfo(\"Sending SIGTERM to stack...\")\n os.killpg(os.getpgid(self.stack_process.pid), signal.SIGTERM)\n rospy.loginfo(\"Waiting for termination of stack...\")\n self.stack_process.wait()\n time.sleep(5)\n rospy.loginfo(\"Terminated stack.\")\n\n rospy.loginfo(\"Stack is no longer running\")\n self.world_info_publisher.unregister()\n self.map_file_publisher.unregister()\n self.vehicle_status_publisher.unregister()\n self.vehicle_info_publisher.unregister()\n self.waypoint_publisher.unregister()\n self.stack_process = None\n rospy.loginfo(\"Cleanup finished\")\n\n def on_vehicle_control(self, data):\n \"\"\"\n callback if a new vehicle control command is received\n \"\"\"\n cmd = carla.VehicleControl()\n cmd.throttle = data.throttle\n cmd.steer = data.steer\n cmd.brake = data.brake\n cmd.hand_brake = data.hand_brake\n cmd.reverse = data.reverse\n cmd.gear = data.gear\n cmd.manual_gear_shift = data.manual_gear_shift\n self.current_control = cmd\n if not self.vehicle_control_event.is_set():\n self.vehicle_control_event.set()\n # After the first vehicle control is sent out, it is possible to use the stepping mode\n self.step_mode_possible = True\n\n def build_camera_info(self, attributes): # pylint: disable=no-self-use\n \"\"\"\n Private function to compute camera info\n\n camera info doesn't change over time\n \"\"\"\n camera_info = CameraInfo()\n # store info without header\n camera_info.header = None\n camera_info.width = int(attributes['width'])\n camera_info.height = int(attributes['height'])\n camera_info.distortion_model = 'plumb_bob'\n cx = camera_info.width / 2.0\n cy = camera_info.height / 2.0\n fx = camera_info.width / (\n 2.0 * math.tan(float(attributes['fov']) * math.pi / 360.0))\n fy = fx\n camera_info.K = [fx, 0, cx, 0, fy, cy, 0, 0, 1]\n camera_info.D = [0, 0, 0, 0, 0]\n camera_info.R = [1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]\n camera_info.P = [fx, 0, cx, 0, 0, fy, cy, 0, 0, 0, 1.0, 0]\n return camera_info\n\n def publish_plan(self):\n \"\"\"\n publish the global plan\n \"\"\"\n msg = Path()\n msg.header.frame_id = \"/map\"\n msg.header.stamp = rospy.Time.now()\n for wp in self._global_plan_world_coord:\n pose = PoseStamped()\n pose.pose.position.x = wp[0].location.x\n pose.pose.position.y = -wp[0].location.y\n pose.pose.position.z = wp[0].location.z\n quaternion = tf.transformations.quaternion_from_euler(\n 0, 0, -math.radians(wp[0].rotation.yaw))\n pose.pose.orientation.x = quaternion[0]\n pose.pose.orientation.y = quaternion[1]\n pose.pose.orientation.z = quaternion[2]\n pose.pose.orientation.w = quaternion[3]\n msg.poses.append(pose)\n\n rospy.loginfo(\"Publishing Plan...\")\n self.waypoint_publisher.publish(msg)\n\n def sensors(self):\n \"\"\"\n Define the sensor suite required by the agent\n\n :return: a list containing the required sensors\n \"\"\"\n raise NotImplementedError(\n \"This function has to be implemented by the derived classes\")\n\n def get_header(self):\n \"\"\"\n Returns ROS message header\n \"\"\"\n header = Header()\n header.stamp = rospy.Time.from_sec(self.timestamp)\n return header\n\n def publish_lidar(self, sensor_id, data):\n \"\"\"\n Function to publish lidar data\n \"\"\"\n header = self.get_header()\n header.frame_id = 'ego_vehicle/lidar/{}'.format(sensor_id)\n\n lidar_data = numpy.frombuffer(\n data, dtype=numpy.float32)\n lidar_data = numpy.reshape(\n lidar_data, (int(lidar_data.shape[0] / 3), 3))\n # we take the oposite of y axis\n # (as lidar point are express in left handed coordinate system, and ros need right handed)\n # we need a copy here, because the data are read only in carla numpy\n # array\n lidar_data = -1.0 * lidar_data\n # we also need to permute x and y\n lidar_data = lidar_data[..., [1, 0, 2]]\n msg = create_cloud_xyz32(header, lidar_data)\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_gnss(self, sensor_id, data):\n \"\"\"\n Function to publish gnss data\n \"\"\"\n msg = NavSatFix()\n msg.header = self.get_header()\n msg.header.frame_id = 'gps'\n msg.latitude = data[0]\n msg.longitude = data[1]\n msg.altitude = data[2]\n msg.status.status = NavSatStatus.STATUS_SBAS_FIX\n # pylint: disable=line-too-long\n msg.status.service = NavSatStatus.SERVICE_GPS | NavSatStatus.SERVICE_GLONASS | NavSatStatus.SERVICE_COMPASS | NavSatStatus.SERVICE_GALILEO\n # pylint: enable=line-too-long\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_camera(self, sensor_id, data):\n \"\"\"\n Function to publish camera data\n \"\"\"\n msg = self.cv_bridge.cv2_to_imgmsg(data, encoding='bgra8')\n # the camera data is in respect to the camera's own frame\n msg.header = self.get_header()\n msg.header.frame_id = 'ego_vehicle/camera/rgb/{}'.format(sensor_id)\n\n cam_info = self.id_to_camera_info_map[sensor_id]\n cam_info.header = msg.header\n self.publisher_map[sensor_id + '_info'].publish(cam_info)\n self.publisher_map[sensor_id].publish(msg)\n\n def publish_can(self, sensor_id, data):\n \"\"\"\n publish can data\n \"\"\"\n if not self.vehicle_info_publisher:\n self.vehicle_info_publisher = rospy.Publisher(\n '/carla/ego_vehicle/vehicle_info', CarlaEgoVehicleInfo, queue_size=1, latch=True)\n info_msg = CarlaEgoVehicleInfo()\n for wheel in data['wheels']:\n wheel_info = CarlaEgoVehicleInfoWheel()\n wheel_info.tire_friction = wheel['tire_friction']\n wheel_info.damping_rate = wheel['damping_rate']\n wheel_info.steer_angle = wheel['steer_angle']\n wheel_info.disable_steering = wheel['disable_steering']\n info_msg.wheels.append(wheel_info)\n info_msg.max_rpm = data['max_rpm']\n info_msg.moi = data['moi']\n info_msg.damping_rate_full_throttle = data['damping_rate_full_throttle']\n info_msg.damping_rate_zero_throttle_clutch_disengaged = data['damping_rate_zero_throttle_clutch_disengaged']\n info_msg.use_gear_autobox = data['use_gear_autobox']\n info_msg.clutch_strength = data['clutch_strength']\n info_msg.mass = data['mass']\n info_msg.drag_coefficient = data['drag_coefficient']\n info_msg.center_of_mass.x = data['center_of_mass']['x']\n info_msg.center_of_mass.y = data['center_of_mass']['y']\n info_msg.center_of_mass.z = data['center_of_mass']['z']\n self.vehicle_info_publisher.publish(info_msg)\n msg = CarlaEgoVehicleStatus()\n msg.header = self.get_header()\n msg.velocity = data['speed']\n self.speed = data['speed']\n # msg.acceleration\n msg.control.throttle = self.current_control.throttle\n msg.control.steer = self.current_control.steer\n msg.control.brake = self.current_control.brake\n msg.control.hand_brake = self.current_control.hand_brake\n msg.control.reverse = self.current_control.reverse\n msg.control.gear = self.current_control.gear\n msg.control.manual_gear_shift = self.current_control.manual_gear_shift\n\n self.vehicle_status_publisher.publish(msg)\n\n def publish_hd_map(self, sensor_id, data):\n \"\"\"\n publish hd map data\n \"\"\"\n roll = -math.radians(data['transform']['roll'])\n pitch = -math.radians(data['transform']['pitch'])\n yaw = -math.radians(data['transform']['yaw'])\n quat = tf.transformations.quaternion_from_euler(roll, pitch, yaw)\n x = data['transform']['x']\n y = -data['transform']['y']\n z = data['transform']['z']\n\n if self.odometry_publisher:\n\n odometry = Odometry()\n odometry.header.frame_id = 'map'\n odometry.header.stamp = rospy.Time.from_sec(self.timestamp)\n odometry.child_frame_id = 'base_link'\n odometry.pose.pose.position.x = x\n odometry.pose.pose.position.y = y\n odometry.pose.pose.position.z = z\n\n odometry.pose.pose.orientation.x = quat[0]\n odometry.pose.pose.orientation.y = quat[1]\n odometry.pose.pose.orientation.z = quat[2]\n odometry.pose.pose.orientation.w = quat[3]\n\n odometry.twist.twist.linear.x = self.speed\n odometry.twist.twist.linear.y = 0\n odometry.twist.twist.linear.z = 0\n\n self.odometry_publisher.publish(odometry)\n\n if self.world_info_publisher:\n # extract map name\n map_name = os.path.basename(data['map_file'])[:-4]\n if self.current_map_name != map_name:\n self.current_map_name = map_name\n world_info = CarlaWorldInfo()\n world_info.map_name = self.current_map_name\n world_info.opendrive = data['opendrive']\n self.world_info_publisher.publish(world_info)\n if self.map_file_publisher:\n self.map_file_publisher.publish(data['map_file'])\n\n def use_stepping_mode(self): # pylint: disable=no-self-use\n \"\"\"\n Overload this function to use stepping mode!\n \"\"\"\n return False\n\n def run_step(self, input_data, timestamp):\n \"\"\"\n Execute one step of navigation.\n \"\"\"\n self.vehicle_control_event.clear()\n self.timestamp = timestamp\n self.clock_publisher.publish(Clock(rospy.Time.from_sec(timestamp)))\n\n # check if stack is still running\n if self.stack_process and self.stack_process.poll() is not None:\n raise RuntimeError(\"Stack exited with: {} {}\".format(\n self.stack_process.returncode, self.stack_process.communicate()[0]))\n\n # publish global plan to ROS once\n if self._global_plan_world_coord and not self.global_plan_published:\n self.global_plan_published = True\n self.publish_plan()\n\n new_data_available = False\n\n # publish data of all sensors\n for key, val in input_data.items():\n new_data_available = True\n sensor_type = self.id_to_sensor_type_map[key]\n if sensor_type == 'sensor.camera.rgb':\n self.publish_camera(key, val[1])\n elif sensor_type == 'sensor.lidar.ray_cast':\n self.publish_lidar(key, val[1])\n elif sensor_type == 'sensor.other.gnss':\n self.publish_gnss(key, val[1])\n elif sensor_type == 'sensor.can_bus':\n self.publish_can(key, val[1])\n elif sensor_type == 'sensor.hd_map':\n self.publish_hd_map(key, val[1])\n else:\n raise TypeError(\"Invalid sensor type: {}\".format(sensor_type))\n\n if self.use_stepping_mode():\n if self.step_mode_possible and new_data_available:\n self.vehicle_control_event.wait()\n # if the stepping mode is not used or active, there is no need to wait here\n\n return self.current_control\n" }, { "path": "scenario_runner/srunner/autoagents/sensor_interface.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis file containts CallBack class and SensorInterface, responsible of\nhandling the use of sensors for the agents\n\"\"\"\n\nimport copy\nimport logging\nimport numpy as np\n\nimport carla\n\n\nclass CallBack(object):\n\n \"\"\"\n Class the sensors listen to in order to receive their data each frame\n \"\"\"\n\n def __init__(self, tag, sensor, data_provider):\n \"\"\"\n Initializes the call back\n \"\"\"\n self._tag = tag\n self._data_provider = data_provider\n\n self._data_provider.register_sensor(tag, sensor)\n\n def __call__(self, data):\n \"\"\"\n call function\n \"\"\"\n if isinstance(data, carla.Image):\n self._parse_image_cb(data, self._tag)\n elif isinstance(data, carla.LidarMeasurement):\n self._parse_lidar_cb(data, self._tag)\n elif isinstance(data, carla.GnssMeasurement):\n self._parse_gnss_cb(data, self._tag)\n else:\n logging.error('No callback method for this sensor.')\n\n # Parsing CARLA physical Sensors\n def _parse_image_cb(self, image, tag):\n \"\"\"\n parses cameras\n \"\"\"\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = copy.deepcopy(array)\n array = np.reshape(array, (image.height, image.width, 4))\n self._data_provider.update_sensor(tag, array, image.frame)\n\n def _parse_lidar_cb(self, lidar_data, tag):\n \"\"\"\n parses lidar sensors\n \"\"\"\n points = np.frombuffer(lidar_data.raw_data, dtype=np.dtype('f4'))\n points = copy.deepcopy(points)\n points = np.reshape(points, (int(points.shape[0] / 3), 3))\n self._data_provider.update_sensor(tag, points, lidar_data.frame)\n\n def _parse_gnss_cb(self, gnss_data, tag):\n \"\"\"\n parses gnss sensors\n \"\"\"\n array = np.array([gnss_data.latitude,\n gnss_data.longitude,\n gnss_data.altitude], dtype=np.float64)\n self._data_provider.update_sensor(tag, array, gnss_data.frame)\n\n\nclass SensorInterface(object):\n\n \"\"\"\n Class that contains all sensor data\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Initializes the class\n \"\"\"\n self._sensors_objects = {}\n self._data_buffers = {}\n self._timestamps = {}\n\n def register_sensor(self, tag, sensor):\n \"\"\"\n Registers the sensors\n \"\"\"\n if tag in self._sensors_objects:\n raise ValueError(\"Duplicated sensor tag [{}]\".format(tag))\n\n self._sensors_objects[tag] = sensor\n self._data_buffers[tag] = None\n self._timestamps[tag] = -1\n\n def update_sensor(self, tag, data, timestamp):\n \"\"\"\n Updates the sensor\n \"\"\"\n if tag not in self._sensors_objects:\n raise ValueError(\"The sensor with tag [{}] has not been created!\".format(tag))\n self._data_buffers[tag] = data\n self._timestamps[tag] = timestamp\n\n def all_sensors_ready(self):\n \"\"\"\n Checks if all the sensors have sent data at least once\n \"\"\"\n for key in self._sensors_objects:\n if self._data_buffers[key] is None:\n return False\n \n # for k, v in self._sensors_objects.items():\n # print(k, v, v.get_transformer().get_matrix(), flush=True)\n return True\n\n def get_data(self):\n \"\"\"\n Returns the data of a sensor\n \"\"\"\n data_dict = {}\n for key in self._sensors_objects:\n data_dict[key] = (self._timestamps[key], self._data_buffers[key])\n return data_dict\n" }, { "path": "scenario_runner/srunner/data/all_towns_traffic_scenarios1_3_4.json", "content": "{\n \"available_scenarios\": [\n {\n \"Town01\": [\n {\n \"available_event_configurations\": [\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"319.64\",\n \"y\": \"-2.20\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"107.98\",\n \"y\": \"330.64\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"317.95\",\n \"y\": \"326.51\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"108.68\",\n \"y\": \"199.13\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"317.15\",\n \"y\": \"195.0\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"108.81\",\n \"y\": \"133.54\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"319.79\",\n \"y\": \"129.41\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"322.54\",\n \"y\": \"55.39\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"170.84\",\n \"y\": \"59.52\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"2.52\",\n \"y\": \"316.80\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"2.52\",\n \"y\": \"163.40\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"-1.81\",\n \"y\": \"163.80\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"-1.61\",\n \"y\": \"10.73\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"396.30\",\n \"y\": \"317.80\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"396.30\",\n \"y\": \"165.30\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"392.17\",\n \"y\": \"12.30\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"338.96\",\n \"y\": \"31.64\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"304.75\",\n \"y\": \"199.22\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.55\",\n \"y\": \"133.65\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.35\",\n \"y\": \"59.61\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"158.10\",\n \"y\": \"31.4\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"92.45\",\n \"y\": \"30.84\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"121.53\",\n \"y\": \"55.42\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"121.33\",\n \"y\": \"129.48\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"122.42\",\n \"y\": \"195.16\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"153.94\",\n \"y\": \"26.19\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"88.23\",\n \"y\": \"297.43\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"334.81\",\n \"y\": \"297.3\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.12\",\n \"y\": \"2.26\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"334.54\",\n \"y\": \"165.78\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"334.54\",\n \"y\": \"100.1\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"334.54\",\n \"y\": \"25.77\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"124.46\",\n \"y\": \"2.26\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"58.81\",\n \"y\": \"2.26\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"92.55\",\n \"y\": \"88.85\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"92.55\",\n \"y\": \"163.42\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"92.55\",\n \"y\": \"228.60\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"187.78\",\n \"y\": \"55.38\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"121.57\",\n \"y\": \"326.42\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"368.55\",\n \"y\": \"326.42\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n }\n ],\n \"scenario_type\": \"Scenario1\"\n },\n {\n \"available_event_configurations\": [\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"319.64\",\n \"y\": \"-2.20\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"107.98\",\n \"y\": \"330.64\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"317.95\",\n \"y\": \"326.51\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"108.68\",\n \"y\": \"199.13\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"317.15\",\n \"y\": \"195.0\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"108.81\",\n \"y\": \"133.54\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"319.79\",\n \"y\": \"129.41\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"322.54\",\n \"y\": \"55.39\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"170.84\",\n \"y\": \"59.52\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"167.69\",\n \"y\": \"1.93\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"2.52\",\n \"y\": \"316.80\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"2.52\",\n \"y\": \"163.40\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"-1.81\",\n \"y\": \"163.80\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"-1.61\",\n \"y\": \"10.73\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"396.30\",\n \"y\": \"317.80\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"392.17\",\n \"y\": \"165.10\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"396.30\",\n \"y\": \"165.30\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"392.17\",\n \"y\": \"12.30\",\n \"yaw\": \"89\",\n \"z\": \"1.0\"\n }\n }\n ],\n \"scenario_type\": \"Scenario3\"\n },\n {\n \"available_event_configurations\": [\n {\n \"other_actors\": {\n \"front\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"374.11\",\n \"y\": \"326.55\",\n \"yaw\": \"180.000015\",\n \"z\": \"1.0\"\n }\n ],\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"335.4\",\n \"y\": \"291.71\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.22\",\n \"y\": \"330.72\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"299.99\",\n \"y\": \"2.0\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"373.1\",\n \"y\": \"-2.3\",\n \"yaw\": \"180.000015\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"338.96\",\n \"y\": \"31.64\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"335.5\",\n \"y\": \"94.65\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"339.55\",\n \"y\": \"168.65\",\n \"yaw\": \"270.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.55\",\n \"y\": \"133.65\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"334.85\",\n \"y\": \"20.61\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"339.35\",\n \"y\": \"94.61\",\n \"yaw\": \"270.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"305.35\",\n \"y\": \"59.61\",\n \"yaw\": \"0\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"119.51\",\n \"y\": \"1.44\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"192.90\",\n \"y\": \"-1.61\",\n \"yaw\": \"180.000015\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"158.10\",\n \"y\": \"31.4\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"53.61\",\n \"y\": \"2.3\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"126.94\",\n \"y\": \"-2.32\",\n \"yaw\": \"180.000015\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"92.45\",\n \"y\": \"30.84\",\n \"yaw\": \"270\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"91.74\",\n \"y\": \"94.29\",\n \"yaw\": \"270.000061\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"87.47\",\n \"y\": \"20.20\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"121.53\",\n \"y\": \"55.42\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"92.46\",\n \"y\": \"168.64\",\n \"yaw\": \"270.000061\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"88.50\",\n \"y\": \"94.86\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"121.33\",\n \"y\": \"129.48\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"91.92\",\n \"y\": \"234.16\",\n \"yaw\": \"270.000061\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"88.42\",\n \"y\": \"160.16\",\n \"yaw\": \"90.0\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"122.42\",\n \"y\": \"195.16\",\n \"yaw\": \"180\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"192.94\",\n \"y\": \"55.69\",\n \"yaw\": \"180.000046\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"118.94\",\n \"y\": \"60.19\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"153.94\",\n \"y\": \"26.19\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"127.39\",\n \"y\": \"326.52\",\n \"yaw\": \"180.000046\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"53.12\",\n \"y\": \"330.65\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"88.23\",\n \"y\": \"297.43\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"left\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"373.65\",\n \"y\": \"326.84\",\n \"yaw\": \"180.000046\",\n \"z\": \"1.0\"\n }\n ],\n \"right\": [\n {\n \"pitch\": \"0.0\",\n \"x\": \"300.15\",\n \"y\": \"330.50\",\n \"yaw\": \"0.000031\",\n \"z\": \"1.0\"\n }\n ]\n },\n \"transform\": {\n \"pitch\": \"0\",\n \"x\": \"334.81\",\n \"y\": \"297.3\",\n \"yaw\": \"90\",\n \"z\": \"1.0\"\n }\n },\n {\n \"other_actors\": {\n \"front\": [\n {\n 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\"Scenario8\"\n }\n ]\n }\n ]\n}" }, { "path": "scenario_runner/srunner/data/no_scenarios.json", "content": "{\n\"available_scenarios\": [\n{\n}\n]\n}" }, { "path": "scenario_runner/srunner/data/routes_debug.xml", "content": "\n\n\t \n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\n\n" }, { "path": "scenario_runner/srunner/data/routes_devtest.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "scenario_runner/srunner/data/routes_training.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "scenario_runner/srunner/examples/CatalogExample.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/ChangeLane.xml", "content": "\n\n \n \n\t\n\t\n\t\n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/ChangingWeather.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/ControlLoss.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/CutIn.xml", "content": "\n\n \n \n \n\t \n \n \n \n \n\t \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/CyclistCrossing.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/FollowLeadingVehicle.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/FollowLeadingVehicle.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/FreeRide.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/LaneChangeSimple.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/LeadingVehicle.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/NoSignalJunction.xml", "content": "\n\n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/ObjectCrossing.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/OppositeDirection.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/OscControllerExample.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/PedestrianCrossingFront.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/RunningRedLight.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "scenario_runner/srunner/examples/SignalizedJunctionLeftTurn.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/SignalizedJunctionRightTurn.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/VehicleTurning.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/catalogs/ControllerCatalog.xosc", "content": "\n\n \n \n \n \n \n \n \n \n" }, { "path": "scenario_runner/srunner/examples/catalogs/EnvironmentCatalog.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/catalogs/ManeuverCatalog.xosc", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/catalogs/MiscObjectCatalog.xosc", "content": "\n\n \n \n \n \n \n
\n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/catalogs/PedestrianCatalog.xosc", "content": "\n\n \n \n \n \n
\n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/examples/catalogs/VehicleCatalog.xosc", "content": "\n\n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/metrics/data/CriteriaFilter_criteria.json", "content": "{\n \"RouteCompletionTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 100,\n \"expected_value_acceptable\": null,\n \"actual_value\": 100,\n \"optional\": false,\n \"_wsize\": 2,\n \"_current_index\": 759,\n \"_route_length\": 762,\n \"_accum_meters\": [\n 0.0,\n 1.1180341243743896,\n 2.118034243583679,\n 3.1180343627929688,\n 4.118034482002258,\n 5.118034601211548,\n 6.118034720420837,\n 7.118034839630127,\n 8.118034958839417,\n 9.118035078048706,\n 10.118035197257996,\n 11.118035316467285,\n 12.118035435676575,\n 13.118035554885864,\n 14.118035674095154,\n 15.118035793304443,\n 16.118035912513733,\n 17.408029437065125,\n 17.408029437065125,\n 18.408029556274414,\n 19.408029675483704,\n 20.408029794692993,\n 21.408029913902283,\n 22.60914123058319,\n 23.882590174674988,\n 25.156023859977722,\n 26.42948305606842,\n 27.702926516532898,\n 28.976354002952576,\n 30.22618818283081,\n 31.444236278533936,\n 32.66231644153595,\n 33.88036382198334,\n 35.098384618759155,\n 36.31645858287811,\n 37.5344922542572,\n 38.54053473472595,\n 40.18173956871033,\n 40.18173956871033,\n 41.18173956871033,\n 42.18173956871033,\n 44.18173956871033,\n 45.18173956871033,\n 46.18173956871033,\n 47.18173956871033,\n 48.18173956871033,\n 49.18173956871033,\n 50.18173956871033,\n 51.18173956871033,\n 52.18173956871033,\n 53.18173956871033,\n 54.18173956871033,\n 55.18173956871033,\n 56.18173956871033,\n 57.18173956871033,\n 58.18173956871033,\n 59.18173956871033,\n 60.18173956871033,\n 61.18173956871033,\n 62.18173956871033,\n 63.18173956871033,\n 64.18173956871033,\n 65.18173956871033,\n 66.18173956871033,\n 67.18173956871033,\n 68.18173956871033,\n 69.18173956871033,\n 70.18173956871033,\n 71.18173956871033,\n 72.18173956871033,\n 73.18173956871033,\n 74.18173956871033,\n 75.18173956871033,\n 76.18173956871033,\n 77.18173956871033,\n 78.18173956871033,\n 79.18173956871033,\n 80.18173956871033,\n 81.18173956871033,\n 82.18173956871033,\n 83.18173956871033,\n 84.18173956871033,\n 85.18173956871033,\n 86.18173956871033,\n 87.18173956871033,\n 88.18173956871033,\n 89.18173956871033,\n 90.18173956871033,\n 91.18173956871033,\n 92.18173956871033,\n 93.18173956871033,\n 94.18173956871033,\n 95.18173956871033,\n 96.18173956871033,\n 97.18173956871033,\n 98.18173956871033,\n 99.18173956871033,\n 100.18173956871033,\n 101.18173956871033,\n 102.18173956871033,\n 103.18173956871033,\n 104.18173956871033,\n 105.18173956871033,\n 106.18173956871033,\n 107.18173956871033,\n 108.18173956871033,\n 109.18173956871033,\n 110.1817090511322,\n 111.1817090511322,\n 112.1817090511322,\n 113.1817090511322,\n 114.1817090511322,\n 115.1817090511322,\n 116.1817090511322,\n 117.1817090511322,\n 118.1817090511322,\n 119.1817090511322,\n 120.1817090511322,\n 121.1817090511322,\n 122.1817090511322,\n 123.1817090511322,\n 124.1817090511322,\n 125.1817090511322,\n 126.1817090511322,\n 127.1817090511322,\n 128.1817090511322,\n 129.1817090511322,\n 130.1817090511322,\n 131.1817090511322,\n 132.1817090511322,\n 133.1817090511322,\n 134.1817090511322,\n 135.1817090511322,\n 136.1817090511322,\n 137.1817090511322,\n 138.1817090511322,\n 139.1817090511322,\n 140.1817090511322,\n 141.1817090511322,\n 142.1817090511322,\n 143.1817090511322,\n 144.1817090511322,\n 145.1817090511322,\n 146.1817090511322,\n 147.1817090511322,\n 148.1817090511322,\n 149.1817090511322,\n 150.1817090511322,\n 151.1817090511322,\n 152.1817090511322,\n 153.1817090511322,\n 154.1817090511322,\n 155.1817090511322,\n 156.1817090511322,\n 157.1817090511322,\n 158.1817090511322,\n 159.1817090511322,\n 160.1817090511322,\n 161.1817090511322,\n 162.1817090511322,\n 163.1817090511322,\n 164.1817090511322,\n 165.1817090511322,\n 166.1817090511322,\n 167.1817090511322,\n 168.1817090511322,\n 169.1817090511322,\n 170.1817090511322,\n 171.1817090511322,\n 172.1817090511322,\n 173.1817090511322,\n 174.1817090511322,\n 175.1817090511322,\n 176.1817090511322,\n 177.1817090511322,\n 178.1817090511322,\n 179.1817090511322,\n 180.1817090511322,\n 181.1817090511322,\n 182.1817090511322,\n 183.1817090511322,\n 184.1817090511322,\n 185.1817090511322,\n 186.1817090511322,\n 187.1817090511322,\n 188.1817090511322,\n 189.1817090511322,\n 190.1817090511322,\n 191.1817090511322,\n 192.1817090511322,\n 193.1817090511322,\n 194.1817090511322,\n 195.1817090511322,\n 196.1817090511322,\n 197.1817090511322,\n 198.1817090511322,\n 199.1817090511322,\n 200.1817090511322,\n 201.1817090511322,\n 202.1817090511322,\n 203.1817090511322,\n 204.1817090511322,\n 205.1817090511322,\n 206.1817090511322,\n 207.1817090511322,\n 208.1817090511322,\n 209.1817090511322,\n 210.1817090511322,\n 211.1817090511322,\n 212.1817090511322,\n 213.1817090511322,\n 214.1817090511322,\n 215.1817090511322,\n 216.1817090511322,\n 217.1817090511322,\n 218.1817090511322,\n 219.1817090511322,\n 220.1817090511322,\n 221.1817090511322,\n 222.1817090511322,\n 223.1817090511322,\n 224.1817090511322,\n 225.1817090511322,\n 226.1817090511322,\n 227.1817090511322,\n 228.1817090511322,\n 229.1817090511322,\n 230.1817090511322,\n 231.1817090511322,\n 232.1817090511322,\n 233.1817090511322,\n 234.1817090511322,\n 235.1817090511322,\n 236.1817090511322,\n 237.1817090511322,\n 238.18171668052673,\n 239.18171668052673,\n 240.18171668052673,\n 241.18171668052673,\n 242.18171668052673,\n 243.18171668052673,\n 244.18171668052673,\n 245.18171668052673,\n 246.18171668052673,\n 247.18171668052673,\n 248.18171668052673,\n 249.18171668052673,\n 250.18171668052673,\n 251.18171668052673,\n 252.18171668052673,\n 253.18171668052673,\n 254.18171668052673,\n 255.18171668052673,\n 256.18171668052673,\n 258.18171668052673,\n 259.18171668052673,\n 260.18171668052673,\n 261.18171668052673,\n 262.18171668052673,\n 263.18171668052673,\n 264.43171668052673,\n 264.43171668052673,\n 265.43171668052673,\n 266.4848858118057,\n 267.6947091817856,\n 268.9045317173004,\n 270.11435973644257,\n 271.3241786956787,\n 272.53399670124054,\n 273.74381709098816,\n 274.95476615428925,\n 276.1825375556946,\n 277.41030740737915,\n 278.6380902528763,\n 279.86586582660675,\n 281.093638420105,\n 282.32140839099884,\n 283.54918599128723,\n 284.56516194343567,\n 285.86318135261536,\n 285.86318135261536,\n 286.86318135261536,\n 287.86318135261536,\n 288.86318135261536,\n 289.86318135261536,\n 290.86318135261536,\n 291.86318135261536,\n 292.86318135261536,\n 293.86318135261536,\n 294.86318135261536,\n 295.86318135261536,\n 296.86318135261536,\n 297.86318135261536,\n 298.86318135261536,\n 299.86318135261536,\n 300.86318135261536,\n 301.86318135261536,\n 302.86318135261536,\n 303.86318135261536,\n 304.86318135261536,\n 305.86318135261536,\n 306.86318135261536,\n 307.86318135261536,\n 308.86318135261536,\n 309.86318135261536,\n 310.86318135261536,\n 311.86318135261536,\n 312.86318135261536,\n 313.86318135261536,\n 314.86318135261536,\n 315.86318135261536,\n 316.86318135261536,\n 317.86318135261536,\n 318.86318135261536,\n 319.86318135261536,\n 320.86318135261536,\n 321.86318135261536,\n 322.86318135261536,\n 323.86318135261536,\n 324.86318135261536,\n 325.86318135261536,\n 326.86318135261536,\n 327.86318135261536,\n 328.86318135261536,\n 329.86318135261536,\n 330.86318135261536,\n 331.86318135261536,\n 332.86318135261536,\n 333.8631966114044,\n 334.8631966114044,\n 335.8631966114044,\n 336.8631966114044,\n 337.8631966114044,\n 338.8631966114044,\n 339.8631966114044,\n 340.8631966114044,\n 341.8631966114044,\n 342.8631966114044,\n 343.8631966114044,\n 344.8631966114044,\n 345.8631966114044,\n 346.8631966114044,\n 347.8631966114044,\n 348.8631966114044,\n 349.8631966114044,\n 350.8631966114044,\n 351.8631966114044,\n 352.8631966114044,\n 353.8631966114044,\n 354.8631966114044,\n 355.8631966114044,\n 356.8631966114044,\n 357.8631966114044,\n 358.8631966114044,\n 359.8631966114044,\n 360.8631966114044,\n 361.8631966114044,\n 362.8631966114044,\n 363.8631966114044,\n 364.8631966114044,\n 365.8631966114044,\n 366.8631966114044,\n 367.8631966114044,\n 368.8631966114044,\n 369.8631966114044,\n 370.8631966114044,\n 371.8631966114044,\n 372.8631966114044,\n 373.8631966114044,\n 374.8631966114044,\n 375.8631966114044,\n 376.8631966114044,\n 377.8631966114044,\n 378.8631966114044,\n 379.8631966114044,\n 380.8631966114044,\n 381.8631966114044,\n 382.8631966114044,\n 383.8631966114044,\n 384.8631966114044,\n 385.8631966114044,\n 386.8631966114044,\n 387.8631966114044,\n 388.8631966114044,\n 389.8631966114044,\n 390.8631966114044,\n 391.8631966114044,\n 392.8631966114044,\n 394.8331673145294,\n 394.8331673145294,\n 395.6912509202957,\n 396.5167629122734,\n 397.34227895736694,\n 398.1678013205528,\n 398.9933475255966,\n 399.81885331869125,\n 400.6444185972214,\n 401.4699310064316,\n 402.290342271328,\n 403.09512996673584,\n 403.8999582529068,\n 404.70476245880127,\n 405.509556889534,\n 406.314362347126,\n 407.1191706061363,\n 407.92396891117096,\n 409.09423565864563,\n 409.09423565864563,\n 410.0942357778549,\n 411.0942358970642,\n 412.0942360162735,\n 413.0942361354828,\n 414.0942362546921,\n 415.09423637390137,\n 416.09423649311066,\n 417.09423661231995,\n 418.09423673152924,\n 419.0942368507385,\n 420.0942369699478,\n 421.0942370891571,\n 422.0942372083664,\n 423.0942373275757,\n 424.094237446785,\n 425.094233751297,\n 426.0942338705063,\n 427.0942339897156,\n 428.09423410892487,\n 429.09423422813416,\n 430.09423434734344,\n 431.09423446655273,\n 432.094234585762,\n 433.0942347049713,\n 434.0942348241806,\n 435.0942349433899,\n 436.0942350625992,\n 437.0942351818085,\n 438.09423530101776,\n 439.09423542022705,\n 440.09423553943634,\n 441.09423565864563,\n 442.0942357778549,\n 443.0942358970642,\n 444.0942360162735,\n 445.0942361354828,\n 446.0942362546921,\n 447.0942325592041,\n 448.0942326784134,\n 449.0942327976227,\n 450.09423291683197,\n 451.09423303604126,\n 452.09423315525055,\n 453.0942294597626,\n 454.09422957897186,\n 455.09422969818115,\n 456.09422981739044,\n 457.09423184394836,\n 458.09423196315765,\n 459.09423208236694,\n 460.09423220157623,\n 461.0942323207855,\n 462.0942324399948,\n 463.09423065185547,\n 464.09423077106476,\n 465.09423089027405,\n 466.09423100948334,\n 467.0942311286926,\n 468.0942312479019,\n 469.0942313671112,\n 470.0942314863205,\n 471.0942316055298,\n 472.0942317247391,\n 473.0942327976227,\n 474.09423291683197,\n 475.09423303604126,\n 476.09423315525055,\n 477.0942323207855,\n 478.724232673645,\n 478.724232673645,\n 479.64406257867813,\n 480.41382187604904,\n 481.18358182907104,\n 481.9533463716507,\n 482.7230935692787,\n 483.4928572177887,\n 484.2626251578331,\n 485.03238236904144,\n 485.8388220667839,\n 486.66605401039124,\n 487.49329620599747,\n 488.3205181956291,\n 489.1477633714676,\n 489.9749982357025,\n 490.802244246006,\n 492.28208762407303,\n 492.28208762407303,\n 493.28208762407303,\n 494.28208762407303,\n 495.28208762407303,\n 496.28208762407303,\n 497.28208762407303,\n 498.28208762407303,\n 499.28208762407303,\n 500.28208762407303,\n 501.28208762407303,\n 502.28208762407303,\n 503.28208762407303,\n 504.28208762407303,\n 505.28208762407303,\n 506.28208762407303,\n 507.28208762407303,\n 508.28208762407303,\n 509.28208762407303,\n 510.28208762407303,\n 511.28208762407303,\n 512.282087624073,\n 513.282087624073,\n 514.282087624073,\n 515.282087624073,\n 516.282087624073,\n 517.282087624073,\n 518.282087624073,\n 519.282087624073,\n 520.282087624073,\n 521.282087624073,\n 522.282087624073,\n 523.282087624073,\n 524.282087624073,\n 525.282087624073,\n 526.282087624073,\n 527.282087624073,\n 528.282087624073,\n 529.282087624073,\n 530.282087624073,\n 531.282087624073,\n 532.282087624073,\n 533.282087624073,\n 534.282087624073,\n 535.282087624073,\n 536.282087624073,\n 537.282087624073,\n 538.282087624073,\n 539.282087624073,\n 540.282087624073,\n 541.282087624073,\n 542.282087624073,\n 543.282087624073,\n 544.282087624073,\n 545.282087624073,\n 546.282087624073,\n 547.282087624073,\n 548.282087624073,\n 549.282087624073,\n 550.282087624073,\n 551.282087624073,\n 552.282087624073,\n 553.282087624073,\n 554.2821028828621,\n 555.2821028828621,\n 556.2821028828621,\n 557.2821028828621,\n 558.2821028828621,\n 559.2821028828621,\n 560.2821028828621,\n 561.2821028828621,\n 562.2821028828621,\n 563.2821028828621,\n 564.2821028828621,\n 565.2821028828621,\n 566.2821028828621,\n 567.2821028828621,\n 568.2821028828621,\n 569.2833998799324,\n 570.2833999991417,\n 571.283400118351,\n 572.2834002375603,\n 573.2834003567696,\n 574.2834004759789,\n 575.2834005951881,\n 576.2834007143974,\n 577.2834008336067,\n 578.283400952816,\n 579.2834010720253,\n 580.2834011912346,\n 581.2834013104439,\n 582.2834014296532,\n 583.2834015488625,\n 584.2834016680717,\n 585.283401787281,\n 586.2834019064903,\n 587.2834020256996,\n 588.2834021449089,\n 589.2834022641182,\n 590.2834023833275,\n 591.2834025025368,\n 592.2834026217461,\n 593.2834027409554,\n 594.2834028601646,\n 595.2834029793739,\n 596.2834030985832,\n 597.2834032177925,\n 598.2834033370018,\n 599.2834034562111,\n 600.2834035754204,\n 601.2834036946297,\n 602.283403813839,\n 603.2834039330482,\n 604.2834040522575,\n 605.2834041714668,\n 606.2834042906761,\n 607.2834044098854,\n 608.2834045290947,\n 609.283404648304,\n 610.2834047675133,\n 611.2834048867226,\n 612.2834050059319,\n 613.2834051251411,\n 614.2834052443504,\n 615.2834053635597,\n 616.283405482769,\n 617.2834056019783,\n 618.2834057211876,\n 619.2834058403969,\n 620.2834059596062,\n 621.2834060788155,\n 622.2834061980247,\n 623.283406317234,\n 624.2834064364433,\n 625.2834065556526,\n 626.2834066748619,\n 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\"_terminate_on_failure\": false,\n \"test_status\": \"FAILURE\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 5,\n \"optional\": false,\n \"_collision_sensor\": null,\n \"other_actor\": null,\n \"other_actor_type\": null,\n \"registered_collisions\": [],\n \"last_id\": null,\n \"collision_time\": 70.1000010445714,\n \"terminate_on_failure\": false\n },\n \"InRouteTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": true,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 0,\n \"optional\": false,\n \"list_traffic_events\": [],\n \"_offroad_max\": 30,\n \"_offroad_min\": 15.0,\n \"_route_length\": 762,\n \"_current_index\": 760,\n \"_out_route_distance\": 0,\n \"_in_safe_route\": true,\n \"_accum_meters\": [\n 0.0,\n 1.1180341243743896,\n 2.118034243583679,\n 3.1180343627929688,\n 4.118034482002258,\n 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704.283408343792,\n 705.2834084630013,\n 706.2834085822105,\n 707.2834087014198,\n 708.2834088206291,\n 709.2834089398384,\n 710.2834090590477,\n 711.283409178257,\n 712.2834016680717,\n 713.283401787281,\n 714.2834019064903,\n 715.2834020256996,\n 716.2834021449089,\n 717.2834022641182,\n 718.2834023833275,\n 719.2834025025368,\n 720.2834026217461,\n 721.2834027409554,\n 722.2815870046616,\n 723.2815870046616,\n 724.2815870046616,\n 725.2815870046616,\n 726.2815870046616,\n 727.2815870046616,\n 728.2815870046616,\n 729.2815870046616,\n 730.2815870046616,\n 731.2815870046616,\n 732.2815870046616,\n 733.2815870046616,\n 734.2815870046616,\n 735.2815870046616,\n 736.2815870046616,\n 737.2815870046616,\n 738.2815870046616,\n 739.2815870046616,\n 740.2815870046616,\n 741.2815870046616,\n 742.2815870046616,\n 743.2815870046616,\n 744.2815870046616,\n 745.2815870046616,\n 746.2815870046616,\n 747.2815870046616,\n 748.2815870046616,\n 749.2815870046616,\n 750.2815870046616,\n 751.2815870046616,\n 752.2815870046616,\n 753.2815870046616,\n 754.2815870046616,\n 755.2815870046616,\n 756.2815870046616,\n 757.2815870046616,\n 758.2815870046616,\n 759.2815870046616,\n 760.2815870046616\n ],\n \"terminate_on_failure\": false\n },\n \"OutsideRouteLanesTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"FAILURE\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 35.22,\n \"optional\": false,\n \"_current_index\": 760,\n \"_route_length\": 762,\n \"_outside_lane_active\": true,\n \"_wrong_lane_active\": true,\n \"_last_road_id\": 15,\n \"_last_lane_id\": -1,\n \"_total_distance\": 759.2815870046616,\n \"_wrong_distance\": 0,\n \"terminate_on_failure\": false\n },\n \"RunningRedLightTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"FAILURE\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 3,\n \"optional\": false,\n \"_last_red_light_id\": 288,\n \"debug\": false,\n \"terminate_on_failure\": false\n },\n \"RunningStopTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 0,\n \"optional\": false,\n \"list_traffic_events\": [],\n \"_list_stop_signs\": [],\n \"_target_stop_sign\": null,\n \"_stop_completed\": false,\n \"_affected_by_stop\": false,\n \"terminate_on_failure\": false\n },\n \"ActorSpeedAboveThresholdTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": true,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 0,\n \"optional\": false,\n \"list_traffic_events\": [],\n \"_speed_threshold\": 0.1,\n \"_below_threshold_max_time\": 90.0,\n \"_time_last_valid_state\": 70.80000105500221,\n \"terminate_on_failure\": false\n }\n}" }, { "path": "scenario_runner/srunner/metrics/data/DistanceBetweenVehicles_criteria.json", "content": "{\n \"CollisionTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 0,\n \"optional\": false,\n \"list_traffic_events\": [],\n \"_collision_sensor\": null,\n \"other_actor\": null,\n \"other_actor_type\": null,\n \"registered_collisions\": [],\n \"last_id\": null,\n \"collision_time\": null,\n \"terminate_on_failure\": false\n }\n}" }, { "path": "scenario_runner/srunner/metrics/data/DistanceToLaneCenter_criteria.json", "content": "{\n \"CollisionTest\": {\n \"children\": [],\n \"feedback_message\": \"\",\n \"blackbox_level\": 4,\n \"_terminate_on_failure\": false,\n \"test_status\": \"SUCCESS\",\n \"expected_value_success\": 0,\n \"expected_value_acceptable\": null,\n \"actual_value\": 0,\n \"optional\": false,\n \"list_traffic_events\": [],\n \"_collision_sensor\": null,\n \"other_actor\": null,\n \"other_actor_type\": null,\n \"registered_collisions\": [],\n \"last_id\": null,\n \"collision_time\": null,\n \"terminate_on_failure\": false\n }\n}" }, { "path": "scenario_runner/srunner/metrics/examples/basic_metric.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provide BasicMetric, the basic class of all the metrics.\n\"\"\"\n\nclass BasicMetric(object):\n \"\"\"\n Base class of all the metrics.\n \"\"\"\n\n def __init__(self, town_map, log, criteria=None):\n \"\"\"\n Initialization of the metric class. This calls the metrics log and creates the metrics\n\n Args:\n town_map (carla.Map): Map of the simulation. Used to access the Waypoint API.\n log (srunner.metrics.tools.Metricslog): instance of a class used to access the recorder information\n criteria (dict): list of dictionaries with all the criteria information\n \"\"\"\n\n # Create the metrics of the simulation. This part is left to the user\n self._create_metric(town_map, log, criteria)\n\n def _create_metric(self, town_map, log, criteria):\n \"\"\"\n Pure virtual function to setup the metrics by the user.\n\n Args:\n town_map (carla.Map): Map of the simulation. Used to access the Waypoint API.\n log (srunner.metrics.tools.Metricslog): instance of a class used to access the recorder information\n criteria (dict): dictionaries with all the criteria information\n \"\"\"\n raise NotImplementedError(\n \"This function should be re-implemented by all metrics\"\n \"If this error becomes visible the class hierarchy is somehow broken\")\n" }, { "path": "scenario_runner/srunner/metrics/examples/criteria_filter.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis metric filters the useful information of the criteria (sucess / fail ...),\nand dump it into a json file\n\nIt is meant to serve as an example of how to use the criteria\n\"\"\"\n\nimport json\n\nfrom srunner.metrics.examples.basic_metric import BasicMetric\n\n\nclass CriteriaFilter(BasicMetric):\n \"\"\"\n Metric class CriteriaFilter\n \"\"\"\n\n def _create_metric(self, town_map, log, criteria):\n \"\"\"\n Implementation of the metric. This is an example to show how to use the criteria\n \"\"\"\n\n ### Parse the criteria information, filtering only the useful information, and dump it into a json ###\n\n results = {}\n for criterion_name in criteria:\n criterion = criteria[criterion_name]\n results.update({criterion_name:\n {\n \"test_status\": criterion[\"test_status\"],\n \"actual_value\": criterion[\"actual_value\"],\n \"success_value\": criterion[\"expected_value_success\"]\n }\n }\n )\n\n with open('srunner/metrics/data/CriteriaFilter_results.json', 'w') as fw:\n json.dump(results, fw, sort_keys=False, indent=4)\n" }, { "path": "scenario_runner/srunner/metrics/examples/distance_between_vehicles.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis metric calculates the distance between the ego vehicle and\nanother actor, dumping it to a json file.\n\nIt is meant to serve as an example of how to use the information from\nthe recorder\n\"\"\"\n\nimport math\nimport matplotlib.pyplot as plt\n\nfrom srunner.metrics.examples.basic_metric import BasicMetric\n\n\nclass DistanceBetweenVehicles(BasicMetric):\n \"\"\"\n Metric class DistanceBetweenVehicles\n \"\"\"\n\n def _create_metric(self, town_map, log, criteria):\n \"\"\"\n Implementation of the metric. This is an example to show how to use the recorder,\n accessed via the log.\n \"\"\"\n\n # Get the ID of the two vehicles\n ego_id = log.get_ego_vehicle_id()\n adv_id = log.get_actor_ids_with_role_name(\"scenario\")[0] # Could have also used its type_id\n\n dist_list = []\n frames_list = []\n\n # Get the frames both actors were alive\n start_ego, end_ego = log.get_actor_alive_frames(ego_id)\n start_adv, end_adv = log.get_actor_alive_frames(adv_id)\n start = max(start_ego, start_adv)\n end = min(end_ego, end_adv)\n\n # Get the distance between the two\n for i in range(start, end):\n\n # Get the transforms\n ego_location = log.get_actor_transform(ego_id, i).location\n adv_location = log.get_actor_transform(adv_id, i).location\n\n # Filter some points for a better graph\n if adv_location.z < -10:\n continue\n\n dist_v = ego_location - adv_location\n dist = math.sqrt(dist_v.x * dist_v.x + dist_v.y * dist_v.y + dist_v.z * dist_v.z)\n\n dist_list.append(dist)\n frames_list.append(i)\n\n # Use matplotlib to show the results\n plt.plot(frames_list, dist_list)\n plt.ylabel('Distance [m]')\n plt.xlabel('Frame number')\n plt.title('Distance between the ego vehicle and the adversary over time')\n plt.show()\n" }, { "path": "scenario_runner/srunner/metrics/examples/distance_to_lane_center.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis metric calculates the distance between the ego vehicle and\nthe center of the lane, dumping it to a json file.\n\nIt is meant to serve as an example of how to use the map API\n\"\"\"\n\nimport math\nimport json\n\nfrom srunner.metrics.examples.basic_metric import BasicMetric\n\n\nclass DistanceToLaneCenter(BasicMetric):\n \"\"\"\n Metric class DistanceToLaneCenter\n \"\"\"\n\n def _create_metric(self, town_map, log, criteria):\n \"\"\"\n Implementation of the metric.\n \"\"\"\n\n # Get ego vehicle id\n ego_id = log.get_ego_vehicle_id()\n\n dist_list = []\n frames_list = []\n\n # Get the frames the ego actor was alive and its transforms\n start, end = log.get_actor_alive_frames(ego_id)\n\n # Get the projected distance vector to the center of the lane\n for i in range(start, end + 1):\n\n ego_location = log.get_actor_transform(ego_id, i).location\n ego_waypoint = town_map.get_waypoint(ego_location)\n\n # Get the distance vector and project it\n a = ego_location - ego_waypoint.transform.location # Ego to waypoint vector\n b = ego_waypoint.transform.get_right_vector() # Waypoint perpendicular vector\n b_norm = math.sqrt(b.x * b.x + b.y * b.y + b.z * b.z)\n\n ab_dot = a.x * b.x + a.y * b.y + a.z * b.z\n dist_v = ab_dot/(b_norm*b_norm)*b\n dist = math.sqrt(dist_v.x * dist_v.x + dist_v.y * dist_v.y + dist_v.z * dist_v.z)\n\n # Get the sign of the distance (left side is positive)\n c = ego_waypoint.transform.get_forward_vector() # Waypoint forward vector\n ac_cross = c.x * a.y - c.y * a.x\n if ac_cross < 0:\n dist *= -1\n\n dist_list.append(dist)\n frames_list.append(i)\n\n # Save the results to a file\n results = {'frames': frames_list, 'distance': dist_list}\n with open('srunner/metrics/data/DistanceToLaneCenter_results.json', 'w') as fw:\n json.dump(results, fw, sort_keys=False, indent=4)\n" }, { "path": "scenario_runner/srunner/metrics/tools/metrics_log.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nSupport class of the MetricsManager to query the information available\nto the metrics.\n\nIt also provides a series of functions to help the user querry\nspecific information\n\"\"\"\n\nimport fnmatch\nfrom srunner.metrics.tools.metrics_parser import MetricsParser\n\nclass MetricsLog(object): # pylint: disable=too-many-public-methods\n \"\"\"\n Utility class to query the log.\n \"\"\"\n\n def __init__(self, recorder):\n \"\"\"\n Initializes the log class and parses it to extract the dictionaries.\n \"\"\"\n # Parse the information\n parser = MetricsParser(recorder)\n self._simulation, self._actors, self._frames = parser.parse_recorder_info()\n\n ### Functions used to get general info of the simulation ###\n def get_actor_collisions(self, actor_id):\n \"\"\"\n Returns a dict where the keys are the frame number and the values,\n a list of actor ids the actor collided with.\n\n Args:\n actor_id (int): ID of the actor.\n \"\"\"\n actor_collisions = {}\n\n for i, frame in enumerate(self._frames):\n collisions = frame[\"events\"][\"collisions\"]\n\n if actor_id in collisions:\n actor_collisions.update({i: collisions[actor_id]})\n\n return actor_collisions\n\n def get_total_frame_count(self):\n \"\"\"\n Returns an int with the total amount of frames the simulation lasted.\n \"\"\"\n\n return self._simulation[\"total_frames\"]\n\n def get_elapsed_time(self, frame):\n \"\"\"\n Returns a float with the elapsed time of a specific frame.\n \"\"\"\n\n return self._frames[frame][\"frame\"][\"elapsed_time\"]\n\n def get_delta_time(self, frame):\n \"\"\"\n Returns a float with the delta time of a specific frame.\n \"\"\"\n\n return self._frames[frame][\"frame\"][\"delta_time\"]\n\n def get_platform_time(self, frame):\n \"\"\"\n Returns a float with the platform time time of a specific frame.\n \"\"\"\n\n return self._frames[frame][\"frame\"][\"platform_time\"]\n\n ### Functions used to get info about the actors ###\n def get_ego_vehicle_id(self):\n \"\"\"\n Returns the id of the ego vehicle.\n \"\"\"\n return self.get_actor_ids_with_role_name(\"hero\")[0]\n\n def get_actor_ids_with_role_name(self, role_name):\n \"\"\"\n Returns a list of actor ids that match the given role_name.\n\n Args:\n role_name (str): string with the desired role_name to filter the actors.\n \"\"\"\n actor_list = []\n\n for actor_id in self._actors:\n actor = self._actors[actor_id]\n if \"role_name\" in actor and actor[\"role_name\"] == role_name:\n actor_list.append(actor_id)\n\n return actor_list\n\n def get_actor_ids_with_type_id(self, type_id):\n \"\"\"\n Returns a list of actor ids that match the given type_id, matching fnmatch standard.\n\n Args:\n type_id (str): string with the desired type id to filter the actors.\n \"\"\"\n actor_list = []\n\n for actor_id in self._actors:\n actor = self._actors[actor_id]\n if \"type_id\" in actor and fnmatch.fnmatch(actor[\"type_id\"], type_id):\n actor_list.append(actor_id)\n\n return actor_list\n\n def get_actor_attributes(self, actor_id):\n \"\"\"\n Returns a dictionary with all the attributes of an actor.\n\n Args:\n actor_id (int): ID of the actor.\n \"\"\"\n if actor_id in self._actors:\n return self._actors[actor_id]\n\n return None\n\n def get_actor_bounding_box(self, actor_id):\n \"\"\"\n Returns the bounding box of the specified actor\n\n Args:\n actor_id (int): Id of the actor\n \"\"\"\n\n if actor_id in self._actors:\n if \"bounding_box\" in self._actors[actor_id]:\n return self._actors[actor_id][\"bounding_box\"]\n return None\n\n return None\n\n def get_traffic_light_trigger_volume(self, traffic_light_id):\n \"\"\"\n Returns the trigger volume of the specified traffic light\n\n Args:\n actor_id (int): Id of the traffic light\n \"\"\"\n\n if traffic_light_id in self._actors:\n if \"trigger_volume\" in self._actors[traffic_light_id]:\n return self._actors[traffic_light_id][\"trigger_volume\"]\n return None\n\n return None\n\n def get_actor_alive_frames(self, actor_id):\n \"\"\"\n Returns a tuple with the first and last frame an actor was alive.\n It is important to note that frames start at 1, not 0.\n\n Args:\n actor_id (int): Id of the actor\n \"\"\"\n\n if actor_id in self._actors:\n\n actor_info = self._actors[actor_id]\n first_frame = actor_info[\"created\"]\n if \"destroyed\" in actor_info:\n last_frame = actor_info[\"destroyed\"] - 1\n else:\n last_frame = self.get_total_frame_count()\n\n return first_frame, last_frame\n\n return None, None\n\n ### Functions used to get the actor states ###\n def _get_actor_state(self, actor_id, state, frame):\n \"\"\"\n Given an actor id, returns the specific variable of that actor at a given frame.\n Returns None if the actor_id or the state are missing.\n\n Args:\n actor_id (int): Id of the actor to be checked.\n frame: (int): frame number of the simulation.\n attribute (str): name of the actor's attribute to be returned.\n \"\"\"\n frame_state = self._frames[frame - 1][\"actors\"]\n\n # Check if the actor exists\n if actor_id in frame_state:\n\n # Check if the state exists\n if state not in frame_state[actor_id]:\n return None\n\n state_info = frame_state[actor_id][state]\n return state_info\n\n return None\n\n def _get_all_actor_states(self, actor_id, state, first_frame=None, last_frame=None):\n \"\"\"\n Given an actor id, returns a list of the specific variable of that actor during\n a frame interval. Some elements might be None.\n\n By default, first_frame and last_frame are the start and end of the simulation, respectively.\n\n Args:\n actor_id (int): ID of the actor.\n attribute: name of the actor's attribute to be returned.\n first_frame (int): First frame checked. By default, 0.\n last_frame (int): Last frame checked. By default, max number of frames.\n \"\"\"\n if first_frame is None:\n first_frame = 1\n if last_frame is None:\n last_frame = self.get_total_frame_count()\n\n state_list = []\n\n for frame_number in range(first_frame, last_frame + 1):\n state_info = self._get_actor_state(actor_id, state, frame_number)\n state_list.append(state_info)\n\n return state_list\n\n def _get_states_at_frame(self, frame, state, actor_list=None):\n \"\"\"\n Returns a dict where the keys are the frame number, and the values are the\n carla.Transform of the actor at the given frame.\n\n By default, all actors will be considered.\n \"\"\"\n states = {}\n actor_info = self._frames[frame][\"actors\"]\n\n for actor_id in actor_info:\n if not actor_list:\n _state = self._get_actor_state(actor_id, state, frame)\n if _state:\n states.update({actor_id: _state})\n elif actor_id in actor_list:\n _state = self._get_actor_state(actor_id, state, frame)\n states.update({actor_id: _state})\n\n return states\n\n # Transforms\n def get_actor_transform(self, actor_id, frame):\n \"\"\"\n Returns the transform of the actor at a given frame.\n \"\"\"\n return self._get_actor_state(actor_id, \"transform\", frame)\n\n def get_all_actor_transforms(self, actor_id, first_frame=None, last_frame=None):\n \"\"\"\n Returns a list with all the transforms of the actor at the frame interval.\n \"\"\"\n return self._get_all_actor_states(actor_id, \"transform\", first_frame, last_frame)\n\n def get_actor_transforms_at_frame(self, frame, actor_list=None):\n \"\"\"\n Returns a dictionary {int - carla.Transform} with the actor ID and transform\n at a given frame of all the actors at actor_list.\n \"\"\"\n return self._get_states_at_frame(frame, \"transform\", actor_list)\n\n # Velocities\n def get_actor_velocity(self, actor_id, frame):\n \"\"\"\n Returns the velocity of the actor at a given frame.\n \"\"\"\n return self._get_actor_state(actor_id, \"velocity\", frame)\n\n def get_all_actor_velocities(self, actor_id, first_frame=None, last_frame=None):\n \"\"\"\n Returns a list with all the velocities of the actor at the frame interval.\n \"\"\"\n return self._get_all_actor_states(actor_id, \"velocity\", first_frame, last_frame)\n\n def get_actor_velocities_at_frame(self, frame, actor_list=None):\n \"\"\"\n Returns a dictionary {int - carla.Vector3D} with the actor ID and velocity\n at a given frame of all the actors at actor_list.\n \"\"\"\n return self._get_states_at_frame(frame, \"velocity\", actor_list)\n\n # Angular velocities\n def get_actor_angular_velocity(self, actor_id, frame):\n \"\"\"\n Returns the angular velocity of the actor at a given frame.\n \"\"\"\n return self._get_actor_state(actor_id, \"angular_velocity\", frame)\n\n def get_all_actor_angular_velocities(self, actor_id, first_frame=None, last_frame=None):\n \"\"\"\n Returns a list with all the angular velocities of the actor at the frame interval.\n \"\"\"\n return self._get_all_actor_states(actor_id, \"angular_velocity\", first_frame, last_frame)\n\n def get_actor_angular_velocities_at_frame(self, frame, actor_list=None):\n \"\"\"\n Returns a dictionary {int - carla.Vector3D} with the actor ID and angular velocity\n at a given frame of all the actors at actor_list.\n \"\"\"\n return self._get_states_at_frame(frame, \"angular_velocity\", actor_list)\n\n # Acceleration\n def get_actor_acceleration(self, actor_id, frame):\n \"\"\"\n Returns the acceleration of the actor at a given frame.\n \"\"\"\n return self._get_actor_state(actor_id, \"acceleration\", frame)\n\n def get_all_actor_accelerations(self, actor_id, first_frame=None, last_frame=None):\n \"\"\"\n Returns a list with all the accelerations of the actor at the frame interval.\n \"\"\"\n return self._get_all_actor_states(actor_id, \"acceleration\", first_frame, last_frame)\n\n def get_actor_accelerations_at_frame(self, frame, actor_list=None):\n \"\"\"\n Returns a dictionary {int - carla.Vector3D} with the actor ID and angular velocity\n at a given frame of all the actors at actor_list.\n \"\"\"\n return self._get_states_at_frame(frame, \"acceleration\", actor_list)\n\n # Controls\n def get_vehicle_control(self, vehicle_id, frame):\n \"\"\"\n Returns the control of the vehicle at a given frame.\n \"\"\"\n return self._get_actor_state(vehicle_id, \"control\", frame)\n\n def get_vehicle_physics_control(self, vehicle_id, frame):\n \"\"\"\n Returns the carla.VehiclePhysicsControl of a vehicle at a given frame.\n Returns None if the id can't be found.\n \"\"\"\n\n for i in range(frame - 1, -1, -1): # Go backwards from the frame until 0\n physics_info = self._frames[i][\"events\"][\"physics_control\"]\n\n if vehicle_id in physics_info:\n return physics_info[vehicle_id]\n\n return None\n\n def get_walker_speed(self, walker_id, frame):\n \"\"\"\n Returns the speed of the walker at a specific frame.\n \"\"\"\n return self._get_actor_state(walker_id, \"speed\", frame)\n\n # Traffic lights\n def get_traffic_light_state(self, traffic_light_id, frame):\n \"\"\"\n Returns the state of the traffic light at a specific frame.\n \"\"\"\n return self._get_actor_state(traffic_light_id, \"state\", frame)\n\n def is_traffic_light_frozen(self, traffic_light_id, frame):\n \"\"\"\n Returns whether or not the traffic light is frozen at a specific frame.\n \"\"\"\n return self._get_actor_state(traffic_light_id, \"frozen\", frame)\n\n def get_traffic_light_elapsed_time(self, traffic_light_id, frame):\n \"\"\"\n Returns the elapsed time of the traffic light at a specific frame.\n \"\"\"\n return self._get_actor_state(traffic_light_id, \"elapsed_time\", frame)\n\n def get_traffic_light_state_time(self, traffic_light_id, state, frame):\n \"\"\"\n Returns the state time of the traffic light at a specific frame.\n Returns None if the id can't be found.\n \"\"\"\n\n for i in range(frame - 1, -1, -1): # Go backwards from the frame until 0\n state_times_info = self._frames[i][\"events\"][\"traffic_light_state_time\"]\n\n if traffic_light_id in state_times_info:\n states = state_times_info[traffic_light_id]\n if state in states:\n return state_times_info[traffic_light_id][state]\n\n return None\n\n # Vehicle lights\n def get_vehicle_lights(self, vehicle_id, frame):\n \"\"\"\n Returns the vehicle lights of the vehicle at a specific frame.\n \"\"\"\n return self._get_actor_state(vehicle_id, \"lights\", frame)\n\n def is_vehicle_light_active(self, light, vehicle_id, frame):\n \"\"\"\n Returns the elapsed time of the traffic light at a specific frame.\n \"\"\"\n lights = self.get_vehicle_lights(vehicle_id, frame)\n\n if light in lights:\n return True\n\n return False\n\n # Scene lights\n def get_scene_light_state(self, light_id, frame):\n \"\"\"\n Returns the state of the scene light at a specific frame.\n Returns None if the id can't be found.\n \"\"\"\n\n for i in range(frame - 1, -1, -1): # Go backwards from the frame until 0\n scene_lights_info = self._frames[i][\"events\"][\"scene_lights\"]\n\n if light_id in scene_lights_info:\n return scene_lights_info[light_id]\n\n return None\n" }, { "path": "scenario_runner/srunner/metrics/tools/metrics_parser.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de\n# Barcelona (UAB).\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nSupport class of the MetricsManager to parse the information of\nthe CARLA recorder into a readable dictionary\n\"\"\"\n\nimport carla\n\n\ndef parse_actor(info):\n \"\"\"\n Returns a dictionary with the basic actor information\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n\n actor = {\n \"type_id\": info[2],\n \"location\": carla.Location(\n float(info[5][1:-1]) / 100,\n float(info[6][:-1]) / 100,\n float(info[7][:-1]) / 100\n )\n }\n\n return actor\n\n\ndef parse_transform(info):\n \"\"\"\n Parses a list into a carla.Transform\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n transform = carla.Transform(\n carla.Location(\n float(info[3][1:-1]) / 100,\n float(info[4][:-1]) / 100,\n float(info[5][:-1]) / 100,\n ),\n carla.Rotation(\n float(info[8][:-1]), # pitch\n float(info[9][:-1]), # yaw\n float(info[7][1:-1]) # roll\n )\n )\n\n return transform\n\n\ndef parse_control(info):\n \"\"\"\n Parses a list into a carla.VehicleControl\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n control = carla.VehicleControl(\n float(info[5]), # throttle\n float(info[3]), # steer\n float(info[7]), # brake\n bool(int(info[9])), # hand_brake\n int(info[11]) < 0, # reverse\n False, # manual_gear_shift\n int(info[11]), # gear\n )\n\n return control\n\n\ndef parse_vehicle_lights(info):\n \"\"\"\n Parses a list into a carla.VehicleLightState\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n srt_to_vlight = {\n \"None\": carla.VehicleLightState.NONE,\n \"Position\": carla.VehicleLightState.Position,\n \"LowBeam\": carla.VehicleLightState.LowBeam,\n \"HighBeam\": carla.VehicleLightState.HighBeam,\n \"Brake\": carla.VehicleLightState.Brake,\n \"RightBlinker\": carla.VehicleLightState.RightBlinker,\n \"LeftBlinker\": carla.VehicleLightState.LeftBlinker,\n \"Reverse\": carla.VehicleLightState.Reverse,\n \"Fog\": carla.VehicleLightState.Fog,\n \"Interior\": carla.VehicleLightState.Interior,\n \"Special1\": carla.VehicleLightState.Special1,\n \"Special2\": carla.VehicleLightState.Special2,\n }\n\n lights = []\n for i in range(2, len(info)):\n lights.append(srt_to_vlight[info[i]])\n\n return lights\n\n\ndef parse_traffic_light(info):\n \"\"\"\n Parses a list into a dictionary with all the traffic light's information\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n number_to_state = {\n \"0\": carla.TrafficLightState.Red,\n \"1\": carla.TrafficLightState.Yellow,\n \"2\": carla.TrafficLightState.Green,\n \"3\": carla.TrafficLightState.Off,\n \"4\": carla.TrafficLightState.Unknown,\n }\n\n traffic_light = {\n \"state\": number_to_state[info[3]],\n \"frozen\": bool(int(info[5])),\n \"elapsed_time\": float(info[7]),\n }\n\n return traffic_light\n\n\ndef parse_velocity(info):\n \"\"\"\n Parses a list into a carla.Vector3D with the velocity\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n velocity = carla.Vector3D(\n float(info[3][1:-1]),\n float(info[4][:-1]),\n float(info[5][:-1])\n )\n\n return velocity\n\n\ndef parse_angular_velocity(info):\n \"\"\"\n Parses a list into a carla.Vector3D with the angular velocity\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n velocity = carla.Vector3D(\n float(info[7][1:-1]),\n float(info[8][:-1]),\n float(info[9][:-1])\n )\n\n return velocity\n\n\ndef parse_scene_lights(info):\n \"\"\"\n Parses a list into a carla.VehicleLightState\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n\n red = int(float(info[7][1:-1]) * 255)\n green = int(float(info[8][:-1]) * 255)\n blue = int(float(info[9][:-1]) * 255)\n\n scene_light = carla.LightState(\n int(float(info[5])),\n carla.Color(red, green, blue),\n carla.LightGroup.NONE,\n bool(info[3])\n )\n\n return scene_light\n\n\ndef parse_bounding_box(info):\n \"\"\"\n Parses a list into a carla.BoundingBox\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n location = carla.Location(\n float(info[3][1:-1])/100,\n float(info[4][:-1])/100,\n float(info[5][:-1])/100,\n )\n\n extent = carla.Vector3D(\n float(info[7][1:-1])/100,\n float(info[8][:-1])/100,\n float(info[9][:-1])/100,\n )\n\n bbox = carla.BoundingBox(location, extent)\n\n return bbox\n\n\ndef parse_state_times(info):\n \"\"\"\n Parses a list into a dict containing the state times of the traffic lights\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n\n state_times = {\n carla.TrafficLightState.Green: float(info[3]),\n carla.TrafficLightState.Yellow: float(info[5]),\n carla.TrafficLightState.Red: float(info[7]),\n }\n\n return state_times\n\n\ndef parse_vector_list(info):\n \"\"\"\n Parses a list of string into a list of Vector2D\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n vector_list = []\n for i in range(0, len(info), 2):\n vector = carla.Vector2D(\n float(info[i][1:-1]),\n float(info[i+1][:-1]),\n )\n vector_list.append(vector)\n\n return vector_list\n\n\ndef parse_gears_control(info):\n \"\"\"\n Parses a list into a GearPhysicsControl\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n gears_control = carla.GearPhysicsControl(\n float(info[3]),\n float(info[5]),\n float(info[7]),\n )\n\n return gears_control\n\n\ndef parse_wheels_control(info):\n \"\"\"\n Parses a list into a WheelsPhysicsControl\n\n Args:\n info (list): list corresponding to a row of the recorder\n \"\"\"\n gears_control = carla.WheelPhysicsControl(\n float(info[3]),\n float(info[5]),\n float(info[7]),\n float(info[9]),\n float(info[11]),\n float(info[13]),\n carla.Vector3D()\n )\n\n return gears_control\n\n\nclass MetricsParser(object):\n \"\"\"\n Class used to parse the CARLA recorder into readable information\n \"\"\"\n\n def __init__(self, recorder_info):\n\n self.recorder_info = recorder_info\n self.frame_list = None\n self.frame_row = None\n self.i = 0\n\n def get_row_elements(self, indent_num, split_string):\n \"\"\"\n returns a list with the elements of the row\n \"\"\"\n return self.frame_row[indent_num:].split(split_string)\n\n def next_row(self):\n \"\"\"\n Gets the next row of the recorder\n \"\"\"\n self.i += 1\n self.frame_row = self.frame_list[self.i]\n\n def parse_recorder_info(self):\n \"\"\"\n Parses the recorder into readable information.\n\n Args:\n recorder_info (str): string given by the recorder\n \"\"\"\n\n # Divide it into frames\n recorder_list = self.recorder_info.split(\"Frame\")\n\n # Get general information\n header = recorder_list[0].split(\"\\n\")\n sim_map = header[1][5:]\n sim_date = header[2][6:]\n\n annex = recorder_list[-1].split(\"\\n\")\n sim_frames = int(annex[0][3:])\n sim_duration = float(annex[1][10:-8])\n\n recorder_list = recorder_list[1:-1]\n\n simulation_info = {\n \"map\": sim_map,\n \"date:\": sim_date,\n \"total_frames\": sim_frames,\n \"duration\": sim_duration\n }\n\n actors_info = {}\n frames_info = []\n\n for frame in recorder_list:\n\n # Divide the frame in lines\n self.frame_list = frame.split(\"\\n\")\n\n # Get the general frame information\n frame_info = self.frame_list[0].split(\" \")\n frame_number = int(frame_info[1])\n frame_time = float(frame_info[3])\n\n try:\n prev_frame = frames_info[frame_number - 2]\n prev_time = prev_frame[\"frame\"][\"elapsed_time\"]\n delta_time = round(frame_time - prev_time, 6)\n except IndexError:\n delta_time = 0\n\n # Variable to store all the information about the frame\n frame_state = {\n \"frame\": {\n \"elapsed_time\": frame_time,\n \"delta_time\": delta_time,\n \"platform_time\": None\n },\n \"actors\": {},\n \"events\":{\n \"scene_lights\": {},\n \"physics_control\": {},\n \"traffic_light_state_time\": {},\n \"collisions\": {}\n }\n }\n\n # Loop through all the other rows.\n self.i = 0\n self.next_row()\n\n while self.frame_row.startswith(' Create') or self.frame_row.startswith(' '):\n\n if self.frame_row.startswith(' Create'):\n elements = self.get_row_elements(1, \" \")\n actor_id = int(elements[1][:-1])\n\n actor = parse_actor(elements)\n actors_info.update({actor_id: actor})\n actors_info[actor_id].update({\"created\": frame_number})\n else:\n elements = self.get_row_elements(2, \" = \")\n actors_info[actor_id].update({elements[0]: elements[1]})\n\n self.next_row()\n\n while self.frame_row.startswith(' Destroy'):\n\n elements = self.get_row_elements(1, \" \")\n\n actor_id = int(elements[1])\n actors_info[actor_id].update({\"destroyed\": frame_number})\n\n self.next_row()\n\n while self.frame_row.startswith(' Collision'):\n\n elements = self.get_row_elements(1, \" \")\n\n actor_id = int(elements[4])\n other_id = int(elements[-1])\n\n if actor_id not in frame_state[\"events\"][\"collisions\"]:\n frame_state[\"events\"][\"collisions\"][actor_id] = [other_id]\n else:\n collisions = frame_state[\"events\"][\"collisions\"][actor_id]\n collisions.append(other_id)\n frame_state[\"events\"][\"collisions\"].update({actor_id: collisions})\n\n self.next_row()\n\n while self.frame_row.startswith(' Parenting'):\n\n elements = self.get_row_elements(1, \" \")\n\n actor_id = int(elements[1])\n parent_id = int(elements[3])\n actors_info[actor_id].update({\"parent\": parent_id})\n\n self.next_row()\n\n if self.frame_row.startswith(' Positions'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n transform = parse_transform(elements)\n frame_state[\"actors\"].update({actor_id: {\"transform\": transform}})\n\n self.next_row()\n\n if self.frame_row.startswith(' State traffic lights'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n traffic_light = parse_traffic_light(elements)\n frame_state[\"actors\"].update({actor_id: traffic_light})\n self.next_row()\n\n if self.frame_row.startswith(' Vehicle animations'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n control = parse_control(elements)\n frame_state[\"actors\"][actor_id].update({\"control\": control})\n self.next_row()\n\n if self.frame_row.startswith(' Walker animations'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n frame_state[\"actors\"][actor_id].update({\"speed\": elements[3]})\n self.next_row()\n\n if self.frame_row.startswith(' Vehicle light animations'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n lights = parse_vehicle_lights(elements)\n frame_state[\"actors\"][actor_id].update({\"lights\": lights})\n self.next_row()\n\n if self.frame_row.startswith(' Scene light changes'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n scene_light = parse_scene_lights(elements)\n frame_state[\"events\"][\"scene_lights\"].update({actor_id: scene_light})\n self.next_row()\n\n if self.frame_row.startswith(' Dynamic actors'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n velocity = parse_velocity(elements)\n frame_state[\"actors\"][actor_id].update({\"velocity\": velocity})\n\n angular_v = parse_angular_velocity(elements)\n frame_state[\"actors\"][actor_id].update({\"angular_velocity\": angular_v})\n\n if delta_time == 0:\n acceleration = carla.Vector3D(0, 0, 0)\n else:\n prev_velocity = frame_state[\"actors\"][actor_id][\"velocity\"]\n acceleration = (velocity - prev_velocity) / delta_time\n\n frame_state[\"actors\"][actor_id].update({\"acceleration\": acceleration})\n self.next_row()\n\n if self.frame_row.startswith(' Actor bounding boxes'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n bbox = parse_bounding_box(elements)\n actors_info[actor_id].update({\"bounding_box\": bbox})\n self.next_row()\n\n if self.frame_row.startswith(' Actor trigger volumes'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n trigvol = parse_bounding_box(elements)\n actors_info[actor_id].update({\"trigger_volume\": trigvol})\n self.next_row()\n\n if self.frame_row.startswith(' Current platform time'):\n\n elements = self.get_row_elements(1, \" \")\n\n platform_time = float(elements[-1])\n frame_state[\"frame\"][\"platform_time\"] = platform_time\n self.next_row()\n\n if self.frame_row.startswith(' Physics Control'):\n self.next_row()\n\n actor_id = None\n while self.frame_row.startswith(' '):\n\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n physics_control = carla.VehiclePhysicsControl()\n self.next_row()\n\n forward_gears = []\n wheels = []\n while self.frame_row.startswith(' '):\n\n if self.frame_row.startswith(' '):\n elements = self.get_row_elements(4, \" \")\n if elements[0] == \"gear\":\n forward_gears.append(parse_gears_control(elements))\n elif elements[0] == \"wheel\":\n wheels.append(parse_wheels_control(elements))\n\n else:\n elements = self.get_row_elements(3, \" = \")\n name = elements[0]\n\n if name == \"center_of_mass\":\n values = elements[1].split(\" \")\n value = carla.Vector3D(\n float(values[0][1:-1]),\n float(values[1][:-1]),\n float(values[2][:-1]),\n )\n setattr(physics_control, name, value)\n elif name == \"torque_curve\" or name == \"steering_curve\":\n values = elements[1].split(\" \")\n value = parse_vector_list(values)\n setattr(physics_control, name, value)\n\n elif name == \"use_gear_auto_box\":\n name = \"use_gear_autobox\"\n value = True if elements[1] == \"true\" else False\n setattr(physics_control, name, value)\n\n elif \"forward_gears\" in name or \"wheels\" in name:\n pass\n\n else:\n name = name.lower()\n value = float(elements[1])\n setattr(physics_control, name, value)\n\n self.next_row()\n\n setattr(physics_control, \"forward_gears\", forward_gears)\n setattr(physics_control, \"wheels\", wheels)\n frame_state[\"events\"][\"physics_control\"].update({actor_id: physics_control})\n\n if self.frame_row.startswith(' Traffic Light time events'):\n self.next_row()\n\n while self.frame_row.startswith(' '):\n elements = self.get_row_elements(2, \" \")\n actor_id = int(elements[1])\n\n state_times = parse_state_times(elements)\n frame_state[\"events\"][\"traffic_light_state_time\"].update({actor_id: state_times})\n self.next_row()\n\n frames_info.append(frame_state)\n\n return simulation_info, actors_info, frames_info\n" }, { "path": "scenario_runner/srunner/openscenario/0.9.x/OpenSCENARIO_Catalog.xsd", "content": "\n\n \n \n \n \n \n \n \n XML Schema Definition for OpenSCENARIO Catalog XML files - Version Draft 0.9.1, (c)2017 by VIRES Simulationstechnologie GmbH, Germany\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/openscenario/0.9.x/OpenSCENARIO_TypeDefs.xsd", "content": "\n\n \n \n \n \n \n XML Schema Type Definitions for OpenSCENARIO XML files - Version Draft 0.9.1, (c)2017 by VIRES Simulationstechnologie GmbH, Germany\n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/openscenario/0.9.x/OpenSCENARIO_v0.9.1.xsd", "content": "\n\n \n \n \n \n \n \n \n XML Schema Definition for OpenSCENARIO XML files - Version 0.9.1, (c)2017 by VIRES Simulationstechnologie GmbH, Germany\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n" }, { "path": "scenario_runner/srunner/openscenario/0.9.x/migration0_9_1to1_0.xslt", "content": "\n\n \n \n \n \n \n longitudinal\n lateral\n cartesianDistance\n \n \n \n \n \n greaterThan\n lessThan\n equalTo\n \n \n \n \n \n none\n railing\n barrier\n \n \n \n \n \n endTransition\n stopTransition\n completeState\n \n \n \n \n \n risingOrFalling\n falling\n rising\n \n \n \n \n \n position\n follow\n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Review catalogs since driver catalog and pedestrian catalogs are merged into controller catalog\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: OSCTrajectory is restructured and connot be automatically migrated. Review XML\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Review catalogs since driver catalog and pedestrian catalogs are merged into controller catalog\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Review catalogs since driver catalog and pedestrian catalogs are merged into controller catalog\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Jam is desupported: removed during migration\n \n \n \n \n WARNING: Script is desupported: removed during migration\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: OSCPrivateAction.Meeting is desupported: removed during migration\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ERROR: Act: Cancel and End are both defined: Version 1.0 only supports Stop Trigger. Invalid XML is produced and needs to be revisited.\n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Storyboard.Story.Act.Sequence.Actors.ByCondition: \"anyEntity\" is de-supported for version 1.0\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: OSCPrivateAction.Longitudinal.Speed or OSCPrivateAction.Lateral.LaneChange.Dynamics: Distance, time, rate must exclude each other in the original file. This results in a invalid output.\n \n \n \n \n WARNING: OSCPrivateAction.Longitudinal.Speed or OSCPrivateAction.Lateral.LaneChange.Dynamics: There must be at least one of time, rate or distance in the original file. This results in a invalid output.\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Review catalogs since driver catalog and pedestrian catalogs are merged into controller catalog\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: Event.priority: 'following' is desupported: removed during migration. This results in unvalifd XML code.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: OSCPrivateAction.Meeting is desupported: removed during migration: This may result in unvalid XML\n \n \n \n \n WARNING: Jam is desupported: removed during migration: This may result in unvalid XML\n \n \n \n \n WARNING: Script is desupported: removed during migration: This may result in unvalid XML\n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Migration: Insert Controller here\n \n \n \n ERROR: OSCTrafficDefinition.DriverDistribution.Driver cannot be migrated automatically and will result in unvalid XML output.\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n WARNING: OSCTrajectory.Vertex: OSCTrajectory is restructured and connot be automatically migrated. Review XML.\n \n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": "scenario_runner/srunner/openscenario/OpenSCENARIO.xsd", "content": "\r\n\r\n\r\n 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}, { "path": "scenario_runner/srunner/scenarioconfigs/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/scenarioconfigs/openscenario_configuration.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the key configuration parameters for a scenario based on OpenSCENARIO\n\"\"\"\n\nimport logging\nimport os\nimport xml.etree.ElementTree as ET\n\nimport xmlschema\n\nimport carla\n\n# pylint: disable=line-too-long\nfrom srunner.scenarioconfigs.scenario_configuration import ActorConfigurationData, ScenarioConfiguration\n# pylint: enable=line-too-long\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider # workaround\nfrom srunner.tools.openscenario_parser import OpenScenarioParser\n\n\nclass OpenScenarioConfiguration(ScenarioConfiguration):\n\n \"\"\"\n Limitations:\n - Only one Story + Init is supported per Storyboard\n \"\"\"\n\n def __init__(self, filename, client):\n\n self.xml_tree = ET.parse(filename)\n self._filename = filename\n\n self._validate_openscenario_configuration()\n self.client = client\n\n self.catalogs = {}\n\n self.other_actors = []\n self.ego_vehicles = []\n self.trigger_points = []\n self.weather = carla.WeatherParameters()\n\n self.storyboard = self.xml_tree.find(\"Storyboard\")\n self.story = self.storyboard.find(\"Story\")\n self.init = self.storyboard.find(\"Init\")\n\n logging.basicConfig()\n self.logger = logging.getLogger(\"[SR:OpenScenarioConfiguration]\")\n\n self._global_parameters = {}\n\n self._set_parameters()\n self._parse_openscenario_configuration()\n\n def _validate_openscenario_configuration(self):\n \"\"\"\n Validate the given OpenSCENARIO config against the 0.9.1 XSD\n\n Note: This will throw if the config is not valid. But this is fine here.\n \"\"\"\n xsd_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../openscenario/OpenSCENARIO.xsd\")\n xsd = xmlschema.XMLSchema(xsd_file)\n xsd.validate(self.xml_tree)\n\n def _validate_openscenario_catalog_configuration(self, catalog_xml_tree):\n \"\"\"\n Validate the given OpenSCENARIO catalog config against the 0.9.1 XSD\n\n Note: This will throw if the catalog config is not valid. But this is fine here.\n \"\"\"\n xsd_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../openscenario/OpenSCENARIO.xsd\")\n xsd = xmlschema.XMLSchema(xsd_file)\n xsd.validate(catalog_xml_tree)\n\n def _parse_openscenario_configuration(self):\n \"\"\"\n Parse the given OpenSCENARIO config file, set and validate parameters\n \"\"\"\n OpenScenarioParser.set_osc_filepath(os.path.dirname(self._filename))\n\n self._check_version()\n self._load_catalogs()\n self._set_scenario_name()\n self._set_carla_town()\n self._set_actor_information()\n\n self._validate_result()\n\n def _check_version(self):\n \"\"\"\n Ensure correct OpenSCENARIO version is used\n \"\"\"\n header = self.xml_tree.find(\"FileHeader\")\n if not (header.attrib.get('revMajor') == \"1\" and header.attrib.get('revMinor') == \"0\"):\n raise AttributeError(\"Only OpenSCENARIO 1.0 is supported\")\n\n def _load_catalogs(self):\n \"\"\"\n Read Catalog xml files into dictionary for later use\n\n NOTE: Catalogs must have distinct names, even across different types\n \"\"\"\n catalogs = self.xml_tree.find(\"CatalogLocations\")\n if list(catalogs) is None:\n return\n\n catalog_types = [\"Vehicle\",\n \"Controller\",\n \"Pedestrian\",\n \"MiscObject\",\n \"Environment\",\n \"Maneuver\",\n \"Trajectory\",\n \"Route\"]\n for catalog_type in catalog_types:\n catalog = catalogs.find(catalog_type + \"Catalog\")\n if catalog is None:\n continue\n\n catalog_path = catalog.find(\"Directory\").attrib.get('path') + \"/\" + catalog_type + \"Catalog.xosc\"\n if not os.path.isabs(catalog_path) and \"xosc\" in self._filename:\n catalog_path = os.path.dirname(os.path.abspath(self._filename)) + \"/\" + catalog_path\n\n if not os.path.isfile(catalog_path):\n self.logger.warning(\" The %s path for the %s Catalog is invalid\", catalog_path, catalog_type)\n else:\n xml_tree = ET.parse(catalog_path)\n self._validate_openscenario_catalog_configuration(xml_tree)\n catalog = xml_tree.find(\"Catalog\")\n catalog_name = catalog.attrib.get(\"name\")\n self.catalogs[catalog_name] = {}\n for entry in catalog:\n self.catalogs[catalog_name][entry.attrib.get(\"name\")] = entry\n\n def _set_scenario_name(self):\n \"\"\"\n Extract the scenario name from the OpenSCENARIO header information\n \"\"\"\n header = self.xml_tree.find(\"FileHeader\")\n self.name = header.attrib.get('description', 'Unknown')\n\n if self.name.startswith(\"CARLA:\"):\n OpenScenarioParser.set_use_carla_coordinate_system()\n\n def _set_carla_town(self):\n \"\"\"\n Extract the CARLA town (level) from the RoadNetwork information from OpenSCENARIO\n\n Note: The specification allows multiple Logics elements within the RoadNetwork element.\n Hence, there can be multiple towns specified. We just use the _last_ one.\n \"\"\"\n for logic in self.xml_tree.find(\"RoadNetwork\").findall(\"LogicFile\"):\n self.town = logic.attrib.get('filepath', None)\n\n if self.town is not None and \".xodr\" in self.town:\n if not os.path.isabs(self.town):\n self.town = os.path.dirname(os.path.abspath(self._filename)) + \"/\" + self.town\n if not os.path.exists(self.town):\n raise AttributeError(\"The provided RoadNetwork '{}' does not exist\".format(self.town))\n\n # workaround for relative positions during init\n world = self.client.get_world()\n if world is None or world.get_map().name != self.town:\n self.logger.warning(\" Wrong OpenDRIVE map in use. Forcing reload of CARLA world\")\n if \".xodr\" in self.town:\n with open(self.town) as od_file:\n data = od_file.read()\n self.client.generate_opendrive_world(str(data))\n else:\n self.client.load_world(self.town)\n world = self.client.get_world()\n CarlaDataProvider.set_world(world)\n world.wait_for_tick()\n else:\n CarlaDataProvider.set_world(world)\n\n def _set_parameters(self):\n \"\"\"\n Parse the complete scenario definition file, and replace all parameter references\n with the actual values\n\n Set _global_parameters.\n \"\"\"\n\n self.xml_tree, self._global_parameters = OpenScenarioParser.set_parameters(self.xml_tree)\n\n for elem in self.xml_tree.iter():\n if elem.find('ParameterDeclarations') is not None:\n elem, _ = OpenScenarioParser.set_parameters(elem)\n\n OpenScenarioParser.set_global_parameters(self._global_parameters)\n\n def _set_actor_information(self):\n \"\"\"\n Extract all actors and their corresponding specification\n\n NOTE: The rolename property has to be unique!\n \"\"\"\n for entity in self.xml_tree.iter(\"Entities\"):\n for obj in entity.iter(\"ScenarioObject\"):\n rolename = obj.attrib.get('name', 'simulation')\n args = dict()\n for prop in obj.iter(\"Property\"):\n key = prop.get('name')\n value = prop.get('value')\n args[key] = value\n\n for catalog_reference in obj.iter(\"CatalogReference\"):\n entry = OpenScenarioParser.get_catalog_entry(self.catalogs, catalog_reference)\n if entry.tag == \"Vehicle\":\n self._extract_vehicle_information(entry, rolename, entry, args)\n elif entry.tag == \"Pedestrian\":\n self._extract_pedestrian_information(entry, rolename, entry, args)\n elif entry.tag == \"MiscObject\":\n self._extract_misc_information(entry, rolename, entry, args)\n else:\n self.logger.error(\n \" A CatalogReference specifies a reference that is not an Entity. Skipping...\")\n\n for vehicle in obj.iter(\"Vehicle\"):\n self._extract_vehicle_information(obj, rolename, vehicle, args)\n\n for pedestrian in obj.iter(\"Pedestrian\"):\n self._extract_pedestrian_information(obj, rolename, pedestrian, args)\n\n for misc in obj.iter(\"MiscObject\"):\n self._extract_misc_information(obj, rolename, misc, args)\n\n # Set transform for all actors\n # This has to be done in a multi-stage loop to resolve relative position settings\n all_actor_transforms_set = False\n while not all_actor_transforms_set:\n all_actor_transforms_set = True\n for actor in self.other_actors + self.ego_vehicles:\n if actor.transform is None:\n try:\n actor.transform = self._get_actor_transform(actor.rolename)\n except AttributeError as e:\n if \"Object '\" in str(e):\n ref_actor_rolename = str(e).split('\\'')[1]\n for ref_actor in self.other_actors + self.ego_vehicles:\n if ref_actor.rolename == ref_actor_rolename:\n if ref_actor.transform is not None:\n raise e\n break\n if actor.transform is None:\n all_actor_transforms_set = False\n\n def _extract_vehicle_information(self, obj, rolename, vehicle, args):\n \"\"\"\n Helper function to _set_actor_information for getting vehicle information from XML tree\n \"\"\"\n color = None\n model = vehicle.attrib.get('name', \"vehicle.*\")\n category = vehicle.attrib.get('vehicleCategory', \"car\")\n ego_vehicle = False\n for prop in obj.iter(\"Property\"):\n if prop.get('name', '') == 'type':\n ego_vehicle = prop.get('value') == 'ego_vehicle'\n if prop.get('name', '') == 'color':\n color = prop.get('value')\n\n speed = self._get_actor_speed(rolename)\n new_actor = ActorConfigurationData(\n model, None, rolename, speed, color=color, category=category, args=args)\n\n if ego_vehicle:\n self.ego_vehicles.append(new_actor)\n else:\n self.other_actors.append(new_actor)\n\n def _extract_pedestrian_information(self, obj, rolename, pedestrian, args):\n \"\"\"\n Helper function to _set_actor_information for getting pedestrian information from XML tree\n \"\"\"\n model = pedestrian.attrib.get('model', \"walker.*\")\n\n speed = self._get_actor_speed(rolename)\n new_actor = ActorConfigurationData(model, None, rolename, speed, category=\"pedestrian\", args=args)\n\n self.other_actors.append(new_actor)\n\n def _extract_misc_information(self, obj, rolename, misc, args):\n \"\"\"\n Helper function to _set_actor_information for getting vehicle information from XML tree\n \"\"\"\n category = misc.attrib.get('miscObjectCategory')\n if category == \"barrier\":\n model = \"static.prop.streetbarrier\"\n elif category == \"guardRail\":\n model = \"static.prop.chainbarrier\"\n else:\n model = misc.attrib.get('name')\n new_actor = ActorConfigurationData(model, None, rolename, category=\"misc\", args=args)\n\n self.other_actors.append(new_actor)\n\n def _get_actor_transform(self, actor_name):\n \"\"\"\n Get the initial actor transform provided by the Init section\n\n Note: - The OpenScenario specification allows multiple definitions. We use the _first_ one\n - The OpenScenario specification allows different ways of specifying a position.\n We currently support the specification with absolute world coordinates and the relative positions\n RelativeWorld, RelativeObject and RelativeLane\n - When using relative positions the relevant reference point (e.g. transform of another actor)\n should be defined before!\n \"\"\"\n\n actor_transform = carla.Transform()\n\n actor_found = False\n\n for private_action in self.init.iter(\"Private\"):\n if private_action.attrib.get('entityRef', None) == actor_name:\n if actor_found:\n # pylint: disable=line-too-long\n self.logger.warning(\n \" Warning: The actor '%s' was already assigned an initial position. Overwriting pose!\", actor_name)\n # pylint: enable=line-too-long\n actor_found = True\n for position in private_action.iter('Position'):\n transform = OpenScenarioParser.convert_position_to_transform(\n position, actor_list=self.other_actors + self.ego_vehicles)\n if transform:\n actor_transform = transform\n\n if not actor_found:\n # pylint: disable=line-too-long\n self.logger.warning(\n \" Warning: The actor '%s' was not assigned an initial position. Using (0,0,0)\", actor_name)\n # pylint: enable=line-too-long\n\n return actor_transform\n\n def _get_actor_speed(self, actor_name):\n \"\"\"\n Get the initial actor speed provided by the Init section\n \"\"\"\n actor_speed = 0\n actor_found = False\n\n for private_action in self.init.iter(\"Private\"):\n if private_action.attrib.get('entityRef', None) == actor_name:\n if actor_found:\n # pylint: disable=line-too-long\n self.logger.warning(\n \" Warning: The actor '%s' was already assigned an initial speed. Overwriting inital speed!\", actor_name)\n # pylint: enable=line-too-long\n actor_found = True\n\n for longitudinal_action in private_action.iter('LongitudinalAction'):\n for speed in longitudinal_action.iter('SpeedAction'):\n for target in speed.iter('SpeedActionTarget'):\n for absolute in target.iter('AbsoluteTargetSpeed'):\n speed = float(absolute.attrib.get('value', 0))\n if speed >= 0:\n actor_speed = speed\n else:\n raise AttributeError(\n \"Warning: Speed value of actor {} must be positive. Speed set to 0.\".format(actor_name)) # pylint: disable=line-too-long\n return actor_speed\n\n def _validate_result(self):\n \"\"\"\n Check that the current scenario configuration is valid\n \"\"\"\n if not self.name:\n raise AttributeError(\"No scenario name found\")\n\n if not self.town:\n raise AttributeError(\"CARLA level not defined\")\n\n if not self.ego_vehicles:\n self.logger.warning(\" No ego vehicles defined in scenario\")\n" }, { "path": "scenario_runner/srunner/scenarioconfigs/route_scenario_configuration.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the key configuration parameters for a route-based scenario\n\"\"\"\n\nimport carla\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenarioconfigs.scenario_configuration import ScenarioConfiguration\n\n\nclass RouteConfiguration(object):\n\n \"\"\"\n This class provides the basic configuration for a route\n \"\"\"\n\n def __init__(self, route=None):\n self.data = route\n self.folder_name = None\n\n def parse_xml(self, node):\n \"\"\"\n Parse route config XML\n \"\"\"\n self.data = []\n\n for waypoint in node.iter(\"waypoint\"):\n x = float(waypoint.attrib.get('x', 0))\n y = float(waypoint.attrib.get('y', 0))\n z = float(waypoint.attrib.get('z', 0))\n c = waypoint.attrib.get('connection', '')\n connection = RoadOption[c.split('.')[1]]\n\n self.data.append((carla.Location(x, y, z), connection))\n\n\nclass RouteScenarioConfiguration(ScenarioConfiguration):\n\n \"\"\"\n Basic configuration of a RouteScenario\n \"\"\"\n folder_name = None\n trajectory = None\n scenario_file = None\n" }, { "path": "scenario_runner/srunner/scenarioconfigs/scenario_configuration.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the key configuration parameters for an XML-based scenario\n\"\"\"\n\nimport carla\n\n\nclass ActorConfigurationData(object):\n\n \"\"\"\n This is a configuration base class to hold model and transform attributes\n \"\"\"\n\n def __init__(self, model, transform, rolename='other', speed=0, autopilot=False,\n random=False, color=None, category=\"car\", args=None):\n self.model = model\n self.rolename = rolename\n self.transform = transform\n self.speed = speed\n self.autopilot = autopilot\n self.random_location = random\n self.color = color\n self.category = category\n self.args = args\n\n @staticmethod\n def parse_from_node(node, rolename):\n \"\"\"\n static method to initialize an ActorConfigurationData from a given ET tree\n \"\"\"\n\n model = node.attrib.get('model', 'vehicle.*')\n\n pos_x = float(node.attrib.get('x', 0))\n pos_y = float(node.attrib.get('y', 0))\n pos_z = float(node.attrib.get('z', 0))\n yaw = float(node.attrib.get('yaw', 0))\n\n transform = carla.Transform(carla.Location(x=pos_x, y=pos_y, z=pos_z), carla.Rotation(yaw=yaw))\n\n rolename = node.attrib.get('rolename', rolename)\n\n speed = node.attrib.get('speed', 0)\n\n autopilot = False\n if 'autopilot' in node.keys():\n autopilot = True\n\n random_location = False\n if 'random_location' in node.keys():\n random_location = True\n\n color = node.attrib.get('color', None)\n\n return ActorConfigurationData(model, transform, rolename, speed, autopilot, random_location, color)\n\n\nclass ScenarioConfiguration(object):\n\n \"\"\"\n This class provides a basic scenario configuration incl.:\n - configurations for all actors\n - town, where the scenario should be executed\n - name of the scenario (e.g. ControlLoss_1)\n - type is the class of scenario (e.g. ControlLoss)\n \"\"\"\n\n trigger_points = []\n ego_vehicles = []\n other_actors = []\n town = None\n name = None\n type = None\n route = None\n agent = None\n weather = carla.WeatherParameters()\n friction = None\n subtype = None\n route_var_name = None\n" }, { "path": "scenario_runner/srunner/scenariomanager/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/actor_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a wrapper to access/use user-defined actor\ncontrols for example to realize OpenSCENARIO controllers.\n\nAt the moment only controls implemented in Python are supported.\n\nA user must not modify this module.\n\"\"\"\n\nimport importlib\nimport os\nimport sys\n\nimport carla\n\nfrom srunner.scenariomanager.actorcontrols.external_control import ExternalControl\nfrom srunner.scenariomanager.actorcontrols.npc_vehicle_control import NpcVehicleControl\nfrom srunner.scenariomanager.actorcontrols.pedestrian_control import PedestrianControl\n\n\nclass ActorControl(object):\n\n \"\"\"\n This class provides a wrapper (access mechanism) for user-defined actor controls.\n The controllers are loaded via importlib. Therefore, the module name of the controller\n has to match the control class name (e.g. my_own_control.py and MyOwnControl()).\n\n At the moment only controllers implemented in Python are supported, or controllers that\n are completely implemented outside of ScenarioRunner (see ExternalControl).\n\n This wrapper is for example used to realize the OpenSCENARIO controllers.\n\n Note:\n If no controllers are provided in OpenSCENARIO a default controller for vehicles and\n pedestrians is instantiated. For vehicles the NpcVehicleControl is used, for pedestrians\n it is the PedestrianControl.\n\n Args:\n actor (carla.Actor): Actor that should be controlled by the controller.\n control_py_module (string): Fully qualified path to the controller python module.\n args (dict): A dictionary containing all parameters of the controller as (key, value) pairs.\n\n Attributes:\n control_instance: Instance of the user-defined controller.\n _last_longitudinal_command: Timestamp of the last issued longitudinal control command (e.g. target speed).\n Defaults to None. Used to avoid that 2 longitudinal control commands are issued at the same time.\n _last_waypoint_command: Timestamp of the last issued waypoint control command.\n Defaults to None. Used to avoid that 2 waypoint control commands are issued at the same time.\n \"\"\"\n\n control_instance = None\n\n _last_longitudinal_command = None\n _last_waypoint_command = None\n\n def __init__(self, actor, control_py_module, args):\n\n # use importlib to import the control module\n if not control_py_module:\n if isinstance(actor, carla.Walker):\n self.control_instance = PedestrianControl(actor)\n elif isinstance(actor, carla.Vehicle):\n self.control_instance = NpcVehicleControl(actor)\n else:\n # use ExternalControl for all misc objects to handle all actors the same way\n self.control_instance = ExternalControl(actor)\n else:\n if \".py\" in control_py_module:\n module_name = os.path.basename(control_py_module).split('.')[0]\n sys.path.append(os.path.dirname(control_py_module))\n module_control = importlib.import_module(module_name)\n control_class_name = module_control.__name__.title().replace('_', '')\n else:\n sys.path.append(os.path.dirname(__file__))\n module_control = importlib.import_module(control_py_module)\n control_class_name = control_py_module.split('.')[-1].title().replace('_', '')\n\n self.control_instance = getattr(module_control, control_class_name)(actor, args)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n self.control_instance.reset()\n\n def update_target_speed(self, target_speed, start_time=None):\n \"\"\"\n Update the actor's target speed.\n\n Args:\n target_speed (float): New target speed [m/s].\n start_time (float): Start time of the new \"maneuver\" [s].\n \"\"\"\n self.control_instance.update_target_speed(target_speed)\n if start_time:\n self._last_longitudinal_command = start_time\n\n def update_waypoints(self, waypoints, start_time=None):\n \"\"\"\n Update the actor's waypoints\n\n Args:\n waypoints (List of carla.Transform): List of new waypoints.\n start_time (float): Start time of the new \"maneuver\" [s].\n \"\"\"\n self.control_instance.update_waypoints(waypoints)\n if start_time:\n self._last_waypoint_command = start_time\n\n def check_reached_waypoint_goal(self):\n \"\"\"\n Check if the actor reached the end of the waypoint list\n\n returns:\n True if the end was reached, False otherwise.\n \"\"\"\n return self.control_instance.check_reached_waypoint_goal()\n\n def get_last_longitudinal_command(self):\n \"\"\"\n Get timestamp of the last issued longitudinal control command (target_speed)\n\n returns:\n Timestamp of last longitudinal control command\n \"\"\"\n return self._last_longitudinal_command\n\n def get_last_waypoint_command(self):\n \"\"\"\n Get timestamp of the last issued waypoint control command\n\n returns:\n Timestamp of last waypoint control command\n \"\"\"\n return self._last_waypoint_command\n\n def set_init_speed(self):\n \"\"\"\n Update the actor's initial speed setting\n \"\"\"\n self.control_instance.set_init_speed()\n\n def run_step(self):\n \"\"\"\n Execute on tick of the controller's control loop\n \"\"\"\n self.control_instance.run_step()\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/basic_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the base class for user-defined actor\ncontrollers. All user-defined controls must be derived from\nthis class.\n\nA user must not modify the module.\n\"\"\"\n\n\nclass BasicControl(object):\n\n \"\"\"\n This class is the base class for user-defined actor controllers\n All user-defined agents must be derived from this class.\n\n Args:\n actor (carla.Actor): Actor that should be controlled by the controller.\n\n Attributes:\n _actor (carla.Actor): Controlled actor.\n Defaults to None.\n _target_speed (float): Logitudinal target speed of the controller.\n Defaults to 0.\n _init_speed (float): Initial longitudinal speed of the controller.\n Defaults to 0.\n _waypoints (list of carla.Transform): List of target waypoints the actor\n should travel along. A waypoint here is of type carla.Transform!\n Defaults to [].\n _waypoints_updated (boolean):\n Defaults to False.\n _reached_goal (boolean):\n Defaults to False.\n \"\"\"\n\n _actor = None\n _waypoints = []\n _waypoints_updated = False\n _target_speed = 0\n _reached_goal = False\n _init_speed = False\n\n def __init__(self, actor):\n \"\"\"\n Initialize the actor\n \"\"\"\n self._actor = actor\n\n def update_target_speed(self, speed):\n \"\"\"\n Update the actor's target speed and set _init_speed to False.\n\n Args:\n speed (float): New target speed [m/s].\n \"\"\"\n self._target_speed = speed\n self._init_speed = False\n\n def update_waypoints(self, waypoints, start_time=None):\n \"\"\"\n Update the actor's waypoints\n\n Args:\n waypoints (List of carla.Transform): List of new waypoints.\n \"\"\"\n self._waypoints = waypoints\n self._waypoints_updated = True\n\n def set_init_speed(self):\n \"\"\"\n Set _init_speed to True\n \"\"\"\n self._init_speed = True\n\n def check_reached_waypoint_goal(self):\n \"\"\"\n Check if the actor reached the end of the waypoint list\n\n returns:\n True if the end was reached, False otherwise.\n \"\"\"\n return self._reached_goal\n\n def reset(self):\n \"\"\"\n Pure virtual function to reset the controller. This should be implemented\n in the user-defined agent implementation.\n \"\"\"\n raise NotImplementedError(\n \"This function must be re-implemented by the user-defined actor control.\"\n \"If this error becomes visible the class hierarchy is somehow broken\")\n\n def run_step(self):\n \"\"\"\n Pure virtual function to run one step of the controllers's control loop.\n This should be implemented in the user-defined agent implementation.\n \"\"\"\n raise NotImplementedError(\n \"This function must be re-implemented by the user-defined actor control.\"\n \"If this error becomes visible the class hierarchy is somehow broken\")\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/external_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an example controller for actors, that use an external\nsoftware for longitudinal and lateral control command calculation.\nExamples for external controls are: Autoware, CARLA manual_control, etc.\n\nThis module is not intended for modification.\n\"\"\"\n\nfrom srunner.scenariomanager.actorcontrols.basic_control import BasicControl\n\n\nclass ExternalControl(BasicControl):\n\n \"\"\"\n Actor control class for actors, with externally implemented longitudinal and\n lateral controlers (e.g. Autoware).\n\n Args:\n actor (carla.Actor): Actor that should be controlled by the agent.\n \"\"\"\n\n def __init__(self, actor, args=None):\n super(ExternalControl, self).__init__(actor)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n if self._actor and self._actor.is_alive:\n self._actor = None\n\n def run_step(self):\n \"\"\"\n The control loop and setting the actor controls is implemented externally.\n \"\"\"\n pass\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/npc_vehicle_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an example control for vehicles\n\"\"\"\n\nimport math\n\nimport carla\nfrom agents.navigation.basic_agent import LocalPlanner\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.actorcontrols.basic_control import BasicControl\n\n\nclass NpcVehicleControl(BasicControl):\n\n \"\"\"\n Controller class for vehicles derived from BasicControl.\n\n The controller makes use of the LocalPlanner implemented in CARLA.\n\n Args:\n actor (carla.Actor): Vehicle actor that should be controlled.\n \"\"\"\n\n _args = {'K_P': 1.0, 'K_D': 0.01, 'K_I': 0.0, 'dt': 0.05}\n\n def __init__(self, actor, args=None):\n super(NpcVehicleControl, self).__init__(actor)\n\n self._local_planner = LocalPlanner( # pylint: disable=undefined-variable\n self._actor, opt_dict={\n 'target_speed': self._target_speed * 3.6,\n 'lateral_control_dict': self._args})\n\n if self._waypoints:\n self._update_plan()\n\n def _update_plan(self):\n \"\"\"\n Update the plan (waypoint list) of the LocalPlanner\n \"\"\"\n plan = []\n for transform in self._waypoints:\n waypoint = CarlaDataProvider.get_map().get_waypoint(\n transform.location, project_to_road=True, lane_type=carla.LaneType.Any)\n plan.append((waypoint, RoadOption.LANEFOLLOW))\n self._local_planner.set_global_plan(plan)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n if self._actor and self._actor.is_alive:\n if self._local_planner:\n self._local_planner.reset_vehicle()\n self._local_planner = None\n self._actor = None\n\n def run_step(self):\n \"\"\"\n Execute on tick of the controller's control loop\n\n If _waypoints are provided, the vehicle moves towards the next waypoint\n with the given _target_speed, until reaching the final waypoint. Upon reaching\n the final waypoint, _reached_goal is set to True.\n\n If _waypoints is empty, the vehicle moves in its current direction with\n the given _target_speed.\n\n If _init_speed is True, the control command is post-processed to ensure that\n the initial actor velocity is maintained independent of physics.\n \"\"\"\n self._reached_goal = False\n\n if self._waypoints_updated:\n self._waypoints_updated = False\n self._update_plan()\n\n target_speed = self._target_speed\n self._local_planner.set_speed(target_speed * 3.6)\n control = self._local_planner.run_step(debug=False)\n\n # Check if the actor reached the end of the plan\n if self._local_planner.done():\n self._reached_goal = True\n\n self._actor.apply_control(control)\n\n if self._init_speed:\n current_speed = math.sqrt(self._actor.get_velocity().x**2 + self._actor.get_velocity().y**2)\n\n # If _init_speed is set, and the PID controller is not yet up to the point to take over,\n # we manually set the vehicle to drive with the correct velocity\n if abs(target_speed - current_speed) > 3:\n yaw = self._actor.get_transform().rotation.yaw * (math.pi / 180)\n vx = math.cos(yaw) * target_speed\n vy = math.sin(yaw) * target_speed\n self._actor.set_target_velocity(carla.Vector3D(vx, vy, 0))\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/pedestrian_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an example control for pedestrians\n\"\"\"\n\nimport math\n\nimport carla\n\nfrom srunner.scenariomanager.actorcontrols.basic_control import BasicControl\n\n\nclass PedestrianControl(BasicControl):\n\n \"\"\"\n Controller class for pedestrians derived from BasicControl.\n\n Args:\n actor (carla.Actor): Pedestrian actor that should be controlled.\n \"\"\"\n\n def __init__(self, actor, args=None):\n if not isinstance(actor, carla.Walker):\n raise RuntimeError(\"PedestrianControl: The to be controlled actor is not a pedestrian\")\n\n super(PedestrianControl, self).__init__(actor)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n if self._actor and self._actor.is_alive:\n self._actor = None\n\n def run_step(self):\n \"\"\"\n Execute on tick of the controller's control loop\n\n If _waypoints are provided, the pedestrian moves towards the next waypoint\n with the given _target_speed, until reaching the final waypoint. Upon reaching\n the final waypoint, _reached_goal is set to True.\n\n If _waypoints is empty, the pedestrians moves in its current direction with\n the given _target_speed.\n \"\"\"\n if not self._actor or not self._actor.is_alive:\n return\n\n control = self._actor.get_control()\n control.speed = self._target_speed\n\n if self._waypoints:\n self._reached_goal = False\n location = self._waypoints[0].location\n direction = location - self._actor.get_location()\n direction_norm = math.sqrt(direction.x**2 + direction.y**2)\n control.direction = direction / direction_norm\n self._actor.apply_control(control)\n if direction_norm < 1.0:\n self._waypoints = self._waypoints[1:]\n if not self._waypoints:\n self._reached_goal = True\n else:\n control.direction = self._actor.get_transform().rotation.get_forward_vector()\n self._actor.apply_control(control)\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/simple_vehicle_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an example control for vehicles which\ndoes not use CARLA's vehicle engine.\n\nLimitations:\n- Does not respect any traffic regulation: speed limit, traffic light, priorities, etc.\n- Can only consider obstacles in forward facing reaching (i.e. in tight corners obstacles may be ignored).\n\"\"\"\n\nfrom distutils.util import strtobool\nimport math\nimport cv2\nimport numpy as np\n\nimport carla\n\nfrom srunner.scenariomanager.actorcontrols.basic_control import BasicControl\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\n\n\nclass SimpleVehicleControl(BasicControl):\n\n \"\"\"\n Controller class for vehicles derived from BasicControl.\n\n The controller directly sets velocities in CARLA, therefore bypassing\n CARLA's vehicle engine. This allows a very precise speed control, but comes\n with limitations during cornering.\n\n In addition, the controller can consider blocking obstacles, which are\n classified as dynamic (i.e. vehicles, bikes, pedestrians). Activation of this\n features is controlled by passing proper arguments to the class constructor.\n The collision detection uses CARLA's obstacle sensor (sensor.other.obstacle),\n which checks for obstacles in the direct forward channel of the vehicle, i.e.\n there are limitation with sideways obstacles and while cornering.\n\n Args:\n actor (carla.Actor): Vehicle actor that should be controlled.\n args (dictionary): Dictonary of (key, value) arguments to be used by the controller.\n May include: (consider_obstacles, true/false) - Enable consideration of obstacles\n (proximity_threshold, distance) - Distance in front of actor in which\n obstacles are considered\n (attach_camera, true/false) - Attach OpenCV display to actor\n (useful for debugging)\n\n Attributes:\n\n _generated_waypoint_list (list of carla.Transform): List of target waypoints the actor\n should travel along. A waypoint here is of type carla.Transform!\n Defaults to [].\n _last_update (float): Last time step the update function (tick()) was called.\n Defaults to None.\n _consider_obstacles (boolean): Enable/Disable consideration of obstacles\n Defaults to False.\n _proximity_threshold (float): Distance in front of actor in which obstacles are considered\n Defaults to infinity.\n _cv_image (CV Image): Contains the OpenCV image, in case a debug camera is attached to the actor\n Defaults to None.\n _camera (sensor.camera.rgb): Debug camera attached to actor\n Defaults to None.\n _obstacle_sensor (sensor.other.obstacle): Obstacle sensor attached to actor\n Defaults to None.\n _obstacle_distance (float): Distance of the closest obstacle returned by the obstacle sensor\n Defaults to infinity.\n _obstacle_actor (carla.Actor): Closest obstacle returned by the obstacle sensor\n Defaults to None.\n \"\"\"\n\n def __init__(self, actor, args=None):\n super(SimpleVehicleControl, self).__init__(actor)\n self._generated_waypoint_list = []\n self._last_update = None\n self._consider_obstacles = False\n self._proximity_threshold = float('inf')\n\n self._cv_image = None\n self._camera = None\n self._obstacle_sensor = None\n self._obstacle_distance = float('inf')\n self._obstacle_actor = None\n\n if args and 'consider_obstacles' in args and strtobool(args['consider_obstacles']):\n self._consider_obstacles = strtobool(args['consider_obstacles'])\n bp = CarlaDataProvider.get_world().get_blueprint_library().find('sensor.other.obstacle')\n bp.set_attribute('distance', '250')\n if args and 'proximity_threshold' in args:\n self._proximity_threshold = float(args['proximity_threshold'])\n bp.set_attribute('distance', str(max(float(args['proximity_threshold']), 250)))\n bp.set_attribute('hit_radius', '1')\n bp.set_attribute('only_dynamics', 'True')\n self._obstacle_sensor = CarlaDataProvider.get_world().spawn_actor(\n bp, carla.Transform(carla.Location(x=self._actor.bounding_box.extent.x, z=1.0)), attach_to=self._actor)\n self._obstacle_sensor.listen(lambda event: self._on_obstacle(event)) # pylint: disable=unnecessary-lambda\n\n if args and 'attach_camera' in args and strtobool(args['attach_camera']):\n bp = CarlaDataProvider.get_world().get_blueprint_library().find('sensor.camera.rgb')\n self._camera = CarlaDataProvider.get_world().spawn_actor(bp, carla.Transform(\n carla.Location(x=0.0, z=30.0), carla.Rotation(pitch=-60)), attach_to=self._actor)\n self._camera.listen(lambda image: self._on_camera_update(image)) # pylint: disable=unnecessary-lambda\n\n def _on_obstacle(self, event):\n \"\"\"\n Callback for the obstacle sensor\n\n Sets _obstacle_distance and _obstacle_actor according to the closest obstacle\n found by the sensor.\n \"\"\"\n if not event:\n return\n self._obstacle_distance = event.distance\n self._obstacle_actor = event.other_actor\n\n def _on_camera_update(self, image):\n \"\"\"\n Callback for the camera sensor\n\n Sets the OpenCV image (_cv_image). Requires conversion from BGRA to RGB.\n \"\"\"\n if not image:\n return\n\n image_data = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n np_image = np.reshape(image_data, (image.height, image.width, 4))\n np_image = np_image[:, :, :3]\n np_image = np_image[:, :, ::-1]\n self._cv_image = cv2.cvtColor(np_image, cv2.COLOR_BGR2RGB)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n if self._camera:\n self._camera.destroy()\n self._camera = None\n if self._obstacle_sensor:\n self._obstacle_sensor.destroy()\n self._obstacle_sensor = None\n if self._actor and self._actor.is_alive:\n self._actor = None\n\n def run_step(self):\n \"\"\"\n Execute on tick of the controller's control loop\n\n If _waypoints are provided, the vehicle moves towards the next waypoint\n with the given _target_speed, until reaching the final waypoint. Upon reaching\n the final waypoint, _reached_goal is set to True.\n\n If _waypoints is empty, the vehicle moves in its current direction with\n the given _target_speed.\n\n If _consider_obstacles is true, the speed is adapted according to the closest\n obstacle in front of the actor, if it is within the _proximity_threshold distance.\n \"\"\"\n\n if self._cv_image is not None:\n cv2.imshow(\"\", self._cv_image)\n cv2.waitKey(1)\n\n if self._reached_goal:\n # Reached the goal, so stop\n velocity = carla.Vector3D(0, 0, 0)\n self._actor.set_target_velocity(velocity)\n return\n\n self._reached_goal = False\n\n if not self._waypoints:\n # No waypoints are provided, so we have to create a list of waypoints internally\n # get next waypoints from map, to avoid leaving the road\n self._reached_goal = False\n\n map_wp = None\n if not self._generated_waypoint_list:\n map_wp = CarlaDataProvider.get_map().get_waypoint(CarlaDataProvider.get_location(self._actor))\n else:\n map_wp = CarlaDataProvider.get_map().get_waypoint(self._generated_waypoint_list[-1].location)\n while len(self._generated_waypoint_list) < 50:\n map_wps = map_wp.next(3.0)\n if map_wps:\n self._generated_waypoint_list.append(map_wps[0].transform)\n map_wp = map_wps[0]\n else:\n break\n\n direction_norm = self._set_new_velocity(self._generated_waypoint_list[0].location)\n if direction_norm < 2.0:\n self._generated_waypoint_list = self._generated_waypoint_list[1:]\n else:\n # When changing from \"free\" driving without pre-defined waypoints to a defined route with waypoints\n # it may happen that the first few waypoints are too close to the ego vehicle for obtaining a\n # reasonable control command. Therefore, we drop these waypoints first.\n while self._waypoints and self._waypoints[0].location.distance(self._actor.get_location()) < 0.5:\n self._waypoints = self._waypoints[1:]\n\n self._reached_goal = False\n direction_norm = self._set_new_velocity(self._waypoints[0].location)\n if direction_norm < 4.0:\n self._waypoints = self._waypoints[1:]\n if not self._waypoints:\n self._reached_goal = True\n\n def _set_new_velocity(self, next_location):\n \"\"\"\n Calculate and set the new actor veloctiy given the current actor\n location and the _next_location_\n\n If _consider_obstacles is true, the speed is adapted according to the closest\n obstacle in front of the actor, if it is within the _proximity_threshold distance.\n\n Args:\n next_location (carla.Location): Next target location of the actor\n\n returns:\n direction (carla.Vector3D): Length of direction vector of the actor\n \"\"\"\n\n current_time = GameTime.get_time()\n target_speed = self._target_speed\n\n if not self._last_update:\n self._last_update = current_time\n\n if self._consider_obstacles:\n # If distance is less than the proximity threshold, adapt velocity\n if self._obstacle_distance < self._proximity_threshold:\n distance = max(self._obstacle_distance, 0)\n if distance > 0:\n current_speed = math.sqrt(self._actor.get_velocity().x**2 + self._actor.get_velocity().y**2)\n current_speed_other = math.sqrt(\n self._obstacle_actor.get_velocity().x**2 + self._obstacle_actor.get_velocity().y**2)\n if current_speed_other < current_speed:\n acceleration = -0.5 * (current_speed - current_speed_other)**2 / distance\n target_speed = max(acceleration * (current_time - self._last_update) + current_speed, 0)\n else:\n target_speed = 0\n\n # set new linear velocity\n velocity = carla.Vector3D(0, 0, 0)\n direction = next_location - CarlaDataProvider.get_location(self._actor)\n direction_norm = math.sqrt(direction.x**2 + direction.y**2)\n velocity.x = direction.x / direction_norm * target_speed\n velocity.y = direction.y / direction_norm * target_speed\n\n self._actor.set_target_velocity(velocity)\n\n # set new angular velocity\n current_yaw = CarlaDataProvider.get_transform(self._actor).rotation.yaw\n # When we have a waypoint list, use the direction between the waypoints to calculate the heading (change)\n # otherwise use the waypoint heading directly\n if self._waypoints:\n delta_yaw = math.degrees(math.atan2(direction.y, direction.x)) - current_yaw\n else:\n new_yaw = CarlaDataProvider.get_map().get_waypoint(next_location).transform.rotation.yaw\n delta_yaw = new_yaw - current_yaw\n\n if math.fabs(delta_yaw) > 360:\n delta_yaw = delta_yaw % 360\n\n if delta_yaw > 180:\n delta_yaw = delta_yaw - 360\n elif delta_yaw < -180:\n delta_yaw = delta_yaw + 360\n\n angular_velocity = carla.Vector3D(0, 0, 0)\n if target_speed == 0:\n angular_velocity.z = 0\n else:\n angular_velocity.z = delta_yaw / (direction_norm / target_speed)\n self._actor.set_target_angular_velocity(angular_velocity)\n\n self._last_update = current_time\n\n return direction_norm\n" }, { "path": "scenario_runner/srunner/scenariomanager/actorcontrols/vehicle_longitudinal_control.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides an example longitudinal control for vehicles\n\"\"\"\n\nimport math\n\nimport carla\n\nfrom srunner.scenariomanager.actorcontrols.basic_control import BasicControl\n\n\nclass VehicleLongitudinalControl(BasicControl):\n\n \"\"\"\n Controller class for vehicles derived from BasicControl.\n\n The controller only controls the throttle of a vehicle, but not the steering.\n\n Args:\n actor (carla.Actor): Vehicle actor that should be controlled.\n \"\"\"\n\n def __init__(self, actor, args=None):\n super(VehicleLongitudinalControl, self).__init__(actor)\n\n def reset(self):\n \"\"\"\n Reset the controller\n \"\"\"\n if self._actor and self._actor.is_alive:\n self._actor = None\n\n def run_step(self):\n \"\"\"\n Execute on tick of the controller's control loop\n\n The control loop is very simplistic:\n If the actor speed is below the _target_speed, set throttle to 1.0,\n otherwise, set throttle to 0.0\n Note, that this is a longitudinal controller only.\n\n If _init_speed is True, the control command is post-processed to ensure that\n the initial actor velocity is maintained independent of physics.\n \"\"\"\n\n control = self._actor.get_control()\n\n velocity = self._actor.get_velocity()\n current_speed = math.sqrt(velocity.x**2 + velocity.y**2)\n if current_speed < self._target_speed:\n control.throttle = 1.0\n else:\n control.throttle = 0.0\n\n self._actor.apply_control(control)\n\n if self._init_speed:\n if abs(self._target_speed - current_speed) > 3:\n yaw = self._actor.get_transform().rotation.yaw * (math.pi / 180)\n vx = math.cos(yaw) * self._target_speed\n vy = math.sin(yaw) * self._target_speed\n self._actor.set_target_velocity(carla.Vector3D(vx, vy, 0))\n" }, { "path": "scenario_runner/srunner/scenariomanager/carla_data_provider.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides all frequently used data from CARLA via\nlocal buffers to avoid blocking calls to CARLA\n\"\"\"\n\nfrom __future__ import print_function\n\nimport math\nimport re\nimport numpy.random as random\nfrom six import iteritems\n\nimport carla\n\n\ndef calculate_velocity(actor):\n \"\"\"\n Method to calculate the velocity of a actor\n \"\"\"\n velocity_squared = actor.get_velocity().x**2\n velocity_squared += actor.get_velocity().y**2\n return math.sqrt(velocity_squared)\n\n\nclass CarlaDataProvider(object): # pylint: disable=too-many-public-methods\n\n \"\"\"\n This class provides access to various data of all registered actors\n It buffers the data and updates it on every CARLA tick\n\n Currently available data:\n - Absolute velocity\n - Location\n - Transform\n\n Potential additions:\n - Acceleration\n\n In addition it provides access to the map and the transform of all traffic lights\n \"\"\"\n\n _actor_velocity_map = dict()\n _actor_location_map = dict()\n _actor_transform_map = dict()\n _traffic_light_map = dict()\n _carla_actor_pool = dict()\n _client = None\n _world = None\n _map = None\n _sync_flag = False\n _spawn_points = None\n _spawn_index = 0\n _blueprint_library = None\n _ego_vehicle_route = None\n _traffic_manager_port = 8000\n\n _ego_vehicle = None\n _rng = random.RandomState(2000)\n\n _time_step = -1\n\n @staticmethod\n def register_actor(actor):\n \"\"\"\n Add new actor to dictionaries\n If actor already exists, throw an exception\n \"\"\"\n if actor in CarlaDataProvider._actor_velocity_map:\n raise KeyError(\n \"Vehicle '{}' already registered. Cannot register twice!\".format(actor.id))\n else:\n CarlaDataProvider._actor_velocity_map[actor] = 0.0\n\n if actor in CarlaDataProvider._actor_location_map:\n raise KeyError(\n \"Vehicle '{}' already registered. Cannot register twice!\".format(actor.id))\n else:\n CarlaDataProvider._actor_location_map[actor] = None\n\n if actor in CarlaDataProvider._actor_transform_map:\n raise KeyError(\n \"Vehicle '{}' already registered. Cannot register twice!\".format(actor.id))\n else:\n CarlaDataProvider._actor_transform_map[actor] = None\n\n @staticmethod\n def register_actors(actors):\n \"\"\"\n Add new set of actors to dictionaries\n \"\"\"\n for actor in actors:\n CarlaDataProvider.register_actor(actor)\n\n @staticmethod\n def on_carla_tick():\n \"\"\"\n Callback from CARLA\n \"\"\"\n for actor in CarlaDataProvider._actor_velocity_map:\n if actor is not None and actor.is_alive:\n CarlaDataProvider._actor_velocity_map[actor] = calculate_velocity(actor)\n\n for actor in CarlaDataProvider._actor_location_map:\n if actor is not None and actor.is_alive:\n CarlaDataProvider._actor_location_map[actor] = actor.get_location()\n\n for actor in CarlaDataProvider._actor_transform_map:\n if actor is not None and actor.is_alive:\n CarlaDataProvider._actor_transform_map[actor] = actor.get_transform()\n\n world = CarlaDataProvider._world\n if world is None:\n print(\"WARNING: CarlaDataProvider couldn't find the world\")\n\n @staticmethod\n def get_velocity(actor):\n \"\"\"\n returns the absolute velocity for the given actor\n \"\"\"\n for key in CarlaDataProvider._actor_velocity_map:\n if key.id == actor.id:\n return CarlaDataProvider._actor_velocity_map[key]\n\n # We are intentionally not throwing here\n # This may cause exception loops in py_trees\n print('{}.get_velocity: {} not found!' .format(__name__, actor))\n return 0.0\n\n @staticmethod\n def get_location(actor):\n \"\"\"\n returns the location for the given actor\n \"\"\"\n for key in CarlaDataProvider._actor_location_map:\n if key.id == actor.id:\n return CarlaDataProvider._actor_location_map[key]\n\n # We are intentionally not throwing here\n # This may cause exception loops in py_trees\n print('{}.get_location: {} not found!' .format(__name__, actor))\n return None\n\n @staticmethod\n def get_transform(actor):\n \"\"\"\n returns the transform for the given actor\n \"\"\"\n for key in CarlaDataProvider._actor_transform_map:\n if key.id == actor.id:\n return CarlaDataProvider._actor_transform_map[key]\n\n # We are intentionally not throwing here\n # This may cause exception loops in py_trees\n print('{}.get_transform: {} not found!' .format(__name__, actor))\n return None\n\n @staticmethod\n def set_client(client):\n \"\"\"\n Set the CARLA client\n \"\"\"\n CarlaDataProvider._client = client\n\n @staticmethod\n def get_client():\n \"\"\"\n Get the CARLA client\n \"\"\"\n return CarlaDataProvider._client\n\n @staticmethod\n def set_ego(vehicle):\n \"\"\"\n Set the ego vehicle\n \"\"\"\n CarlaDataProvider._ego_vehicle = vehicle\n\n @staticmethod\n def get_ego():\n \"\"\"\n Get the CARLA ego\n \"\"\"\n return CarlaDataProvider._ego_vehicle\n\n @staticmethod\n def set_world(world):\n \"\"\"\n Set the world and world settings\n \"\"\"\n CarlaDataProvider._world = world\n CarlaDataProvider._sync_flag = world.get_settings().synchronous_mode\n CarlaDataProvider._map = world.get_map()\n CarlaDataProvider._blueprint_library = world.get_blueprint_library()\n CarlaDataProvider.generate_spawn_points()\n CarlaDataProvider.prepare_map()\n\n @staticmethod\n def set_weather(weather):\n \"\"\"\n Set the weather of the world\n \"\"\"\n CarlaDataProvider._world.set_weather(weather)\n\n @staticmethod\n def get_world():\n \"\"\"\n Return world\n \"\"\"\n return CarlaDataProvider._world\n\n @staticmethod\n def get_map(world=None):\n \"\"\"\n Get the current map\n \"\"\"\n if CarlaDataProvider._map is None:\n if world is None:\n if CarlaDataProvider._world is None:\n raise ValueError(\"class member \\'world'\\' not initialized yet\")\n else:\n CarlaDataProvider._map = CarlaDataProvider._world.get_map()\n else:\n CarlaDataProvider._map = world.get_map()\n\n return CarlaDataProvider._map\n\n @staticmethod\n def is_sync_mode():\n \"\"\"\n @return true if syncronuous mode is used\n \"\"\"\n return CarlaDataProvider._sync_flag\n\n @staticmethod\n def find_weather_presets():\n \"\"\"\n Get weather presets from CARLA\n \"\"\"\n rgx = re.compile('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)')\n name = lambda x: ' '.join(m.group(0) for m in rgx.finditer(x))\n presets = [x for x in dir(carla.WeatherParameters) if re.match('[A-Z].+', x)]\n return [(getattr(carla.WeatherParameters, x), name(x)) for x in presets]\n\n @staticmethod\n def prepare_map():\n \"\"\"\n This function set the current map and loads all traffic lights for this map to\n _traffic_light_map\n \"\"\"\n if CarlaDataProvider._map is None:\n CarlaDataProvider._map = CarlaDataProvider._world.get_map()\n\n # Parse all traffic lights\n CarlaDataProvider._traffic_light_map.clear()\n for traffic_light in CarlaDataProvider._world.get_actors().filter('*traffic_light*'):\n if traffic_light not in CarlaDataProvider._traffic_light_map.keys():\n CarlaDataProvider._traffic_light_map[traffic_light] = traffic_light.get_transform()\n else:\n raise KeyError(\n \"Traffic light '{}' already registered. Cannot register twice!\".format(traffic_light.id))\n\n @staticmethod\n def annotate_trafficlight_in_group(traffic_light):\n \"\"\"\n Get dictionary with traffic light group info for a given traffic light\n \"\"\"\n dict_annotations = {'ref': [], 'opposite': [], 'left': [], 'right': []}\n\n # Get the waypoints\n ref_location = CarlaDataProvider.get_trafficlight_trigger_location(traffic_light)\n ref_waypoint = CarlaDataProvider.get_map().get_waypoint(ref_location)\n ref_yaw = ref_waypoint.transform.rotation.yaw\n\n group_tl = traffic_light.get_group_traffic_lights()\n\n for target_tl in group_tl:\n if traffic_light.id == target_tl.id:\n dict_annotations['ref'].append(target_tl)\n else:\n # Get the angle between yaws\n target_location = CarlaDataProvider.get_trafficlight_trigger_location(target_tl)\n target_waypoint = CarlaDataProvider.get_map().get_waypoint(target_location)\n target_yaw = target_waypoint.transform.rotation.yaw\n\n diff = (target_yaw - ref_yaw) % 360\n\n if diff > 330:\n continue\n elif diff > 225:\n dict_annotations['right'].append(target_tl)\n elif diff > 135.0:\n dict_annotations['opposite'].append(target_tl)\n elif diff > 30:\n dict_annotations['left'].append(target_tl)\n\n return dict_annotations\n\n @staticmethod\n def get_trafficlight_trigger_location(traffic_light): # pylint: disable=invalid-name\n \"\"\"\n Calculates the yaw of the waypoint that represents the trigger volume of the traffic light\n \"\"\"\n def rotate_point(point, angle):\n \"\"\"\n rotate a given point by a given angle\n \"\"\"\n x_ = math.cos(math.radians(angle)) * point.x - math.sin(math.radians(angle)) * point.y\n y_ = math.sin(math.radians(angle)) * point.x - math.cos(math.radians(angle)) * point.y\n\n return carla.Vector3D(x_, y_, point.z)\n\n base_transform = traffic_light.get_transform()\n base_rot = base_transform.rotation.yaw\n area_loc = base_transform.transform(traffic_light.trigger_volume.location)\n area_ext = traffic_light.trigger_volume.extent\n\n point = rotate_point(carla.Vector3D(0, 0, area_ext.z), base_rot)\n point_location = area_loc + carla.Location(x=point.x, y=point.y)\n\n return carla.Location(point_location.x, point_location.y, point_location.z)\n\n @staticmethod\n def update_light_states(ego_light, annotations, states, freeze=False, timeout=1000000000):\n \"\"\"\n Update traffic light states\n \"\"\"\n reset_params = []\n\n for state in states:\n relevant_lights = []\n if state == 'ego':\n relevant_lights = [ego_light]\n else:\n relevant_lights = annotations[state]\n for light in relevant_lights:\n prev_state = light.get_state()\n prev_green_time = light.get_green_time()\n prev_red_time = light.get_red_time()\n prev_yellow_time = light.get_yellow_time()\n reset_params.append({'light': light,\n 'state': prev_state,\n 'green_time': prev_green_time,\n 'red_time': prev_red_time,\n 'yellow_time': prev_yellow_time})\n\n light.set_state(states[state])\n if freeze:\n light.set_green_time(timeout)\n light.set_red_time(timeout)\n light.set_yellow_time(timeout)\n\n return reset_params\n\n @staticmethod\n def reset_lights(reset_params):\n \"\"\"\n Reset traffic lights\n \"\"\"\n for param in reset_params:\n param['light'].set_state(param['state'])\n param['light'].set_green_time(param['green_time'])\n param['light'].set_red_time(param['red_time'])\n param['light'].set_yellow_time(param['yellow_time'])\n\n @staticmethod\n def get_next_traffic_light(actor, use_cached_location=True):\n \"\"\"\n returns the next relevant traffic light for the provided actor\n \"\"\"\n\n if not use_cached_location:\n location = actor.get_transform().location\n else:\n location = CarlaDataProvider.get_location(actor)\n\n waypoint = CarlaDataProvider.get_map().get_waypoint(location)\n # Create list of all waypoints until next intersection\n list_of_waypoints = []\n while waypoint and not waypoint.is_intersection:\n list_of_waypoints.append(waypoint)\n waypoint = waypoint.next(2.0)\n if waypoint is not None and len(waypoint) > 0:\n waypoint = waypoint[0]\n\n # If the list is empty, the actor is in an intersection\n if not list_of_waypoints:\n return None\n\n relevant_traffic_light = None\n distance_to_relevant_traffic_light = float(\"inf\")\n\n for traffic_light in CarlaDataProvider._traffic_light_map:\n if hasattr(traffic_light, 'trigger_volume'):\n tl_t = CarlaDataProvider._traffic_light_map[traffic_light]\n transformed_tv = tl_t.transform(traffic_light.trigger_volume.location)\n distance = carla.Location(transformed_tv).distance(list_of_waypoints[-1].transform.location)\n\n if distance < distance_to_relevant_traffic_light:\n relevant_traffic_light = traffic_light\n distance_to_relevant_traffic_light = distance\n\n return relevant_traffic_light\n\n @staticmethod\n def set_ego_vehicle_route(route):\n \"\"\"\n Set the route of the ego vehicle\n\n extend ego_vehicle_route concept to support multi ego_vehicle scenarios\n \"\"\"\n CarlaDataProvider._ego_vehicle_route = route\n\n @staticmethod\n def get_ego_vehicle_route():\n \"\"\"\n returns the currently set route of the ego vehicle\n Note: Can be None\n \"\"\"\n return CarlaDataProvider._ego_vehicle_route\n\n @staticmethod\n def generate_spawn_points():\n \"\"\"\n Generate spawn points for the current map\n \"\"\"\n spawn_points = list(CarlaDataProvider.get_map(CarlaDataProvider._world).get_spawn_points())\n CarlaDataProvider._rng.shuffle(spawn_points)\n CarlaDataProvider._spawn_points = spawn_points\n CarlaDataProvider._spawn_index = 0\n\n @staticmethod\n def create_blueprint(model, rolename='scenario', color=None, actor_category=\"car\"):\n \"\"\"\n Function to setup the blueprint of an actor given its model and other relevant parameters\n \"\"\"\n\n _actor_blueprint_categories = {\n 'car': 'vehicle.tesla.model3',\n 'van': 'vehicle.volkswagen.t2',\n 'truck': 'vehicle.carlamotors.carlacola',\n 'trailer': '',\n 'semitrailer': '',\n 'bus': 'vehicle.volkswagen.t2',\n 'motorbike': 'vehicle.kawasaki.ninja',\n 'bicycle': 'vehicle.diamondback.century',\n 'train': '',\n 'tram': '',\n 'pedestrian': 'walker.pedestrian.0001',\n }\n\n # Set the model\n try:\n blueprint = CarlaDataProvider._rng.choice(CarlaDataProvider._blueprint_library.filter(model))\n except ValueError:\n # The model is not part of the blueprint library. Let's take a default one for the given category\n bp_filter = \"vehicle.*\"\n new_model = _actor_blueprint_categories[actor_category]\n if new_model != '':\n bp_filter = new_model\n print(\"WARNING: Actor model {} not available. Using instead {}\".format(model, new_model))\n blueprint = CarlaDataProvider._rng.choice(CarlaDataProvider._blueprint_library.filter(bp_filter))\n\n # Set the color\n if color:\n if not blueprint.has_attribute('color'):\n print(\n \"WARNING: Cannot set Color ({}) for actor {} due to missing blueprint attribute\".format(\n color, blueprint.id))\n else:\n default_color_rgba = blueprint.get_attribute('color').as_color()\n default_color = '({}, {}, {})'.format(default_color_rgba.r, default_color_rgba.g, default_color_rgba.b)\n try:\n blueprint.set_attribute('color', color)\n except ValueError:\n # Color can't be set for this vehicle\n print(\"WARNING: Color ({}) cannot be set for actor {}. Using instead: ({})\".format(\n color, blueprint.id, default_color))\n blueprint.set_attribute('color', default_color)\n else:\n if blueprint.has_attribute('color') and rolename != 'hero':\n color = CarlaDataProvider._rng.choice(blueprint.get_attribute('color').recommended_values)\n blueprint.set_attribute('color', color)\n\n # Make pedestrians mortal\n if blueprint.has_attribute('is_invincible'):\n blueprint.set_attribute('is_invincible', 'false')\n\n # Set the rolename\n if blueprint.has_attribute('role_name'):\n blueprint.set_attribute('role_name', rolename)\n\n return blueprint\n\n @staticmethod\n def handle_actor_batch(batch):\n \"\"\"\n Forward a CARLA command batch to spawn actors to CARLA, and gather the responses.\n Returns list of actors on success, none otherwise\n \"\"\"\n\n actors = []\n\n sync_mode = CarlaDataProvider.is_sync_mode()\n\n if CarlaDataProvider._client and batch is not None:\n responses = CarlaDataProvider._client.apply_batch_sync(batch, sync_mode)\n else:\n return None\n\n # wait for the actors to be spawned properly before we do anything\n if sync_mode:\n CarlaDataProvider._world.tick()\n else:\n CarlaDataProvider._world.wait_for_tick()\n\n actor_ids = []\n if responses:\n for response in responses:\n if not response.error:\n actor_ids.append(response.actor_id)\n\n carla_actors = CarlaDataProvider._world.get_actors(actor_ids)\n for actor in carla_actors:\n actors.append(actor)\n\n return actors\n\n @staticmethod\n def request_new_actor(model, spawn_point, rolename='scenario', autopilot=False,\n random_location=False, color=None, actor_category=\"car\"):\n \"\"\"\n This method tries to create a new actor, returning it if successful (None otherwise).\n \"\"\"\n blueprint = CarlaDataProvider.create_blueprint(model, rolename, color, actor_category)\n\n if random_location:\n actor = None\n while not actor:\n spawn_point = CarlaDataProvider._rng.choice(CarlaDataProvider._spawn_points)\n actor = CarlaDataProvider._world.try_spawn_actor(blueprint, spawn_point)\n\n else:\n # slightly lift the actor to avoid collisions with ground when spawning the actor\n # DO NOT USE spawn_point directly, as this will modify spawn_point permanently\n _spawn_point = carla.Transform(carla.Location(), spawn_point.rotation)\n _spawn_point.location.x = spawn_point.location.x\n _spawn_point.location.y = spawn_point.location.y\n _spawn_point.location.z = spawn_point.location.z + 0.2\n actor = CarlaDataProvider._world.try_spawn_actor(blueprint, _spawn_point)\n\n if actor is None:\n raise RuntimeError(\n \"Error: Unable to spawn vehicle {} at {}\".format(blueprint.id, spawn_point))\n else:\n # Let's deactivate the autopilot of the actor if it belongs to vehicle\n if actor in CarlaDataProvider._blueprint_library.filter('vehicle.*'):\n actor.set_autopilot(autopilot)\n else:\n pass\n\n # wait for the actor to be spawned properly before we do anything\n if CarlaDataProvider.is_sync_mode():\n CarlaDataProvider._world.tick()\n else:\n CarlaDataProvider._world.wait_for_tick()\n\n if actor is None:\n return None\n\n CarlaDataProvider._carla_actor_pool[actor.id] = actor\n CarlaDataProvider.register_actor(actor)\n return actor\n\n @staticmethod\n def request_new_actors(actor_list):\n \"\"\"\n This method tries to series of actor in batch. If this was successful,\n the new actors are returned, None otherwise.\n\n param:\n - actor_list: list of ActorConfigurationData\n \"\"\"\n\n SpawnActor = carla.command.SpawnActor # pylint: disable=invalid-name\n PhysicsCommand = carla.command.SetSimulatePhysics # pylint: disable=invalid-name\n FutureActor = carla.command.FutureActor # pylint: disable=invalid-name\n ApplyTransform = carla.command.ApplyTransform # pylint: disable=invalid-name\n SetAutopilot = carla.command.SetAutopilot # pylint: disable=invalid-name\n\n batch = []\n actors = []\n\n CarlaDataProvider.generate_spawn_points()\n\n for actor in actor_list:\n\n # Get the blueprint\n blueprint = CarlaDataProvider.create_blueprint(actor.model, actor.rolename, actor.color, actor.category)\n\n # Get the spawn point\n transform = actor.transform\n if actor.random_location:\n if CarlaDataProvider._spawn_index >= len(CarlaDataProvider._spawn_points):\n print(\"No more spawn points to use\")\n break\n else:\n _spawn_point = CarlaDataProvider._spawn_points[CarlaDataProvider._spawn_index]\n CarlaDataProvider._spawn_index += 1\n\n else:\n _spawn_point = carla.Transform()\n _spawn_point.rotation = transform.rotation\n _spawn_point.location.x = transform.location.x\n _spawn_point.location.y = transform.location.y\n _spawn_point.location.z = transform.location.z + 0.2\n\n # Get the command\n command = SpawnActor(blueprint, _spawn_point)\n command.then(SetAutopilot(FutureActor, actor.autopilot,\n CarlaDataProvider._traffic_manager_port))\n\n if actor.category == 'misc':\n command.then(PhysicsCommand(FutureActor, True))\n elif actor.args is not None and 'physics' in actor.args and actor.args['physics'] == \"off\":\n command.then(ApplyTransform(FutureActor, _spawn_point)).then(PhysicsCommand(FutureActor, False))\n\n batch.append(command)\n\n actors = CarlaDataProvider.handle_actor_batch(batch)\n\n if not actors:\n return None\n\n for actor in actors:\n if actor is None:\n continue\n CarlaDataProvider._carla_actor_pool[actor.id] = actor\n CarlaDataProvider.register_actor(actor)\n return actors\n\n @staticmethod\n def request_new_batch_actors(model, amount, spawn_points, autopilot=False,\n random_location=False, rolename='scenario'):\n \"\"\"\n Simplified version of \"request_new_actors\". This method also create several actors in batch.\n\n Instead of needing a list of ActorConfigurationData, an \"amount\" parameter is used.\n This makes actor spawning easier but reduces the amount of configurability.\n\n Some parameters are the same for all actors (rolename, autopilot and random location)\n while others are randomized (color)\n \"\"\"\n\n SpawnActor = carla.command.SpawnActor # pylint: disable=invalid-name\n SetAutopilot = carla.command.SetAutopilot # pylint: disable=invalid-name\n FutureActor = carla.command.FutureActor # pylint: disable=invalid-name\n\n CarlaDataProvider.generate_spawn_points()\n\n batch = []\n\n for i in range(amount):\n # Get vehicle by model\n blueprint = CarlaDataProvider.create_blueprint(model, rolename)\n\n if random_location:\n if CarlaDataProvider._spawn_index >= len(CarlaDataProvider._spawn_points):\n print(\"No more spawn points to use. Spawned {} actors out of {}\".format(i + 1, amount))\n break\n else:\n spawn_point = CarlaDataProvider._spawn_points[CarlaDataProvider._spawn_index]\n CarlaDataProvider._spawn_index += 1\n else:\n try:\n spawn_point = spawn_points[i]\n except IndexError:\n print(\"The amount of spawn points is lower than the amount of vehicles spawned\")\n break\n\n if spawn_point:\n batch.append(SpawnActor(blueprint, spawn_point).then(\n SetAutopilot(FutureActor, autopilot,\n CarlaDataProvider._traffic_manager_port)))\n\n actors = CarlaDataProvider.handle_actor_batch(batch)\n\n if actors is None:\n return None\n\n for actor in actors:\n if actor is None:\n continue\n CarlaDataProvider._carla_actor_pool[actor.id] = actor\n CarlaDataProvider.register_actor(actor)\n return actors\n\n @staticmethod\n def get_actors():\n \"\"\"\n Return list of actors and their ids\n\n Note: iteritems from six is used to allow compatibility with Python 2 and 3\n \"\"\"\n return iteritems(CarlaDataProvider._carla_actor_pool)\n\n @staticmethod\n def actor_id_exists(actor_id):\n \"\"\"\n Check if a certain id is still at the simulation\n \"\"\"\n if actor_id in CarlaDataProvider._carla_actor_pool:\n return True\n\n return False\n\n @staticmethod\n def get_hero_actor():\n \"\"\"\n Get the actor object of the hero actor if it exists, returns none otherwise.\n \"\"\"\n for actor_id in CarlaDataProvider._carla_actor_pool:\n if CarlaDataProvider._carla_actor_pool[actor_id].attributes['role_name'] == 'hero':\n return CarlaDataProvider._carla_actor_pool[actor_id]\n return None\n\n @staticmethod\n def get_actor_by_id(actor_id):\n \"\"\"\n Get an actor from the pool by using its ID. If the actor\n does not exist, None is returned.\n \"\"\"\n if actor_id in CarlaDataProvider._carla_actor_pool:\n return CarlaDataProvider._carla_actor_pool[actor_id]\n\n print(\"Non-existing actor id {}\".format(actor_id))\n return None\n\n @staticmethod\n def remove_actor_by_id(actor_id):\n \"\"\"\n Remove an actor from the pool using its ID\n \"\"\"\n if actor_id in CarlaDataProvider._carla_actor_pool:\n CarlaDataProvider._carla_actor_pool[actor_id].destroy()\n CarlaDataProvider._carla_actor_pool[actor_id] = None\n CarlaDataProvider._carla_actor_pool.pop(actor_id)\n else:\n print(\"Trying to remove a non-existing actor id {}\".format(actor_id))\n\n @staticmethod\n def remove_actors_in_surrounding(location, distance):\n \"\"\"\n Remove all actors from the pool that are closer than distance to the\n provided location\n \"\"\"\n for actor_id in CarlaDataProvider._carla_actor_pool.copy():\n if CarlaDataProvider._carla_actor_pool[actor_id].get_location().distance(location) < distance:\n CarlaDataProvider._carla_actor_pool[actor_id].destroy()\n CarlaDataProvider._carla_actor_pool.pop(actor_id)\n\n # Remove all keys with None values\n CarlaDataProvider._carla_actor_pool = dict({k: v for k, v in CarlaDataProvider._carla_actor_pool.items() if v})\n\n @staticmethod\n def get_traffic_manager_port():\n \"\"\"\n Get the port of the traffic manager.\n \"\"\"\n return CarlaDataProvider._traffic_manager_port\n\n @staticmethod\n def set_traffic_manager_port(tm_port):\n \"\"\"\n Set the port to use for the traffic manager.\n \"\"\"\n CarlaDataProvider._traffic_manager_port = tm_port\n \n @staticmethod\n def set_time_step(time_step):\n CarlaDataProvider._time_step = time_step\n \n @staticmethod\n def get_time_step():\n \"\"\"\n Set the time-step for debug.\n \"\"\"\n return CarlaDataProvider._time_step\n\n @staticmethod\n def cleanup():\n \"\"\"\n Obtain the time-step for debug. \n \"\"\"\n DestroyActor = carla.command.DestroyActor # pylint: disable=invalid-name\n batch = []\n\n for actor_id in CarlaDataProvider._carla_actor_pool.copy():\n actor = CarlaDataProvider._carla_actor_pool[actor_id]\n if actor.is_alive:\n batch.append(DestroyActor(actor))\n\n if CarlaDataProvider._client:\n try:\n CarlaDataProvider._client.apply_batch_sync(batch)\n except RuntimeError as e:\n if \"time-out\" in str(e):\n pass\n else:\n raise e\n\n CarlaDataProvider._actor_velocity_map.clear()\n CarlaDataProvider._actor_location_map.clear()\n CarlaDataProvider._actor_transform_map.clear()\n CarlaDataProvider._traffic_light_map.clear()\n CarlaDataProvider._map = None\n CarlaDataProvider._world = None\n CarlaDataProvider._sync_flag = False\n CarlaDataProvider._ego_vehicle_route = None\n CarlaDataProvider._carla_actor_pool = dict()\n CarlaDataProvider._client = None\n CarlaDataProvider._spawn_points = None\n CarlaDataProvider._spawn_index = 0\n CarlaDataProvider._ego_vehicle = None\n CarlaDataProvider._rng = random.RandomState(2000)\n" }, { "path": "scenario_runner/srunner/scenariomanager/result_writer.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module contains the result gatherer and write for CARLA scenarios.\nIt shall be used from the ScenarioManager only.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport time\nfrom tabulate import tabulate\n\n\nclass ResultOutputProvider(object):\n\n \"\"\"\n This module contains the _result gatherer and write for CARLA scenarios.\n It shall be used from the ScenarioManager only.\n \"\"\"\n\n def __init__(self, data, result, stdout=True, filename=None, junit=None):\n \"\"\"\n Setup all parameters\n - _data contains all scenario-related information\n - _result is overall pass/fail info\n - _stdout (True/False) is used to (de)activate terminal output\n - _filename is used to (de)activate file output in tabular form\n - _junit is used to (de)activate file output in _junit form\n \"\"\"\n self._data = data\n self._result = result\n self._stdout = stdout\n self._filename = filename\n self._junit = junit\n\n self._start_time = time.strftime('%Y-%m-%d %H:%M:%S',\n time.localtime(self._data.start_system_time))\n self._end_time = time.strftime('%Y-%m-%d %H:%M:%S',\n time.localtime(self._data.end_system_time))\n\n def write(self):\n \"\"\"\n Public write function\n \"\"\"\n if self._junit is not None:\n self._write_to_junit()\n\n output = self.create_output_text()\n if self._filename is not None:\n with open(self._filename, 'w') as fd:\n fd.write(output)\n if self._stdout:\n print(output)\n\n def create_output_text(self):\n \"\"\"\n Creates the output message\n \"\"\"\n output = \"\\n\"\n output += \" ======= Results of Scenario: {} ---- {} =======\\n\".format(\n self._data.scenario_tree.name, self._result)\n end_line_length = len(output) - 3\n output += \"\\n\"\n\n # Lis of all the actors\n output += \" > Ego vehicles:\\n\"\n for ego_vehicle in self._data.ego_vehicles:\n output += \"{}; \".format(ego_vehicle)\n output += \"\\n\\n\"\n\n output += \" > Other actors:\\n\"\n for actor in self._data.other_actors:\n output += \"{}; \".format(actor)\n output += \"\\n\\n\"\n\n # Simulation part\n output += \" > Simulation Information\\n\"\n\n system_time = round(self._data.scenario_duration_system, 2)\n game_time = round(self._data.scenario_duration_game, 2)\n ratio = round(self._data.scenario_duration_game / self._data.scenario_duration_system, 3)\n\n list_statistics = [[\"Start Time\", \"{}\".format(self._start_time)]]\n list_statistics.extend([[\"End Time\", \"{}\".format(self._end_time)]])\n list_statistics.extend([[\"Duration (System Time)\", \"{}s\".format(system_time)]])\n list_statistics.extend([[\"Duration (Game Time)\", \"{}s\".format(game_time)]])\n list_statistics.extend([[\"Ratio (System Time / Game Time)\", \"{}s\".format(ratio)]])\n\n output += tabulate(list_statistics, tablefmt='fancy_grid')\n output += \"\\n\\n\"\n\n # Criteria part\n output += \" > Criteria Information\\n\"\n header = ['Actor', 'Criterion', 'Result', 'Actual Value', 'Expected Value']\n list_statistics = [header]\n\n for criterion in self._data.scenario.get_criteria():\n name_string = criterion.name\n if criterion.optional:\n name_string += \" (Opt.)\"\n else:\n name_string += \" (Req.)\"\n\n actor = \"{} (id={})\".format(criterion.actor.type_id[8:], criterion.actor.id)\n criteria = name_string\n result = \"FAILURE\" if criterion.test_status == \"RUNNING\" else criterion.test_status\n actual_value = criterion.actual_value\n expected_value = criterion.expected_value_success\n\n list_statistics.extend([[actor, criteria, result, actual_value, expected_value]])\n\n # Timeout\n actor = \"\"\n criteria = \"Timeout (Req.)\"\n result = \"SUCCESS\" if self._data.scenario_duration_game < self._data.scenario.timeout else \"FAILURE\"\n actual_value = round(self._data.scenario_duration_game, 2)\n expected_value = round(self._data.scenario.timeout, 2)\n\n list_statistics.extend([[actor, criteria, result, actual_value, expected_value]])\n\n # Global and final output message\n list_statistics.extend([['', 'GLOBAL RESULT', self._result, '', '']])\n\n output += tabulate(list_statistics, tablefmt='fancy_grid')\n output += \"\\n\"\n output += \" \" + \"=\" * end_line_length + \"\\n\"\n\n return output\n\n def _write_to_junit(self):\n \"\"\"\n Writing to Junit XML\n \"\"\"\n test_count = 0\n failure_count = 0\n for criterion in self._data.scenario.get_criteria():\n test_count += 1\n if criterion.test_status != \"SUCCESS\":\n failure_count += 1\n\n # handle timeout\n test_count += 1\n if self._data.scenario_duration_game >= self._data.scenario.timeout:\n failure_count += 1\n\n junit_file = open(self._junit, \"w\")\n\n junit_file.write(\"\\n\")\n\n test_suites_string = (\"\\n\" %\n (test_count,\n failure_count,\n self._start_time,\n self._data.scenario_duration_system))\n junit_file.write(test_suites_string)\n\n test_suite_string = (\" \\n\" %\n (self._data.scenario_tree.name,\n test_count,\n failure_count,\n self._data.scenario_duration_system))\n junit_file.write(test_suite_string)\n\n for criterion in self._data.scenario.get_criteria():\n testcase_name = criterion.name + \"_\" + \\\n criterion.actor.type_id[8:] + \"_\" + str(criterion.actor.id)\n result_string = (\" \\n\".format(\n testcase_name, self._data.scenario_tree.name))\n if criterion.test_status != \"SUCCESS\":\n result_string += \" \\n\".format(\n criterion.name, criterion.actual_value)\n else:\n result_string += \" Exact Value: {} = {}\\n\".format(\n criterion.name, criterion.actual_value)\n result_string += \" \\n\"\n junit_file.write(result_string)\n\n # Handle timeout separately\n result_string = (\" \\n\".format(\n self._data.scenario_duration_system,\n self._data.scenario_tree.name))\n if self._data.scenario_duration_game >= self._data.scenario.timeout:\n result_string += \" \\n\".format(\n \"Duration\", self._data.scenario_duration_game)\n else:\n result_string += \" Exact Value: {} = {}\\n\".format(\n \"Duration\", self._data.scenario_duration_game)\n result_string += \" \\n\"\n junit_file.write(result_string)\n\n junit_file.write(\" \\n\")\n junit_file.write(\"\\n\")\n junit_file.close()\n" }, { "path": "scenario_runner/srunner/scenariomanager/scenario_manager.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides the ScenarioManager implementation.\nIt must not be modified and is for reference only!\n\"\"\"\n\nfrom __future__ import print_function\nimport sys\nimport time\n\nimport py_trees\n\nfrom srunner.autoagents.agent_wrapper import AgentWrapper\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.result_writer import ResultOutputProvider\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenariomanager.watchdog import Watchdog\n\n\nclass ScenarioManager(object):\n\n \"\"\"\n Basic scenario manager class. This class holds all functionality\n required to start, and analyze a scenario.\n\n The user must not modify this class.\n\n To use the ScenarioManager:\n 1. Create an object via manager = ScenarioManager()\n 2. Load a scenario via manager.load_scenario()\n 3. Trigger the execution of the scenario manager.run_scenario()\n This function is designed to explicitly control start and end of\n the scenario execution\n 4. Trigger a result evaluation with manager.analyze_scenario()\n 5. If needed, cleanup with manager.stop_scenario()\n \"\"\"\n\n def __init__(self, debug_mode=False, sync_mode=False, timeout=2.0):\n \"\"\"\n Setups up the parameters, which will be filled at load_scenario()\n\n \"\"\"\n self.scenario = None\n self.scenario_tree = None\n self.scenario_class = None\n self.ego_vehicles = None\n self.other_actors = None\n\n self._debug_mode = debug_mode\n self._agent = None\n self._sync_mode = sync_mode\n self._running = False\n self._timestamp_last_run = 0.0\n self._timeout = timeout\n self._watchdog = Watchdog(float(self._timeout))\n\n self.scenario_duration_system = 0.0\n self.scenario_duration_game = 0.0\n self.start_system_time = None\n self.end_system_time = None\n\n def _reset(self):\n \"\"\"\n Reset all parameters\n \"\"\"\n self._running = False\n self._timestamp_last_run = 0.0\n self.scenario_duration_system = 0.0\n self.scenario_duration_game = 0.0\n self.start_system_time = None\n self.end_system_time = None\n GameTime.restart()\n\n def cleanup(self):\n \"\"\"\n This function triggers a proper termination of a scenario\n \"\"\"\n\n if self.scenario is not None:\n self.scenario.terminate()\n\n if self._agent is not None:\n self._agent.cleanup()\n self._agent = None\n\n CarlaDataProvider.cleanup()\n\n def load_scenario(self, scenario, agent=None):\n \"\"\"\n Load a new scenario\n \"\"\"\n self._reset()\n self._agent = AgentWrapper(agent) if agent else None\n if self._agent is not None:\n self._sync_mode = True\n self.scenario_class = scenario\n self.scenario = scenario.scenario\n self.scenario_tree = self.scenario.scenario_tree\n self.ego_vehicles = scenario.ego_vehicles\n self.other_actors = scenario.other_actors\n\n # To print the scenario tree uncomment the next line\n # py_trees.display.render_dot_tree(self.scenario_tree)\n\n if self._agent is not None:\n self._agent.setup_sensors(self.ego_vehicles[0], self._debug_mode)\n\n def run_scenario(self):\n \"\"\"\n Trigger the start of the scenario and wait for it to finish/fail\n \"\"\"\n print(\"ScenarioManager: Running scenario {}\".format(self.scenario_tree.name))\n self.start_system_time = time.time()\n start_game_time = GameTime.get_time()\n\n self._watchdog.start()\n self._running = True\n\n while self._running:\n timestamp = None\n world = CarlaDataProvider.get_world()\n if world:\n snapshot = world.get_snapshot()\n if snapshot:\n timestamp = snapshot.timestamp\n if timestamp:\n self._tick_scenario(timestamp)\n\n self._watchdog.stop()\n\n self.cleanup()\n\n self.end_system_time = time.time()\n end_game_time = GameTime.get_time()\n\n self.scenario_duration_system = self.end_system_time - \\\n self.start_system_time\n self.scenario_duration_game = end_game_time - start_game_time\n\n if self.scenario_tree.status == py_trees.common.Status.FAILURE:\n print(\"ScenarioManager: Terminated due to failure\")\n\n def _tick_scenario(self, timestamp):\n \"\"\"\n Run next tick of scenario and the agent.\n If running synchornously, it also handles the ticking of the world.\n \"\"\"\n\n if self._timestamp_last_run < timestamp.elapsed_seconds and self._running:\n self._timestamp_last_run = timestamp.elapsed_seconds\n\n self._watchdog.update()\n\n if self._debug_mode:\n print(\"\\n--------- Tick ---------\\n\")\n\n # Update game time and actor information\n GameTime.on_carla_tick(timestamp)\n CarlaDataProvider.on_carla_tick()\n\n if self._agent is not None:\n ego_action = self._agent()\n\n # Tick scenario\n self.scenario_tree.tick_once()\n\n if self._debug_mode:\n print(\"\\n\")\n py_trees.display.print_ascii_tree(self.scenario_tree, show_status=True)\n sys.stdout.flush()\n\n if self.scenario_tree.status != py_trees.common.Status.RUNNING:\n self._running = False\n\n if self._agent is not None:\n self.ego_vehicles[0].apply_control(ego_action)\n\n if self._sync_mode and self._running and self._watchdog.get_status():\n CarlaDataProvider.get_world().tick()\n\n def get_running_status(self):\n \"\"\"\n returns:\n bool: False if watchdog exception occured, True otherwise\n \"\"\"\n return self._watchdog.get_status()\n\n def stop_scenario(self):\n \"\"\"\n This function is used by the overall signal handler to terminate the scenario execution\n \"\"\"\n self._running = False\n\n def analyze_scenario(self, stdout, filename, junit):\n \"\"\"\n This function is intended to be called from outside and provide\n the final statistics about the scenario (human-readable, in form of a junit\n report, etc.)\n \"\"\"\n\n failure = False\n timeout = False\n result = \"SUCCESS\"\n\n if self.scenario.test_criteria is None:\n print(\"Nothing to analyze, this scenario has no criteria\")\n return True\n\n for criterion in self.scenario.get_criteria():\n if (not criterion.optional and\n criterion.test_status != \"SUCCESS\" and\n criterion.test_status != \"ACCEPTABLE\"):\n failure = True\n result = \"FAILURE\"\n elif criterion.test_status == \"ACCEPTABLE\":\n result = \"ACCEPTABLE\"\n\n if self.scenario.timeout_node.timeout and not failure:\n timeout = True\n result = \"TIMEOUT\"\n\n output = ResultOutputProvider(self, result, stdout, filename, junit)\n output.write()\n\n return failure or timeout\n" }, { "path": "scenario_runner/srunner/scenariomanager/scenarioatomics/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/scenariomanager/scenarioatomics/atomic_behaviors.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides all atomic scenario behaviors required to realize\ncomplex, realistic scenarios such as \"follow a leading vehicle\", \"lane change\",\netc.\n\nThe atomic behaviors are implemented with py_trees.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport copy\nimport math\nimport operator\nimport os\nimport random\nimport time\nimport subprocess\n\nimport numpy as np\nimport py_trees\nfrom py_trees.blackboard import Blackboard\n\nimport carla\nfrom agents.navigation.basic_agent import BasicAgent, LocalPlanner\nfrom agents.navigation.local_planner import RoadOption\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.actorcontrols.actor_control import ActorControl\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.tools.scenario_helper import detect_lane_obstacle\nfrom srunner.tools.scenario_helper import generate_target_waypoint_list_multilane\n\n\nimport srunner.tools\n\nEPSILON = 0.001\n\n\ndef calculate_distance(location, other_location, global_planner=None):\n \"\"\"\n Method to calculate the distance between to locations\n\n Note: It uses the direct distance between the current location and the\n target location to estimate the time to arrival.\n To be accurate, it would have to use the distance along the\n (shortest) route between the two locations.\n \"\"\"\n if global_planner:\n distance = 0\n\n # Get the route\n route = global_planner.trace_route(location, other_location)\n\n # Get the distance of the route\n for i in range(1, len(route)):\n curr_loc = route[i][0].transform.location\n prev_loc = route[i - 1][0].transform.location\n\n distance += curr_loc.distance(prev_loc)\n\n return distance\n\n return location.distance(other_location)\n\n\ndef get_actor_control(actor):\n \"\"\"\n Method to return the type of control to the actor.\n \"\"\"\n control = actor.get_control()\n actor_type = actor.type_id.split('.')[0]\n if not isinstance(actor, carla.Walker):\n control.steering = 0\n else:\n control.speed = 0\n\n return control, actor_type\n\n\nclass AtomicBehavior(py_trees.behaviour.Behaviour):\n\n \"\"\"\n Base class for all atomic behaviors used to setup a scenario\n\n *All behaviors should use this class as parent*\n\n Important parameters:\n - name: Name of the atomic behavior\n \"\"\"\n\n def __init__(self, name, actor=None):\n \"\"\"\n Default init. Has to be called via super from derived class\n \"\"\"\n super(AtomicBehavior, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self.name = name\n self._actor = actor\n\n def setup(self, unused_timeout=15):\n \"\"\"\n Default setup\n \"\"\"\n self.logger.debug(\"%s.setup()\" % (self.__class__.__name__))\n return True\n\n def initialise(self):\n \"\"\"\n Initialise setup terminates WaypointFollowers\n Check whether WF for this actor is running and terminate all active WFs\n \"\"\"\n if self._actor is not None:\n try:\n check_attr = operator.attrgetter(\"running_WF_actor_{}\".format(self._actor.id))\n terminate_wf = copy.copy(check_attr(py_trees.blackboard.Blackboard()))\n py_trees.blackboard.Blackboard().set(\n \"terminate_WF_actor_{}\".format(self._actor.id), terminate_wf, overwrite=True)\n except AttributeError:\n # It is ok to continue, if the Blackboard variable does not exist\n pass\n self.logger.debug(\"%s.initialise()\" % (self.__class__.__name__))\n\n def terminate(self, new_status):\n \"\"\"\n Default terminate. Can be extended in derived class\n \"\"\"\n self.logger.debug(\"%s.terminate()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n\nclass RunScript(AtomicBehavior):\n\n \"\"\"\n This is an atomic behavior to start execution of an additional script.\n\n Args:\n script (str): String containing the interpreter, scriptpath and arguments\n Example: \"python /path/to/script.py --arg1\"\n base_path (str): String containing the base path of for the script\n\n Attributes:\n _script (str): String containing the interpreter, scriptpath and arguments\n Example: \"python /path/to/script.py --arg1\"\n _base_path (str): String containing the base path of for the script\n Example: \"/path/to/\"\n\n Note:\n This is intended for the use with OpenSCENARIO. Be aware of security side effects.\n \"\"\"\n\n def __init__(self, script, base_path=None, name=\"RunScript\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(RunScript, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._script = script\n self._base_path = base_path\n\n def update(self):\n \"\"\"\n Start script\n \"\"\"\n path = None\n script_components = self._script.split(' ')\n if len(script_components) > 1:\n path = script_components[1]\n\n if not os.path.isfile(path):\n path = os.path.join(self._base_path, path)\n if not os.path.isfile(path):\n new_status = py_trees.common.Status.FAILURE\n print(\"Script file does not exists {}\".format(path))\n else:\n subprocess.Popen(self._script, shell=True, cwd=self._base_path)\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n\nclass ChangeWeather(AtomicBehavior):\n\n \"\"\"\n Atomic to write a new weather configuration into the blackboard.\n Used in combination with WeatherBehavior() to have a continuous weather simulation.\n\n The behavior immediately terminates with SUCCESS after updating the blackboard.\n\n Args:\n weather (srunner.scenariomanager.weather_sim.Weather): New weather settings.\n name (string): Name of the behavior.\n Defaults to 'UpdateWeather'.\n\n Attributes:\n _weather (srunner.scenariomanager.weather_sim.Weather): Weather settings.\n \"\"\"\n\n def __init__(self, weather, name=\"ChangeWeather\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeWeather, self).__init__(name)\n self._weather = weather\n\n def update(self):\n \"\"\"\n Write weather into blackboard and exit with success\n\n returns:\n py_trees.common.Status.SUCCESS\n \"\"\"\n py_trees.blackboard.Blackboard().set(\"CarlaWeather\", self._weather, overwrite=True)\n return py_trees.common.Status.SUCCESS\n\n\nclass ChangeRoadFriction(AtomicBehavior):\n\n \"\"\"\n Atomic to update the road friction in CARLA.\n\n The behavior immediately terminates with SUCCESS after updating the friction.\n\n Args:\n friction (float): New friction coefficient.\n name (string): Name of the behavior.\n Defaults to 'UpdateRoadFriction'.\n\n Attributes:\n _friction (float): Friction coefficient.\n \"\"\"\n\n def __init__(self, friction, name=\"ChangeRoadFriction\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeRoadFriction, self).__init__(name)\n self._friction = friction\n\n def update(self):\n \"\"\"\n Update road friction. Spawns new friction blueprint and removes the old one, if existing.\n\n returns:\n py_trees.common.Status.SUCCESS\n \"\"\"\n\n for actor in CarlaDataProvider.get_world().get_actors().filter('static.trigger.friction'):\n actor.destroy()\n\n friction_bp = CarlaDataProvider.get_world().get_blueprint_library().find('static.trigger.friction')\n extent = carla.Location(1000000.0, 1000000.0, 1000000.0)\n friction_bp.set_attribute('friction', str(self._friction))\n friction_bp.set_attribute('extent_x', str(extent.x))\n friction_bp.set_attribute('extent_y', str(extent.y))\n friction_bp.set_attribute('extent_z', str(extent.z))\n\n # Spawn Trigger Friction\n transform = carla.Transform()\n transform.location = carla.Location(-10000.0, -10000.0, 0.0)\n CarlaDataProvider.get_world().spawn_actor(friction_bp, transform)\n\n return py_trees.common.Status.SUCCESS\n\n\nclass ChangeActorControl(AtomicBehavior):\n\n \"\"\"\n Atomic to change the longitudinal/lateral control logic for an actor.\n The (actor, controller) pair is stored inside the Blackboard.\n\n The behavior immediately terminates with SUCCESS after the controller.\n\n Args:\n actor (carla.Actor): Actor that should be controlled by the controller.\n control_py_module (string): Name of the python module containing the implementation\n of the controller.\n args (dictionary): Additional arguments for the controller.\n name (string): Name of the behavior.\n Defaults to 'ChangeActorControl'.\n\n Attributes:\n _actor_control (ActorControl): Instance of the actor control.\n \"\"\"\n\n def __init__(self, actor, control_py_module, args, name=\"ChangeActorControl\"):\n \"\"\"\n Setup actor controller.\n \"\"\"\n super(ChangeActorControl, self).__init__(name, actor)\n\n self._actor_control = ActorControl(actor, control_py_module=control_py_module, args=args)\n\n def update(self):\n \"\"\"\n Write (actor, controler) pair to Blackboard, or update the controller\n if actor already exists as a key.\n\n returns:\n py_trees.common.Status.SUCCESS\n \"\"\"\n\n actor_dict = {}\n\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if actor_dict:\n if self._actor.id in actor_dict:\n actor_dict[self._actor.id].reset()\n\n actor_dict[self._actor.id] = self._actor_control\n py_trees.blackboard.Blackboard().set(\"ActorsWithController\", actor_dict, overwrite=True)\n\n return py_trees.common.Status.SUCCESS\n\n\nclass UpdateAllActorControls(AtomicBehavior):\n\n \"\"\"\n Atomic to update (run one control loop step) all actor controls.\n\n The behavior is always in RUNNING state.\n\n Args:\n name (string): Name of the behavior.\n Defaults to 'UpdateAllActorControls'.\n \"\"\"\n\n def __init__(self, name=\"UpdateAllActorControls\"):\n \"\"\"\n Constructor\n \"\"\"\n super(UpdateAllActorControls, self).__init__(name)\n\n def update(self):\n \"\"\"\n Execute one control loop step for all actor controls.\n\n returns:\n py_trees.common.Status.RUNNING\n \"\"\"\n\n actor_dict = {}\n\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n for actor_id in actor_dict:\n actor_dict[actor_id].run_step()\n\n return py_trees.common.Status.RUNNING\n\n\nclass ChangeActorTargetSpeed(AtomicBehavior):\n\n \"\"\"\n Atomic to change the target speed for an actor controller.\n\n The behavior is in RUNNING state until the distance/duration\n conditions are satisfied, or if a second ChangeActorTargetSpeed atomic\n for the same actor is triggered.\n\n Args:\n actor (carla.Actor): Controlled actor.\n target_speed (float): New target speed [m/s].\n init_speed (boolean): Flag to indicate if the speed is the initial actor speed.\n Defaults to False.\n duration (float): Duration of the maneuver [s].\n Defaults to None.\n distance (float): Distance of the maneuver [m].\n Defaults to None.\n relative_actor (carla.Actor): If the target speed setting should be relative to another actor.\n Defaults to None.\n value (float): Offset, if the target speed setting should be relative to another actor.\n Defaults to None.\n value_type (string): Either 'Delta' or 'Factor' influencing how the offset to the reference actors\n velocity is applied. Defaults to None.\n continuous (boolean): If True, the atomic remains in RUNNING, independent of duration or distance.\n Defaults to False.\n name (string): Name of the behavior.\n Defaults to 'ChangeActorTargetSpeed'.\n\n Attributes:\n _target_speed (float): New target speed [m/s].\n _init_speed (boolean): Flag to indicate if the speed is the initial actor speed.\n Defaults to False.\n _start_time (float): Start time of the atomic [s].\n Defaults to None.\n _start_location (carla.Location): Start location of the atomic.\n Defaults to None.\n _duration (float): Duration of the maneuver [s].\n Defaults to None.\n _distance (float): Distance of the maneuver [m].\n Defaults to None.\n _relative_actor (carla.Actor): If the target speed setting should be relative to another actor.\n Defaults to None.\n _value (float): Offset, if the target speed setting should be relative to another actor.\n Defaults to None.\n _value_type (string): Either 'Delta' or 'Factor' influencing how the offset to the reference actors\n velocity is applied. Defaults to None.\n _continuous (boolean): If True, the atomic remains in RUNNING, independent of duration or distance.\n Defaults to False.\n \"\"\"\n\n def __init__(self, actor, target_speed, init_speed=False,\n duration=None, distance=None, relative_actor=None,\n value=None, value_type=None, continuous=False, name=\"ChangeActorTargetSpeed\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeActorTargetSpeed, self).__init__(name, actor)\n\n self._target_speed = target_speed\n self._init_speed = init_speed\n\n self._start_time = None\n self._start_location = None\n\n self._relative_actor = relative_actor\n self._value = value\n self._value_type = value_type\n self._continuous = continuous\n self._duration = duration\n self._distance = distance\n\n def initialise(self):\n \"\"\"\n Set initial parameters such as _start_time and _start_location,\n and get (actor, controller) pair from Blackboard.\n\n May throw if actor is not available as key for the ActorsWithController\n dictionary from Blackboard.\n \"\"\"\n actor_dict = {}\n\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n raise RuntimeError(\"Actor not found in ActorsWithController BlackBoard\")\n\n self._start_time = GameTime.get_time()\n self._start_location = CarlaDataProvider.get_location(self._actor)\n\n if self._relative_actor:\n relative_velocity = CarlaDataProvider.get_velocity(self._relative_actor)\n\n # get target velocity\n if self._value_type == 'delta':\n self._target_speed = relative_velocity + self._value\n elif self._value_type == 'factor':\n self._target_speed = relative_velocity * self._value\n else:\n print('self._value_type must be delta or factor')\n\n actor_dict[self._actor.id].update_target_speed(self._target_speed, start_time=self._start_time)\n\n if self._init_speed:\n actor_dict[self._actor.id].set_init_speed()\n\n super(ChangeActorTargetSpeed, self).initialise()\n\n def update(self):\n \"\"\"\n Check the actor's current state and update target speed, if it is relative to another actor.\n\n returns:\n py_trees.common.Status.SUCCESS, if the duration or distance driven exceeded limits\n if another ChangeActorTargetSpeed atomic for the same actor was triggered.\n py_trees.common.Status.FAILURE, if the actor is not found in ActorsWithController Blackboard dictionary.\n py_trees.common.Status.FAILURE, else.\n \"\"\"\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n return py_trees.common.Status.FAILURE\n\n if actor_dict[self._actor.id].get_last_longitudinal_command() != self._start_time:\n return py_trees.common.Status.SUCCESS\n\n new_status = py_trees.common.Status.RUNNING\n\n if self._relative_actor:\n relative_velocity = CarlaDataProvider.get_velocity(self._relative_actor)\n\n # get target velocity\n if self._value_type == 'delta':\n actor_dict[self._actor.id].update_target_speed(relative_velocity + self._value)\n elif self._value_type == 'factor':\n actor_dict[self._actor.id].update_target_speed(relative_velocity * self._value)\n else:\n print('self._value_type must be delta or factor')\n\n # check duration and driven_distance\n if not self._continuous:\n if (self._duration is not None) and (GameTime.get_time() - self._start_time > self._duration):\n new_status = py_trees.common.Status.SUCCESS\n\n driven_distance = CarlaDataProvider.get_location(self._actor).distance(self._start_location)\n if (self._distance is not None) and (driven_distance > self._distance):\n new_status = py_trees.common.Status.SUCCESS\n\n if self._distance is None and self._duration is None:\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass ChangeActorWaypoints(AtomicBehavior):\n\n \"\"\"\n Atomic to change the waypoints for an actor controller.\n\n The behavior is in RUNNING state until the last waypoint is reached, or if a\n second waypoint related atomic for the same actor is triggered. These are:\n - ChangeActorWaypoints\n - ChangeActorWaypointsToReachPosition\n - ChangeActorLateralMotion\n\n Args:\n actor (carla.Actor): Controlled actor.\n waypoints (List of carla.Transform): List of waypoints (CARLA transforms).\n name (string): Name of the behavior.\n Defaults to 'ChangeActorWaypoints'.\n\n Attributes:\n _waypoints (List of carla.Transform): List of waypoints (CARLA transforms).\n _start_time (float): Start time of the atomic [s].\n Defaults to None.\n \"\"\"\n\n def __init__(self, actor, waypoints, name=\"ChangeActorWaypoints\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeActorWaypoints, self).__init__(name, actor)\n\n self._waypoints = waypoints\n self._start_time = None\n\n def initialise(self):\n \"\"\"\n Set _start_time and get (actor, controller) pair from Blackboard.\n\n Set waypoint list for actor controller.\n\n May throw if actor is not available as key for the ActorsWithController\n dictionary from Blackboard.\n \"\"\"\n actor_dict = {}\n\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n raise RuntimeError(\"Actor not found in ActorsWithController BlackBoard\")\n\n self._start_time = GameTime.get_time()\n\n actor_dict[self._actor.id].update_waypoints(self._waypoints, start_time=self._start_time)\n\n super(ChangeActorWaypoints, self).initialise()\n\n def update(self):\n \"\"\"\n Check the actor's state along the waypoint route.\n\n returns:\n py_trees.common.Status.SUCCESS, if the final waypoint was reached, or\n if another ChangeActorWaypoints atomic for the same actor was triggered.\n py_trees.common.Status.FAILURE, if the actor is not found in ActorsWithController Blackboard dictionary.\n py_trees.common.Status.FAILURE, else.\n \"\"\"\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n return py_trees.common.Status.FAILURE\n\n if actor_dict[self._actor.id].get_last_waypoint_command() != self._start_time:\n return py_trees.common.Status.SUCCESS\n\n new_status = py_trees.common.Status.RUNNING\n\n if actor_dict[self._actor.id].check_reached_waypoint_goal():\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass ChangeActorWaypointsToReachPosition(ChangeActorWaypoints):\n\n \"\"\"\n Atomic to change the waypoints for an actor controller in order to reach\n a given position.\n\n The behavior is in RUNNING state until the last waypoint is reached, or if a\n second waypoint related atomic for the same actor is triggered. These are:\n - ChangeActorWaypoints\n - ChangeActorWaypointsToReachPosition\n - ChangeActorLateralMotion\n\n Args:\n actor (carla.Actor): Controlled actor.\n position (carla.Transform): CARLA transform to be reached by the actor.\n name (string): Name of the behavior.\n Defaults to 'ChangeActorWaypointsToReachPosition'.\n\n Attributes:\n _waypoints (List of carla.Transform): List of waypoints (CARLA transforms).\n _end_transform (carla.Transform): Final position (CARLA transform).\n _start_time (float): Start time of the atomic [s].\n Defaults to None.\n _grp (GlobalPlanner): global planner instance of the town\n \"\"\"\n\n def __init__(self, actor, position, name=\"ChangeActorWaypointsToReachPosition\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeActorWaypointsToReachPosition, self).__init__(actor, [])\n\n self._end_transform = position\n\n town_map = CarlaDataProvider.get_map()\n dao = GlobalRoutePlannerDAO(town_map, 2)\n self._grp = GlobalRoutePlanner(dao)\n self._grp.setup()\n\n def initialise(self):\n \"\"\"\n Set _start_time and get (actor, controller) pair from Blackboard.\n\n Generate a waypoint list (route) which representes the route. Set\n this waypoint list for the actor controller.\n\n May throw if actor is not available as key for the ActorsWithController\n dictionary from Blackboard.\n \"\"\"\n\n # get start position\n position_actor = CarlaDataProvider.get_location(self._actor)\n\n # calculate plan with global_route_planner function\n plan = self._grp.trace_route(position_actor, self._end_transform.location)\n\n for elem in plan:\n self._waypoints.append(elem[0].transform)\n\n super(ChangeActorWaypointsToReachPosition, self).initialise()\n\n\nclass ChangeActorLateralMotion(AtomicBehavior):\n\n \"\"\"\n Atomic to change the waypoints for an actor controller.\n\n The behavior is in RUNNING state until the last waypoint is reached, or if a\n second waypoint related atomic for the same actor is triggered. These are:\n - ChangeActorWaypoints\n - ChangeActorWaypointsToReachPosition\n - ChangeActorLateralMotion\n\n Args:\n actor (carla.Actor): Controlled actor.\n direction (string): Lane change direction ('left' or 'right').\n Defaults to 'left'.\n distance_lane_change (float): Distance of the lance change [meters].\n Defaults to 25.\n distance_other_lane (float): Driven distance after the lange change [meters].\n Defaults to 100.\n name (string): Name of the behavior.\n Defaults to 'ChangeActorLateralMotion'.\n\n Attributes:\n _waypoints (List of carla.Transform): List of waypoints representing the lane change (CARLA transforms).\n _direction (string): Lane change direction ('left' or 'right').\n _distance_same_lane (float): Distance on the same lane before the lane change starts [meters]\n Constant to 5.\n _distance_other_lane (float): Max. distance on the target lane after the lance change [meters]\n Constant to 100.\n _distance_lane_change (float): Max. total distance of the lane change [meters].\n _pos_before_lane_change: carla.Location of the actor before the lane change.\n Defaults to None.\n _target_lane_id (int): Id of the target lane\n Defaults to None.\n _start_time (float): Start time of the atomic [s].\n Defaults to None.\n \"\"\"\n\n def __init__(self, actor, direction='left', distance_lane_change=25,\n distance_other_lane=100, name=\"ChangeActorLateralMotion\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeActorLateralMotion, self).__init__(name, actor)\n\n self._waypoints = []\n self._direction = direction\n self._distance_same_lane = 5\n self._distance_other_lane = distance_other_lane\n self._distance_lane_change = distance_lane_change\n self._pos_before_lane_change = None\n self._target_lane_id = None\n\n self._start_time = None\n\n def initialise(self):\n \"\"\"\n Set _start_time and get (actor, controller) pair from Blackboard.\n\n Generate a waypoint list (route) which representes the lane change. Set\n this waypoint list for the actor controller.\n\n May throw if actor is not available as key for the ActorsWithController\n dictionary from Blackboard.\n \"\"\"\n actor_dict = {}\n\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n raise RuntimeError(\"Actor not found in ActorsWithController BlackBoard\")\n\n self._start_time = GameTime.get_time()\n\n # get start position\n position_actor = CarlaDataProvider.get_map().get_waypoint(CarlaDataProvider.get_location(self._actor))\n\n # calculate plan with scenario_helper function\n plan, self._target_lane_id = generate_target_waypoint_list_multilane(\n position_actor, self._direction, self._distance_same_lane,\n self._distance_other_lane, self._distance_lane_change, check='false')\n\n for elem in plan:\n self._waypoints.append(elem[0].transform)\n\n actor_dict[self._actor.id].update_waypoints(self._waypoints, start_time=self._start_time)\n\n super(ChangeActorLateralMotion, self).initialise()\n\n def update(self):\n \"\"\"\n Check the actor's current state and if the lane change was completed\n\n returns:\n py_trees.common.Status.SUCCESS, if lane change was successful, or\n if another ChangeActorLateralMotion atomic for the same actor was triggered.\n py_trees.common.Status.FAILURE, if the actor is not found in ActorsWithController Blackboard dictionary.\n py_trees.common.Status.FAILURE, else.\n \"\"\"\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if not actor_dict or not self._actor.id in actor_dict:\n return py_trees.common.Status.FAILURE\n\n if actor_dict[self._actor.id].get_last_waypoint_command() != self._start_time:\n return py_trees.common.Status.SUCCESS\n\n new_status = py_trees.common.Status.RUNNING\n\n current_position_actor = CarlaDataProvider.get_map().get_waypoint(self._actor.get_location())\n current_lane_id = current_position_actor.lane_id\n\n if current_lane_id == self._target_lane_id:\n # driving on new lane\n distance = current_position_actor.transform.location.distance(self._pos_before_lane_change)\n\n if distance > self._distance_other_lane:\n # long enough distance on new lane --> SUCCESS\n new_status = py_trees.common.Status.SUCCESS\n else:\n # no lane change yet\n self._pos_before_lane_change = current_position_actor.transform.location\n\n return new_status\n\n\nclass ActorTransformSetterToOSCPosition(AtomicBehavior):\n\n \"\"\"\n OpenSCENARIO atomic\n This class contains an atomic behavior to set the transform of an OpenSCENARIO actor.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - osc_position: OpenSCENARIO position\n - physics [optional]: If physics is true, the actor physics will be reactivated upon success\n\n The behavior terminates when actor is set to the new actor transform (closer than 1 meter)\n\n NOTE:\n It is very important to ensure that the actor location is spawned to the new transform because of the\n appearence of a rare runtime processing error. WaypointFollower with LocalPlanner,\n might fail if new_status is set to success before the actor is really positioned at the new transform.\n Therefore: calculate_distance(actor, transform) < 1 meter\n \"\"\"\n\n def __init__(self, actor, osc_position, physics=True, name=\"ActorTransformSetterToOSCPosition\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ActorTransformSetterToOSCPosition, self).__init__(name, actor)\n self._osc_position = osc_position\n self._physics = physics\n self._osc_transform = None\n\n def initialise(self):\n\n super(ActorTransformSetterToOSCPosition, self).initialise()\n\n if self._actor.is_alive:\n self._actor.set_target_velocity(carla.Vector3D(0, 0, 0))\n self._actor.set_target_angular_velocity(carla.Vector3D(0, 0, 0))\n\n def update(self):\n \"\"\"\n Transform actor\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n # calculate transform with method in openscenario_parser.py\n self._osc_transform = srunner.tools.openscenario_parser.OpenScenarioParser.convert_position_to_transform(\n self._osc_position)\n self._actor.set_transform(self._osc_transform)\n\n if not self._actor.is_alive:\n new_status = py_trees.common.Status.FAILURE\n\n if calculate_distance(self._actor.get_location(), self._osc_transform.location) < 1.0:\n if self._physics:\n self._actor.set_simulate_physics(enabled=True)\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass AccelerateToVelocity(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic acceleration behavior. The controlled\n traffic participant will accelerate with _throttle_value_ until reaching\n a given _target_velocity_\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - throttle_value: The amount of throttle used to accelerate in [0,1]\n - target_velocity: The target velocity the actor should reach in m/s\n\n The behavior will terminate, if the actor's velocity is at least target_velocity\n \"\"\"\n\n def __init__(self, actor, throttle_value, target_velocity, name=\"Acceleration\"):\n \"\"\"\n Setup parameters including acceleration value (via throttle_value)\n and target velocity\n \"\"\"\n super(AccelerateToVelocity, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._control, self._type = get_actor_control(actor)\n self._throttle_value = throttle_value\n self._target_velocity = target_velocity\n\n def initialise(self):\n # In case of walkers, we have to extract the current heading\n if self._type == 'walker':\n self._control.speed = self._target_velocity\n self._control.direction = CarlaDataProvider.get_transform(self._actor).get_forward_vector()\n\n super(AccelerateToVelocity, self).initialise()\n\n def update(self):\n \"\"\"\n Set throttle to throttle_value, as long as velocity is < target_velocity\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._type == 'vehicle':\n if CarlaDataProvider.get_velocity(self._actor) < self._target_velocity:\n self._control.throttle = self._throttle_value\n else:\n new_status = py_trees.common.Status.SUCCESS\n self._control.throttle = 0\n\n self._actor.apply_control(self._control)\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass AccelerateToCatchUp(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic acceleration behavior.\n The car will accelerate until it is faster than another car, in order to catch up distance.\n This behaviour is especially useful before a lane change (e.g. LaneChange atom).\n\n Important parameters:\n - actor: CARLA actor to execute the behaviour\n - other_actor: Reference CARLA actor, actor you want to catch up to\n - throttle_value: acceleration value between 0.0 and 1.0\n - delta_velocity: speed up to the velocity of other actor plus delta_velocity\n - trigger_distance: distance between the actors\n - max_distance: driven distance to catch up has to be smaller than max_distance\n\n The behaviour will terminate succesful, when the two actors are in trigger_distance.\n If max_distance is driven by the actor before actors are in trigger_distance,\n then the behaviour ends with a failure.\n \"\"\"\n\n def __init__(self, actor, other_actor, throttle_value=1, delta_velocity=10, trigger_distance=5,\n max_distance=500, name=\"AccelerateToCatchUp\"):\n \"\"\"\n Setup parameters\n The target_speet is calculated on the fly.\n \"\"\"\n super(AccelerateToCatchUp, self).__init__(name, actor)\n\n self._other_actor = other_actor\n self._throttle_value = throttle_value\n self._delta_velocity = delta_velocity # 1m/s=3.6km/h\n self._trigger_distance = trigger_distance\n self._max_distance = max_distance\n\n self._control, self._type = get_actor_control(actor)\n\n self._initial_actor_pos = None\n\n def initialise(self):\n\n # get initial actor position\n self._initial_actor_pos = CarlaDataProvider.get_location(self._actor)\n super(AccelerateToCatchUp, self).initialise()\n\n def update(self):\n\n # get actor speed\n actor_speed = CarlaDataProvider.get_velocity(self._actor)\n target_speed = CarlaDataProvider.get_velocity(self._other_actor) + self._delta_velocity\n\n # distance between actors\n distance = CarlaDataProvider.get_location(self._actor).distance(\n CarlaDataProvider.get_location(self._other_actor))\n\n # driven distance of actor\n driven_distance = CarlaDataProvider.get_location(self._actor).distance(self._initial_actor_pos)\n\n if actor_speed < target_speed:\n # set throttle to throttle_value to accelerate\n self._control.throttle = self._throttle_value\n\n if actor_speed >= target_speed:\n # keep velocity until the actors are in trigger distance\n self._control.throttle = 0\n\n self._actor.apply_control(self._control)\n\n # new status:\n if distance <= self._trigger_distance:\n new_status = py_trees.common.Status.SUCCESS\n\n elif driven_distance > self._max_distance:\n new_status = py_trees.common.Status.FAILURE\n else:\n new_status = py_trees.common.Status.RUNNING\n\n return new_status\n\n\nclass KeepVelocity(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to keep the provided velocity.\n The controlled traffic participant will accelerate as fast as possible\n until reaching a given _target_velocity_, which is then maintained for\n as long as this behavior is active.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - target_velocity: The target velocity the actor should reach\n - duration[optional]: Duration in seconds of this behavior\n - distance[optional]: Maximum distance in meters covered by the actor during this behavior\n\n A termination can be enforced by providing distance or duration values.\n Alternatively, a parallel termination behavior has to be used.\n \"\"\"\n\n def __init__(self, actor, target_velocity, duration=float(\"inf\"), distance=float(\"inf\"), name=\"KeepVelocity\"):\n \"\"\"\n Setup parameters including acceleration value (via throttle_value)\n and target velocity\n \"\"\"\n super(KeepVelocity, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._target_velocity = target_velocity\n\n self._control, self._type = get_actor_control(actor)\n self._map = self._actor.get_world().get_map()\n self._waypoint = self._map.get_waypoint(self._actor.get_location())\n\n self._duration = duration\n self._target_distance = distance\n self._distance = 0\n self._start_time = 0\n self._location = None\n\n def initialise(self):\n self._location = CarlaDataProvider.get_location(self._actor)\n self._start_time = GameTime.get_time()\n\n # In case of walkers, we have to extract the current heading\n if self._type == 'walker':\n self._control.speed = self._target_velocity\n self._control.direction = CarlaDataProvider.get_transform(self._actor).get_forward_vector()\n\n super(KeepVelocity, self).initialise()\n\n def update(self):\n \"\"\"\n As long as the stop condition (duration or distance) is not violated, set a new vehicle control\n\n For vehicles: set throttle to throttle_value, as long as velocity is < target_velocity\n For walkers: simply apply the given self._control\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._type == 'vehicle':\n if CarlaDataProvider.get_velocity(self._actor) < self._target_velocity:\n self._control.throttle = 1.0\n else:\n self._control.throttle = 0.0\n self._actor.apply_control(self._control)\n\n new_location = CarlaDataProvider.get_location(self._actor)\n self._distance += calculate_distance(self._location, new_location)\n self._location = new_location\n\n if self._distance > self._target_distance:\n new_status = py_trees.common.Status.SUCCESS\n\n if GameTime.get_time() - self._start_time > self._duration:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n On termination of this behavior, the throttle should be set back to 0.,\n to avoid further acceleration.\n \"\"\"\n\n if self._type == 'vehicle':\n self._control.throttle = 0.0\n elif self._type == 'walker':\n self._control.speed = 0.0\n if self._actor is not None and self._actor.is_alive:\n self._actor.apply_control(self._control)\n super(KeepVelocity, self).terminate(new_status)\n\n\nclass ChangeAutoPilot(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to disable/enable the use of the autopilot.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - activate: True (=enable autopilot) or False (=disable autopilot)\n - lane_change: Traffic Manager parameter. True (=enable lane changes) or False (=disable lane changes)\n - distance_between_vehicles: Traffic Manager parameter\n - max_speed: Traffic Manager parameter. Max speed of the actor. This will only work for road segments\n with the same speed limit as the first one\n\n The behavior terminates after changing the autopilot state\n \"\"\"\n\n def __init__(self, actor, activate, parameters=None, name=\"ChangeAutoPilot\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(ChangeAutoPilot, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._activate = activate\n self._tm = CarlaDataProvider.get_client().get_trafficmanager(\n CarlaDataProvider.get_traffic_manager_port())\n self._parameters = parameters\n\n def update(self):\n \"\"\"\n De/activate autopilot\n \"\"\"\n self._actor.set_autopilot(self._activate)\n\n if self._parameters is not None:\n if \"auto_lane_change\" in self._parameters:\n lane_change = self._parameters[\"auto_lane_change\"]\n self._tm.auto_lane_change(self._actor, lane_change)\n\n if \"max_speed\" in self._parameters:\n max_speed = self._parameters[\"max_speed\"]\n max_road_speed = self._actor.get_speed_limit()\n if max_road_speed is not None:\n percentage = (max_road_speed - max_speed) / max_road_speed * 100.0\n self._tm.vehicle_percentage_speed_difference(self._actor, percentage)\n else:\n print(\"ChangeAutopilot: Unable to find the vehicle's speed limit\")\n\n if \"distance_between_vehicles\" in self._parameters:\n dist_vehicles = self._parameters[\"distance_between_vehicles\"]\n self._tm.distance_to_leading_vehicle(self._actor, dist_vehicles)\n\n if \"force_lane_change\" in self._parameters:\n force_lane_change = self._parameters[\"force_lane_change\"]\n self._tm.force_lane_change(self._actor, force_lane_change)\n\n if \"ignore_vehicles_percentage\" in self._parameters:\n ignore_vehicles = self._parameters[\"ignore_vehicles_percentage\"]\n self._tm.ignore_vehicles_percentage(self._actor, ignore_vehicles)\n\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n\nclass StopVehicle(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic stopping behavior. The controlled traffic\n participant will decelerate with _bake_value_ until reaching a full stop.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - brake_value: Brake value in [0,1] applied\n\n The behavior terminates when the actor stopped moving\n \"\"\"\n\n def __init__(self, actor, brake_value, name=\"Stopping\"):\n \"\"\"\n Setup _actor and maximum braking value\n \"\"\"\n super(StopVehicle, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._control, self._type = get_actor_control(actor)\n if self._type == 'walker':\n self._control.speed = 0\n self._brake_value = brake_value\n\n def update(self):\n \"\"\"\n Set brake to brake_value until reaching full stop\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._type == 'vehicle':\n if CarlaDataProvider.get_velocity(self._actor) > EPSILON:\n self._control.brake = self._brake_value\n else:\n new_status = py_trees.common.Status.SUCCESS\n self._control.brake = 0\n else:\n new_status = py_trees.common.Status.SUCCESS\n\n self._actor.apply_control(self._control)\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass SyncArrival(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to\n set velocity of actor so that it reaches location at the same time as\n actor_reference. The behavior assumes that the two actors are moving\n towards location in a straight line.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - actor_reference: Reference actor with which arrival is synchronized\n - target_location: CARLA location where the actors should \"meet\"\n - gain[optional]: Coefficient for actor's throttle and break controls\n\n Note: In parallel to this behavior a termination behavior has to be used\n to keep continue synchronization for a certain duration, or for a\n certain distance, etc.\n \"\"\"\n\n def __init__(self, actor, actor_reference, target_location, gain=1, name=\"SyncArrival\"):\n \"\"\"\n Setup required parameters\n \"\"\"\n super(SyncArrival, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._control = carla.VehicleControl()\n self._actor_reference = actor_reference\n self._target_location = target_location\n self._gain = gain\n\n self._control.steering = 0\n\n def update(self):\n \"\"\"\n Dynamic control update for actor velocity\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n distance_reference = calculate_distance(CarlaDataProvider.get_location(self._actor_reference),\n self._target_location)\n distance = calculate_distance(CarlaDataProvider.get_location(self._actor),\n self._target_location)\n\n velocity_reference = CarlaDataProvider.get_velocity(self._actor_reference)\n time_reference = float('inf')\n if velocity_reference > 0:\n time_reference = distance_reference / velocity_reference\n\n velocity_current = CarlaDataProvider.get_velocity(self._actor)\n time_current = float('inf')\n if velocity_current > 0:\n time_current = distance / velocity_current\n\n control_value = (self._gain) * (time_current - time_reference)\n\n if control_value > 0:\n self._control.throttle = min([control_value, 1])\n self._control.brake = 0\n else:\n self._control.throttle = 0\n self._control.brake = min([abs(control_value), 1])\n\n self._actor.apply_control(self._control)\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n On termination of this behavior, the throttle should be set back to 0.,\n to avoid further acceleration.\n \"\"\"\n if self._actor is not None and self._actor.is_alive:\n self._control.throttle = 0.0\n self._control.brake = 0.0\n self._actor.apply_control(self._control)\n super(SyncArrival, self).terminate(new_status)\n\n\nclass AddNoiseToVehicle(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic jitter behavior.\n To add noise to steer as well as throttle of the vehicle.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - steer_value: Applied steering noise in [0,1]\n - throttle_value: Applied throttle noise in [0,1]\n\n The behavior terminates after setting the new actor controls\n \"\"\"\n\n def __init__(self, actor, steer_value, throttle_value, name=\"Jittering\"):\n \"\"\"\n Setup actor , maximum steer value and throttle value\n \"\"\"\n super(AddNoiseToVehicle, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._control = carla.VehicleControl()\n self._steer_value = steer_value\n self._throttle_value = throttle_value\n\n def update(self):\n \"\"\"\n Set steer to steer_value and throttle to throttle_value until reaching full stop\n \"\"\"\n self._control = self._actor.get_control()\n self._control.steer = self._steer_value\n self._control.throttle = self._throttle_value\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n self._actor.apply_control(self._control)\n\n return new_status\n\n\nclass ChangeNoiseParameters(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic jitter behavior.\n To add noise to steer as well as throttle of the vehicle.\n\n This behavior should be used in conjuction with AddNoiseToVehicle\n\n The behavior terminates after one iteration\n \"\"\"\n\n def __init__(self, new_steer_noise, new_throttle_noise,\n noise_mean, noise_std, dynamic_mean_for_steer, dynamic_mean_for_throttle, name=\"ChangeJittering\"):\n \"\"\"\n Setup actor , maximum steer value and throttle value\n \"\"\"\n super(ChangeNoiseParameters, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._new_steer_noise = new_steer_noise\n self._new_throttle_noise = new_throttle_noise\n self._noise_mean = noise_mean\n self._noise_std = noise_std\n self._dynamic_mean_for_steer = dynamic_mean_for_steer\n self._dynamic_mean_for_throttle = dynamic_mean_for_throttle\n\n self._noise_to_apply = abs(random.gauss(self._noise_mean, self._noise_std))\n\n def update(self):\n \"\"\"\n Change the noise parameters from the structure copy that it receives.\n \"\"\"\n\n self._new_steer_noise[0] = min(0, -(self._noise_to_apply - self._dynamic_mean_for_steer))\n self._new_throttle_noise[0] = min(self._noise_to_apply + self._dynamic_mean_for_throttle, 1)\n\n new_status = py_trees.common.Status.SUCCESS\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n\nclass BasicAgentBehavior(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior, which uses the\n basic_agent from CARLA to control the actor until\n reaching a target location.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - target_location: Is the desired target location (carla.location),\n the actor should move to\n\n The behavior terminates after reaching the target_location (within 2 meters)\n \"\"\"\n\n _acceptable_target_distance = 2\n\n def __init__(self, actor, target_location, name=\"BasicAgentBehavior\"):\n \"\"\"\n Setup actor and maximum steer value\n \"\"\"\n super(BasicAgentBehavior, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._agent = BasicAgent(actor) # pylint: disable=undefined-variable\n self._agent.set_destination((target_location.x, target_location.y, target_location.z))\n self._control = carla.VehicleControl()\n self._target_location = target_location\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n\n self._control = self._agent.run_step()\n\n location = CarlaDataProvider.get_location(self._actor)\n if calculate_distance(location, self._target_location) < self._acceptable_target_distance:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n self._actor.apply_control(self._control)\n\n return new_status\n\n def terminate(self, new_status):\n self._control.throttle = 0.0\n self._control.brake = 0.0\n self._actor.apply_control(self._control)\n super(BasicAgentBehavior, self).terminate(new_status)\n\n\nclass Idle(AtomicBehavior):\n\n \"\"\"\n This class contains an idle behavior scenario\n\n Important parameters:\n - duration[optional]: Duration in seconds of this behavior\n\n A termination can be enforced by providing a duration value.\n Alternatively, a parallel termination behavior has to be used.\n \"\"\"\n\n def __init__(self, duration=float(\"inf\"), name=\"Idle\"):\n \"\"\"\n Setup actor\n \"\"\"\n super(Idle, self).__init__(name)\n self._duration = duration\n self._start_time = 0\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def initialise(self):\n \"\"\"\n Set start time\n \"\"\"\n self._start_time = GameTime.get_time()\n super(Idle, self).initialise()\n\n def update(self):\n \"\"\"\n Keep running until termination condition is satisfied\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if GameTime.get_time() - self._start_time > self._duration:\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass WaypointFollower(AtomicBehavior):\n\n \"\"\"\n This is an atomic behavior to follow waypoints while maintaining a given speed.\n If no plan is provided, the actor will follow its foward waypoints indefinetely.\n Otherwise, the behavior will end with SUCCESS upon reaching the end of the plan.\n If no target velocity is provided, the actor continues with its current velocity.\n\n Args:\n actor (carla.Actor): CARLA actor to execute the behavior.\n target_speed (float, optional): Desired speed of the actor in m/s. Defaults to None.\n plan ([carla.Location] or [(carla.Waypoint, carla.agent.navigation.local_planner)], optional):\n Waypoint plan the actor should follow. Defaults to None.\n blackboard_queue_name (str, optional):\n Blackboard variable name, if additional actors should be created on-the-fly. Defaults to None.\n avoid_collision (bool, optional):\n Enable/Disable(=default) collision avoidance for vehicles/bikes. Defaults to False.\n name (str, optional): Name of the behavior. Defaults to \"FollowWaypoints\".\n\n Attributes:\n actor (carla.Actor): CARLA actor to execute the behavior.\n name (str, optional): Name of the behavior.\n _target_speed (float, optional): Desired speed of the actor in m/s. Defaults to None.\n _plan ([carla.Location] or [(carla.Waypoint, carla.agent.navigation.local_planner)]):\n Waypoint plan the actor should follow. Defaults to None.\n _blackboard_queue_name (str):\n Blackboard variable name, if additional actors should be created on-the-fly. Defaults to None.\n _avoid_collision (bool): Enable/Disable(=default) collision avoidance for vehicles/bikes. Defaults to False.\n _actor_dict: Dictonary of all actors, and their corresponding plans (e.g. {actor: plan}).\n _local_planner_dict: Dictonary of all actors, and their corresponding local planners.\n Either \"Walker\" for pedestrians, or a carla.agent.navigation.LocalPlanner for other actors.\n _args_lateral_dict: Parameters for the PID of the used carla.agent.navigation.LocalPlanner.\n _unique_id: Unique ID of the behavior based on timestamp in nanoseconds.\n\n Note:\n OpenScenario:\n The WaypointFollower atomic must be called with an individual name if multiple consecutive WFs.\n Blackboard variables with lists are used for consecutive WaypointFollower behaviors.\n Termination of active WaypointFollowers in initialise of AtomicBehavior because any\n following behavior must terminate the WaypointFollower.\n \"\"\"\n\n def __init__(self, actor, target_speed=None, plan=None, blackboard_queue_name=None,\n avoid_collision=False, name=\"FollowWaypoints\"):\n \"\"\"\n Set up actor and local planner\n \"\"\"\n super(WaypointFollower, self).__init__(name, actor)\n self._actor_dict = {}\n self._actor_dict[actor] = None\n self._target_speed = target_speed\n self._local_planner_dict = {}\n self._local_planner_dict[actor] = None\n self._plan = plan\n self._blackboard_queue_name = blackboard_queue_name\n if blackboard_queue_name is not None:\n self._queue = Blackboard().get(blackboard_queue_name)\n self._args_lateral_dict = {'K_P': 1.0, 'K_D': 0.01, 'K_I': 0.0, 'dt': 0.05}\n self._avoid_collision = avoid_collision\n self._unique_id = 0\n\n def initialise(self):\n \"\"\"\n Delayed one-time initialization\n\n Checks if another WaypointFollower behavior is already running for this actor.\n If this is the case, a termination signal is sent to the running behavior.\n \"\"\"\n super(WaypointFollower, self).initialise()\n self._unique_id = int(round(time.time() * 1e9))\n try:\n # check whether WF for this actor is already running and add new WF to running_WF list\n check_attr = operator.attrgetter(\"running_WF_actor_{}\".format(self._actor.id))\n running = check_attr(py_trees.blackboard.Blackboard())\n active_wf = copy.copy(running)\n active_wf.append(self._unique_id)\n py_trees.blackboard.Blackboard().set(\n \"running_WF_actor_{}\".format(self._actor.id), active_wf, overwrite=True)\n except AttributeError:\n # no WF is active for this actor\n py_trees.blackboard.Blackboard().set(\"terminate_WF_actor_{}\".format(self._actor.id), [], overwrite=True)\n py_trees.blackboard.Blackboard().set(\n \"running_WF_actor_{}\".format(self._actor.id), [self._unique_id], overwrite=True)\n\n for actor in self._actor_dict:\n self._apply_local_planner(actor)\n return True\n\n def _apply_local_planner(self, actor):\n \"\"\"\n Convert the plan into locations for walkers (pedestrians), or to a waypoint list for other actors.\n For non-walkers, activate the carla.agent.navigation.LocalPlanner module.\n \"\"\"\n if self._target_speed is None:\n self._target_speed = CarlaDataProvider.get_velocity(actor)\n else:\n self._target_speed = self._target_speed\n\n if isinstance(actor, carla.Walker):\n self._local_planner_dict[actor] = \"Walker\"\n if self._plan is not None:\n if isinstance(self._plan[0], carla.Location):\n self._actor_dict[actor] = self._plan\n else:\n self._actor_dict[actor] = [element[0].transform.location for element in self._plan]\n else:\n local_planner = LocalPlanner( # pylint: disable=undefined-variable\n actor, opt_dict={\n 'target_speed': self._target_speed * 3.6,\n 'lateral_control_dict': self._args_lateral_dict})\n\n if self._plan is not None:\n if isinstance(self._plan[0], carla.Location):\n plan = []\n for location in self._plan:\n waypoint = CarlaDataProvider.get_map().get_waypoint(location,\n project_to_road=True,\n lane_type=carla.LaneType.Any)\n plan.append((waypoint, RoadOption.LANEFOLLOW))\n local_planner.set_global_plan(plan)\n else:\n local_planner.set_global_plan(self._plan)\n\n self._local_planner_dict[actor] = local_planner\n self._actor_dict[actor] = self._plan\n\n def update(self):\n \"\"\"\n Compute next control step for the given waypoint plan, obtain and apply control to actor\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n check_term = operator.attrgetter(\"terminate_WF_actor_{}\".format(self._actor.id))\n terminate_wf = check_term(py_trees.blackboard.Blackboard())\n\n check_run = operator.attrgetter(\"running_WF_actor_{}\".format(self._actor.id))\n active_wf = check_run(py_trees.blackboard.Blackboard())\n\n # Termination of WF if the WFs unique_id is listed in terminate_wf\n # only one WF should be active, therefore all previous WF have to be terminated\n if self._unique_id in terminate_wf:\n terminate_wf.remove(self._unique_id)\n if self._unique_id in active_wf:\n active_wf.remove(self._unique_id)\n\n py_trees.blackboard.Blackboard().set(\n \"terminate_WF_actor_{}\".format(self._actor.id), terminate_wf, overwrite=True)\n py_trees.blackboard.Blackboard().set(\n \"running_WF_actor_{}\".format(self._actor.id), active_wf, overwrite=True)\n new_status = py_trees.common.Status.SUCCESS\n return new_status\n\n if self._blackboard_queue_name is not None:\n while not self._queue.empty():\n actor = self._queue.get()\n if actor is not None and actor not in self._actor_dict:\n self._apply_local_planner(actor)\n\n success = True\n for actor in self._local_planner_dict:\n local_planner = self._local_planner_dict[actor] if actor else None\n if actor is not None and actor.is_alive and local_planner is not None:\n # Check if the actor is a vehicle/bike\n if not isinstance(actor, carla.Walker):\n control = local_planner.run_step(debug=False)\n if self._avoid_collision and detect_lane_obstacle(actor):\n control.throttle = 0.0\n control.brake = 1.0\n actor.apply_control(control)\n # Check if the actor reached the end of the plan\n # replace access to private _waypoints_queue with public getter\n if local_planner._waypoints_queue: # pylint: disable=protected-access\n success = False\n # If the actor is a pedestrian, we have to use the WalkerAIController\n # The walker is sent to the next waypoint in its plan\n else:\n actor_location = CarlaDataProvider.get_location(actor)\n success = False\n if self._actor_dict[actor]:\n location = self._actor_dict[actor][0]\n direction = location - actor_location\n direction_norm = math.sqrt(direction.x**2 + direction.y**2)\n control = actor.get_control()\n control.speed = self._target_speed\n control.direction = direction / direction_norm\n actor.apply_control(control)\n if direction_norm < 1.0:\n self._actor_dict[actor] = self._actor_dict[actor][1:]\n if self._actor_dict[actor] is None:\n success = True\n else:\n control = actor.get_control()\n control.speed = self._target_speed\n control.direction = CarlaDataProvider.get_transform(actor).rotation.get_forward_vector()\n actor.apply_control(control)\n\n if success:\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n On termination of this behavior,\n the controls should be set back to 0.\n \"\"\"\n for actor in self._local_planner_dict:\n if actor is not None and actor.is_alive:\n control, _ = get_actor_control(actor)\n actor.apply_control(control)\n local_planner = self._local_planner_dict[actor]\n if local_planner is not None and local_planner != \"Walker\":\n local_planner.reset_vehicle()\n local_planner = None\n\n self._local_planner_dict = {}\n self._actor_dict = {}\n super(WaypointFollower, self).terminate(new_status)\n\n\nclass LaneChange(WaypointFollower):\n\n \"\"\"\n This class inherits from the class WaypointFollower.\n\n This class contains an atomic lane change behavior to a parallel lane.\n The vehicle follows a waypoint plan to the other lane, which is calculated in the initialise method.\n This waypoint plan is calculated with a scenario helper function.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - speed: speed of the actor for the lane change, in m/s\n - direction: 'right' or 'left', depending on which lane to change\n - distance_same_lane: straight distance before lane change, in m\n - distance_other_lane: straight distance after lane change, in m\n - distance_lane_change: straight distance for the lane change itself, in m\n\n The total distance driven is greater than the sum of distance_same_lane and distance_other_lane.\n It results from the lane change distance plus the distance_same_lane plus distance_other_lane.\n The lane change distance is set to 25m (straight), the driven distance is slightly greater.\n\n A parallel termination behavior has to be used.\n \"\"\"\n\n def __init__(self, actor, speed=10, direction='left',\n distance_same_lane=5, distance_other_lane=100, distance_lane_change=25, name='LaneChange'):\n\n self._direction = direction\n self._distance_same_lane = distance_same_lane\n self._distance_other_lane = distance_other_lane\n self._distance_lane_change = distance_lane_change\n\n self._target_lane_id = None\n self._distance_new_lane = 0\n self._pos_before_lane_change = None\n\n super(LaneChange, self).__init__(actor, target_speed=speed, name=name)\n\n def initialise(self):\n\n # get start position\n position_actor = CarlaDataProvider.get_map().get_waypoint(self._actor.get_location())\n\n # calculate plan with scenario_helper function\n self._plan, self._target_lane_id = generate_target_waypoint_list_multilane(\n position_actor, self._direction, self._distance_same_lane,\n self._distance_other_lane, self._distance_lane_change, check='true')\n super(LaneChange, self).initialise()\n\n def update(self):\n status = super(LaneChange, self).update()\n\n current_position_actor = CarlaDataProvider.get_map().get_waypoint(self._actor.get_location())\n current_lane_id = current_position_actor.lane_id\n\n if current_lane_id == self._target_lane_id:\n # driving on new lane\n distance = current_position_actor.transform.location.distance(self._pos_before_lane_change)\n\n if distance > self._distance_other_lane:\n # long enough distance on new lane --> SUCCESS\n status = py_trees.common.Status.SUCCESS\n else:\n # no lane change yet\n self._pos_before_lane_change = current_position_actor.transform.location\n\n return status\n\n\nclass SetInitSpeed(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to set the init_speed of an actor,\n succeding immeditely after initializing\n \"\"\"\n\n def __init__(self, actor, init_speed=10, name='SetInitSpeed'):\n\n self._init_speed = init_speed\n self._terminate = None\n self._actor = actor\n\n super(SetInitSpeed, self).__init__(name, actor)\n\n def initialise(self):\n \"\"\"\n Initialize it's speed\n \"\"\"\n\n transform = self._actor.get_transform()\n yaw = transform.rotation.yaw * (math.pi / 180)\n\n vx = math.cos(yaw) * self._init_speed\n vy = math.sin(yaw) * self._init_speed\n self._actor.set_target_velocity(carla.Vector3D(vx, vy, 0))\n\n def update(self):\n \"\"\"\n Nothing to update, end the behavior\n \"\"\"\n\n return py_trees.common.Status.SUCCESS\n\n\nclass HandBrakeVehicle(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic hand brake behavior.\n To set the hand brake value of the vehicle.\n\n Important parameters:\n - vehicle: CARLA actor to execute the behavior\n - hand_brake_value to be applied in [0,1]\n\n The behavior terminates after setting the hand brake value\n \"\"\"\n\n def __init__(self, vehicle, hand_brake_value, name=\"Braking\"):\n \"\"\"\n Setup vehicle control and brake value\n \"\"\"\n super(HandBrakeVehicle, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._vehicle = vehicle\n self._control, self._type = get_actor_control(vehicle)\n self._hand_brake_value = hand_brake_value\n\n def update(self):\n \"\"\"\n Set handbrake\n \"\"\"\n new_status = py_trees.common.Status.SUCCESS\n if self._type == 'vehicle':\n self._control.hand_brake = self._hand_brake_value\n self._vehicle.apply_control(self._control)\n else:\n self._hand_brake_value = None\n self.logger.debug(\"%s.update()[%s->%s]\" %\n (self.__class__.__name__, self.status, new_status))\n self._vehicle.apply_control(self._control)\n\n return new_status\n\n\nclass ActorDestroy(AtomicBehavior):\n\n \"\"\"\n This class contains an actor destroy behavior.\n Given an actor this behavior will delete it.\n\n Important parameters:\n - actor: CARLA actor to be deleted\n\n The behavior terminates after removing the actor\n \"\"\"\n\n def __init__(self, actor, name=\"ActorDestroy\"):\n \"\"\"\n Setup actor\n \"\"\"\n super(ActorDestroy, self).__init__(name, actor)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n if self._actor:\n CarlaDataProvider.remove_actor_by_id(self._actor.id)\n self._actor = None\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass ActorTransformSetter(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to set the transform\n of an actor.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - transform: New target transform (position + orientation) of the actor\n - physics [optional]: If physics is true, the actor physics will be reactivated upon success\n\n The behavior terminates when actor is set to the new actor transform (closer than 1 meter)\n\n NOTE:\n It is very important to ensure that the actor location is spawned to the new transform because of the\n appearence of a rare runtime processing error. WaypointFollower with LocalPlanner,\n might fail if new_status is set to success before the actor is really positioned at the new transform.\n Therefore: calculate_distance(actor, transform) < 1 meter\n \"\"\"\n\n def __init__(self, actor, transform, physics=True, name=\"ActorTransformSetter\"):\n \"\"\"\n Init\n \"\"\"\n super(ActorTransformSetter, self).__init__(name, actor)\n self._transform = transform\n self._physics = physics\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def initialise(self):\n if self._actor.is_alive:\n self._actor.set_target_velocity(carla.Vector3D(0, 0, 0))\n self._actor.set_target_angular_velocity(carla.Vector3D(0, 0, 0))\n self._actor.set_transform(self._transform)\n super(ActorTransformSetter, self).initialise()\n\n def update(self):\n \"\"\"\n Transform actor\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if not self._actor.is_alive:\n new_status = py_trees.common.Status.FAILURE\n\n if calculate_distance(self._actor.get_location(), self._transform.location) < 1.0:\n if self._physics:\n self._actor.set_simulate_physics(enabled=True)\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass TrafficLightStateSetter(AtomicBehavior):\n\n \"\"\"\n This class contains an atomic behavior to set the state of a given traffic light\n\n Args:\n actor (carla.TrafficLight): ID of the traffic light that shall be changed\n state (carla.TrafficLightState): New target state\n\n The behavior terminates after trying to set the new state\n \"\"\"\n\n def __init__(self, actor, state, name=\"TrafficLightStateSetter\"):\n \"\"\"\n Init\n \"\"\"\n super(TrafficLightStateSetter, self).__init__(name)\n\n self._actor = actor if \"traffic_light\" in actor.type_id else None\n self._state = state\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n \"\"\"\n Change the state of the traffic light\n \"\"\"\n if self._actor is None:\n return py_trees.common.Status.FAILURE\n\n new_status = py_trees.common.Status.RUNNING\n if self._actor.is_alive:\n self._actor.set_state(self._state)\n new_status = py_trees.common.Status.SUCCESS\n else:\n # For some reason the actor is gone...\n new_status = py_trees.common.Status.FAILURE\n\n return new_status\n\n\nclass ActorSource(AtomicBehavior):\n\n \"\"\"\n Implementation for a behavior that will indefinitely create actors\n at a given transform if no other actor exists in a given radius\n from the transform.\n\n Important parameters:\n - actor_type_list: Type of CARLA actors to be spawned\n - transform: Spawn location\n - threshold: Min available free distance between other actors and the spawn location\n - blackboard_queue_name: Name of the blackboard used to control this behavior\n - actor_limit [optional]: Maximum number of actors to be spawned (default=7)\n\n A parallel termination behavior has to be used.\n \"\"\"\n\n def __init__(self, actor_type_list, transform, threshold, blackboard_queue_name,\n actor_limit=7, name=\"ActorSource\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(ActorSource, self).__init__(name)\n self._world = CarlaDataProvider.get_world()\n self._actor_types = actor_type_list\n self._spawn_point = transform\n self._threshold = threshold\n self._queue = Blackboard().get(blackboard_queue_name)\n self._actor_limit = actor_limit\n self._last_blocking_actor = None\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n if self._actor_limit > 0:\n world_actors = self._world.get_actors()\n spawn_point_blocked = False\n if (self._last_blocking_actor and\n self._spawn_point.location.distance(self._last_blocking_actor.get_location()) < self._threshold):\n spawn_point_blocked = True\n\n if not spawn_point_blocked:\n for actor in world_actors:\n if self._spawn_point.location.distance(actor.get_location()) < self._threshold:\n spawn_point_blocked = True\n self._last_blocking_actor = actor\n break\n\n if not spawn_point_blocked:\n try:\n new_actor = CarlaDataProvider.request_new_actor(\n np.random.choice(self._actor_types), self._spawn_point)\n self._actor_limit -= 1\n self._queue.put(new_actor)\n except: # pylint: disable=bare-except\n print(\"ActorSource unable to spawn actor\")\n return new_status\n\n\nclass ActorSink(AtomicBehavior):\n\n \"\"\"\n Implementation for a behavior that will indefinitely destroy actors\n that wander near a given location within a specified threshold.\n\n Important parameters:\n - actor_type_list: Type of CARLA actors to be spawned\n - sink_location: Location (carla.location) at which actors will be deleted\n - threshold: Distance around sink_location in which actors will be deleted\n\n A parallel termination behavior has to be used.\n \"\"\"\n\n def __init__(self, sink_location, threshold, name=\"ActorSink\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(ActorSink, self).__init__(name)\n self._sink_location = sink_location\n self._threshold = threshold\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n CarlaDataProvider.remove_actors_in_surrounding(self._sink_location, self._threshold)\n return new_status\n\n\nclass StartRecorder(AtomicBehavior):\n\n \"\"\"\n Atomic that starts the CARLA recorder. Only one can be active\n at a time, and if this isn't the case, the recorder will\n automatically stop the previous one.\n\n Args:\n recorder_name (str): name of the file to write the recorded data.\n Remember that a simple name will save the recording in\n 'CarlaUE4/Saved/'. Otherwise, if some folder appears in the name,\n it will be considered an absolute path.\n name (str): name of the behavior\n \"\"\"\n\n def __init__(self, recorder_name, name=\"StartRecorder\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(StartRecorder, self).__init__(name)\n self._client = CarlaDataProvider.get_client()\n self._recorder_name = recorder_name\n\n def update(self):\n self._client.start_recorder(self._recorder_name)\n return py_trees.common.Status.SUCCESS\n\n\nclass StopRecorder(AtomicBehavior):\n\n \"\"\"\n Atomic that stops the CARLA recorder.\n\n Args:\n name (str): name of the behavior\n \"\"\"\n\n def __init__(self, name=\"StopRecorder\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(StopRecorder, self).__init__(name)\n self._client = CarlaDataProvider.get_client()\n\n def update(self):\n self._client.stop_recorder()\n return py_trees.common.Status.SUCCESS\n\n\nclass TrafficLightManipulator(AtomicBehavior):\n\n \"\"\"\n Atomic behavior that manipulates traffic lights around the ego_vehicle to trigger scenarios 7 to 10.\n This is done by setting 2 of the traffic light at the intersection to green (with some complex precomputation\n to set everything up).\n\n Important parameters:\n - ego_vehicle: CARLA actor that controls this behavior\n - subtype: string that gathers information of the route and scenario number\n (check SUBTYPE_CONFIG_TRANSLATION below)\n \"\"\"\n\n RED = carla.TrafficLightState.Red\n YELLOW = carla.TrafficLightState.Yellow\n GREEN = carla.TrafficLightState.Green\n\n # Time constants\n RED_TIME = 1.5 # Minimum time the ego vehicle waits in red (seconds)\n YELLOW_TIME = 2 # Time spent at yellow state (seconds)\n RESET_TIME = 6 # Time waited before resetting all the junction (seconds)\n\n # Experimental values\n TRIGGER_DISTANCE = 10 # Distance that makes all vehicles in the lane enter the junction (meters)\n DIST_TO_WAITING_TIME = 0.04 # Used to wait longer at larger intersections (s/m)\n\n INT_CONF_OPP1 = {'ego': RED, 'ref': RED, 'left': RED, 'right': RED, 'opposite': GREEN}\n INT_CONF_OPP2 = {'ego': GREEN, 'ref': GREEN, 'left': RED, 'right': RED, 'opposite': GREEN}\n INT_CONF_LFT1 = {'ego': RED, 'ref': RED, 'left': GREEN, 'right': RED, 'opposite': RED}\n INT_CONF_LFT2 = {'ego': GREEN, 'ref': GREEN, 'left': GREEN, 'right': RED, 'opposite': RED}\n INT_CONF_RGT1 = {'ego': RED, 'ref': RED, 'left': RED, 'right': GREEN, 'opposite': RED}\n INT_CONF_RGT2 = {'ego': GREEN, 'ref': GREEN, 'left': RED, 'right': GREEN, 'opposite': RED}\n\n INT_CONF_REF1 = {'ego': GREEN, 'ref': GREEN, 'left': RED, 'right': RED, 'opposite': RED}\n INT_CONF_REF2 = {'ego': YELLOW, 'ref': YELLOW, 'left': RED, 'right': RED, 'opposite': RED}\n\n # Depending on the scenario, IN ORDER OF IMPORTANCE, the traffic light changed\n # The list has to contain only items of the INT_CONF\n SUBTYPE_CONFIG_TRANSLATION = {\n 'S7left': ['left', 'opposite', 'right'],\n 'S7right': ['left', 'opposite'],\n 'S7opposite': ['right', 'left', 'opposite'],\n 'S8left': ['opposite'],\n 'S9right': ['left', 'opposite']\n }\n\n CONFIG_TLM_TRANSLATION = {\n 'left': [INT_CONF_LFT1, INT_CONF_LFT2],\n 'right': [INT_CONF_RGT1, INT_CONF_RGT2],\n 'opposite': [INT_CONF_OPP1, INT_CONF_OPP2]\n }\n\n def __init__(self, ego_vehicle, subtype, debug=False, name=\"TrafficLightManipulator\"):\n super(TrafficLightManipulator, self).__init__(name)\n self.ego_vehicle = ego_vehicle\n self.subtype = subtype\n self.current_step = 1\n self.debug = debug\n\n self.traffic_light = None\n self.annotations = None\n self.configuration = None\n self.prev_junction_state = None\n self.junction_location = None\n self.seconds_waited = 0\n self.prev_time = None\n self.max_trigger_distance = None\n self.waiting_time = None\n self.inside_junction = False\n\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n\n new_status = py_trees.common.Status.RUNNING\n\n # 1) Set up the parameters\n if self.current_step == 1:\n\n # Traffic light affecting the ego vehicle\n self.traffic_light = CarlaDataProvider.get_next_traffic_light(self.ego_vehicle, use_cached_location=False)\n if not self.traffic_light:\n # nothing else to do in this iteration...\n return new_status\n\n # \"Topology\" of the intersection\n self.annotations = CarlaDataProvider.annotate_trafficlight_in_group(self.traffic_light)\n\n # Which traffic light will be modified (apart from the ego lane)\n self.configuration = self.get_traffic_light_configuration(self.subtype, self.annotations)\n if self.configuration is None:\n self.current_step = 0 # End the behavior\n return new_status\n\n # Modify the intersection. Store the previous state\n self.prev_junction_state = self.set_intersection_state(self.INT_CONF_REF1)\n\n self.current_step += 1\n if self.debug:\n print(\"--- All set up\")\n\n # 2) Modify the ego lane to yellow when closeby\n elif self.current_step == 2:\n\n ego_location = CarlaDataProvider.get_location(self.ego_vehicle)\n\n if self.junction_location is None:\n ego_waypoint = CarlaDataProvider.get_map().get_waypoint(ego_location)\n junction_waypoint = ego_waypoint.next(0.5)[0]\n while not junction_waypoint.is_junction:\n next_wp = junction_waypoint.next(0.5)\n if len(next_wp) > 0:\n next_wp = next_wp[0]\n junction_waypoint = next_wp\n else:\n break\n self.junction_location = junction_waypoint.transform.location\n\n distance = ego_location.distance(self.junction_location)\n\n # Failure check\n if self.max_trigger_distance is None:\n self.max_trigger_distance = distance + 1\n if distance > self.max_trigger_distance:\n self.current_step = 0\n\n elif distance < self.TRIGGER_DISTANCE:\n _ = self.set_intersection_state(self.INT_CONF_REF2)\n self.current_step += 1\n\n if self.debug:\n print(\"--- Distance until traffic light changes: {}\".format(distance))\n\n # 3) Modify the ego lane to red and the chosen one to green after several seconds\n elif self.current_step == 3:\n\n if self.passed_enough_time(self.YELLOW_TIME):\n _ = self.set_intersection_state(self.CONFIG_TLM_TRANSLATION[self.configuration][0])\n\n self.current_step += 1\n\n # 4) Wait a bit to let vehicles enter the intersection, then set the ego lane to green\n elif self.current_step == 4:\n\n # Get the time in red, dependent on the intersection dimensions\n if self.waiting_time is None:\n self.waiting_time = self.get_waiting_time(self.annotations, self.configuration)\n\n if self.passed_enough_time(self.waiting_time):\n _ = self.set_intersection_state(self.CONFIG_TLM_TRANSLATION[self.configuration][1])\n\n self.current_step += 1\n\n # 5) Wait for the end of the intersection\n elif self.current_step == 5:\n # the traffic light has been manipulated, wait until the vehicle finsihes the intersection\n ego_location = CarlaDataProvider.get_location(self.ego_vehicle)\n ego_waypoint = CarlaDataProvider.get_map().get_waypoint(ego_location)\n\n if not self.inside_junction:\n if ego_waypoint.is_junction:\n # Wait for the ego_vehicle to enter a junction\n self.inside_junction = True\n else:\n if self.debug:\n print(\"--- Waiting to ENTER a junction\")\n\n else:\n if ego_waypoint.is_junction:\n if self.debug:\n print(\"--- Waiting to EXIT a junction\")\n else:\n # And to leave it\n self.inside_junction = False\n self.current_step += 1\n\n # 6) At the end (or if something failed), reset to the previous state\n else:\n if self.prev_junction_state:\n CarlaDataProvider.reset_lights(self.prev_junction_state)\n if self.debug:\n print(\"--- Returning the intersection to its previous state\")\n\n self.variable_cleanup()\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n def passed_enough_time(self, time_limit):\n \"\"\"\n Returns true or false depending on the time that has passed from the\n first time this function was called\n \"\"\"\n # Start the timer\n if self.prev_time is None:\n self.prev_time = GameTime.get_time()\n\n timestamp = GameTime.get_time()\n self.seconds_waited += (timestamp - self.prev_time)\n self.prev_time = timestamp\n\n if self.debug:\n print(\"--- Waited seconds: {}\".format(self.seconds_waited))\n\n if self.seconds_waited >= time_limit:\n self.seconds_waited = 0\n self.prev_time = None\n\n return True\n return False\n\n def set_intersection_state(self, choice):\n \"\"\"\n Changes the intersection to the desired state\n \"\"\"\n prev_state = CarlaDataProvider.update_light_states(\n self.traffic_light,\n self.annotations,\n choice,\n freeze=True)\n\n return prev_state\n\n def get_waiting_time(self, annotation, direction):\n \"\"\"\n Calculates the time the ego traffic light will remain red\n to let vehicles enter the junction\n \"\"\"\n\n tl = annotation[direction][0]\n ego_tl = annotation[\"ref\"][0]\n\n tl_location = CarlaDataProvider.get_trafficlight_trigger_location(tl)\n ego_tl_location = CarlaDataProvider.get_trafficlight_trigger_location(ego_tl)\n\n distance = ego_tl_location.distance(tl_location)\n\n return self.RED_TIME + distance * self.DIST_TO_WAITING_TIME\n\n def get_traffic_light_configuration(self, subtype, annotations):\n \"\"\"\n Checks the list of possible altered traffic lights and gets\n the first one that exists in the intersection\n\n Important parameters:\n - subtype: Subtype of the scenario\n - annotations: list of the traffic light of the junction, with their direction (right, left...)\n \"\"\"\n configuration = None\n\n if subtype in self.SUBTYPE_CONFIG_TRANSLATION:\n possible_configurations = self.SUBTYPE_CONFIG_TRANSLATION[self.subtype]\n while possible_configurations:\n # Chose the first one and delete it\n configuration = possible_configurations[0]\n possible_configurations = possible_configurations[1:]\n if configuration in annotations:\n if annotations[configuration]:\n # Found a valid configuration\n break\n else:\n # The traffic light doesn't exist, get another one\n configuration = None\n else:\n if self.debug:\n print(\"This configuration name is wrong\")\n configuration = None\n\n if configuration is None and self.debug:\n print(\"This subtype has no traffic light available\")\n else:\n if self.debug:\n print(\"This subtype is unknown\")\n\n return configuration\n\n def variable_cleanup(self):\n \"\"\"\n Resets all variables to the intial state\n \"\"\"\n self.current_step = 1\n self.traffic_light = None\n self.annotations = None\n self.configuration = None\n self.prev_junction_state = None\n self.junction_location = None\n self.max_trigger_distance = None\n self.waiting_time = None\n self.inside_junction = False\n\n\nclass ScenarioTriggerer(AtomicBehavior):\n\n \"\"\"\n Handles the triggering of the scenarios that are part of a route.\n\n Initializes a list of blackboard variables to False, and only sets them to True when\n the ego vehicle is very close to the scenarios\n \"\"\"\n\n WINDOWS_SIZE = 5\n\n def __init__(self, actor, route, blackboard_list, distance,\n repeat_scenarios=False, debug=False, name=\"ScenarioTriggerer\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(ScenarioTriggerer, self).__init__(name)\n self._world = CarlaDataProvider.get_world()\n self._map = CarlaDataProvider.get_map()\n self._repeat = repeat_scenarios\n self._debug = debug\n\n self._actor = actor\n self._route = route\n self._distance = distance\n self._blackboard_list = blackboard_list\n self._triggered_scenarios = [] # List of already done scenarios\n\n self._current_index = 0\n self._route_length = len(self._route)\n self._waypoints, _ = zip(*self._route)\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n lower_bound = self._current_index\n upper_bound = min(self._current_index + self.WINDOWS_SIZE + 1, self._route_length)\n\n shortest_distance = float('inf')\n closest_index = -1\n\n for index in range(lower_bound, upper_bound):\n ref_waypoint = self._waypoints[index]\n ref_location = ref_waypoint.location\n\n dist_to_route = ref_location.distance(location)\n if dist_to_route <= shortest_distance:\n closest_index = index\n shortest_distance = dist_to_route\n\n if closest_index == -1 or shortest_distance == float('inf'):\n return new_status\n\n # Update the ego position at the route\n self._current_index = closest_index\n\n route_location = self._waypoints[closest_index].location\n\n # Check which scenarios can be triggered\n blackboard = py_trees.blackboard.Blackboard()\n for black_var_name, scen_location in self._blackboard_list:\n\n # Close enough\n scen_distance = route_location.distance(scen_location)\n condition1 = bool(scen_distance < self._distance)\n\n # Not being currently done\n value = blackboard.get(black_var_name)\n condition2 = bool(not value)\n\n # Already done, if needed\n condition3 = bool(self._repeat or black_var_name not in self._triggered_scenarios)\n\n if condition1 and condition2 and condition3:\n _ = blackboard.set(black_var_name, True)\n self._triggered_scenarios.append(black_var_name)\n\n if self._debug:\n self._world.debug.draw_point(\n scen_location + carla.Location(z=4),\n size=0.5,\n life_time=0.5,\n color=carla.Color(255, 255, 0)\n )\n self._world.debug.draw_string(\n scen_location + carla.Location(z=5),\n str(black_var_name),\n False,\n color=carla.Color(0, 0, 0),\n life_time=1000\n )\n\n return new_status\n" }, { "path": "scenario_runner/srunner/scenariomanager/scenarioatomics/atomic_criteria.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides all atomic evaluation criteria required to analyze if a\nscenario was completed successfully or failed.\n\nCriteria should run continuously to monitor the state of a single actor, multiple\nactors or environmental parameters. Hence, a termination is not required.\n\nThe atomic criteria are implemented with py_trees.\n\"\"\"\n\nimport weakref\nimport math\nimport numpy as np\nimport py_trees\nimport shapely\n\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenariomanager.traffic_events import TrafficEvent, TrafficEventType\n\n\nclass Criterion(py_trees.behaviour.Behaviour):\n\n \"\"\"\n Base class for all criteria used to evaluate a scenario for success/failure\n\n Important parameters (PUBLIC):\n - name: Name of the criterion\n - expected_value_success: Result in case of success\n (e.g. max_speed, zero collisions, ...)\n - expected_value_acceptable: Result that does not mean a failure,\n but is not good enough for a success\n - actual_value: Actual result after running the scenario\n - test_status: Used to access the result of the criterion\n - optional: Indicates if a criterion is optional (not used for overall analysis)\n \"\"\"\n\n def __init__(self,\n name,\n actor,\n expected_value_success,\n expected_value_acceptable=None,\n optional=False,\n terminate_on_failure=False):\n super(Criterion, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._terminate_on_failure = terminate_on_failure\n\n self.name = name\n self.actor = actor\n self.test_status = \"INIT\"\n self.expected_value_success = expected_value_success\n self.expected_value_acceptable = expected_value_acceptable\n self.actual_value = 0\n self.optional = optional\n self.list_traffic_events = []\n\n def initialise(self):\n \"\"\"\n Initialise the criterion. Can be extended by the user-derived class\n \"\"\"\n self.logger.debug(\"%s.initialise()\" % (self.__class__.__name__))\n\n def terminate(self, new_status):\n \"\"\"\n Terminate the criterion. Can be extended by the user-derived class\n \"\"\"\n if (self.test_status == \"RUNNING\") or (self.test_status == \"INIT\"):\n self.test_status = \"SUCCESS\"\n\n self.logger.debug(\"%s.terminate()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n\nclass MaxVelocityTest(Criterion):\n\n \"\"\"\n This class contains an atomic test for maximum velocity.\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - max_velocity_allowed: maximum allowed velocity in m/s\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n def __init__(self, actor, max_velocity_allowed, optional=False, name=\"CheckMaximumVelocity\"):\n \"\"\"\n Setup actor and maximum allowed velovity\n \"\"\"\n super(MaxVelocityTest, self).__init__(name, actor, max_velocity_allowed, None, optional)\n\n def update(self):\n \"\"\"\n Check velocity\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self.actor is None:\n return new_status\n\n velocity = CarlaDataProvider.get_velocity(self.actor)\n\n self.actual_value = max(velocity, self.actual_value)\n\n if velocity > self.expected_value_success:\n self.test_status = \"FAILURE\"\n else:\n self.test_status = \"SUCCESS\"\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass DrivenDistanceTest(Criterion):\n\n \"\"\"\n This class contains an atomic test to check the driven distance\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - distance_success: If the actor's driven distance is more than this value (in meters),\n the test result is SUCCESS\n - distance_acceptable: If the actor's driven distance is more than this value (in meters),\n the test result is ACCEPTABLE\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n def __init__(self,\n actor,\n distance_success,\n distance_acceptable=None,\n optional=False,\n name=\"CheckDrivenDistance\"):\n \"\"\"\n Setup actor\n \"\"\"\n super(DrivenDistanceTest, self).__init__(name, actor, distance_success, distance_acceptable, optional)\n self._last_location = None\n\n def initialise(self):\n self._last_location = CarlaDataProvider.get_location(self.actor)\n super(DrivenDistanceTest, self).initialise()\n\n def update(self):\n \"\"\"\n Check distance\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self.actor is None:\n return new_status\n\n location = CarlaDataProvider.get_location(self.actor)\n\n if location is None:\n return new_status\n\n if self._last_location is None:\n self._last_location = location\n return new_status\n\n self.actual_value += location.distance(self._last_location)\n self._last_location = location\n\n if self.actual_value > self.expected_value_success:\n self.test_status = \"SUCCESS\"\n elif (self.expected_value_acceptable is not None and\n self.actual_value > self.expected_value_acceptable):\n self.test_status = \"ACCEPTABLE\"\n else:\n self.test_status = \"RUNNING\"\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n Set final status\n \"\"\"\n if self.test_status != \"SUCCESS\":\n self.test_status = \"FAILURE\"\n self.actual_value = round(self.actual_value, 2)\n super(DrivenDistanceTest, self).terminate(new_status)\n\n\nclass AverageVelocityTest(Criterion):\n\n \"\"\"\n This class contains an atomic test for average velocity.\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - avg_velocity_success: If the actor's average velocity is more than this value (in m/s),\n the test result is SUCCESS\n - avg_velocity_acceptable: If the actor's average velocity is more than this value (in m/s),\n the test result is ACCEPTABLE\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n def __init__(self,\n actor,\n avg_velocity_success,\n avg_velocity_acceptable=None,\n optional=False,\n name=\"CheckAverageVelocity\"):\n \"\"\"\n Setup actor and average velovity expected\n \"\"\"\n super(AverageVelocityTest, self).__init__(name, actor,\n avg_velocity_success,\n avg_velocity_acceptable,\n optional)\n self._last_location = None\n self._distance = 0.0\n\n def initialise(self):\n self._last_location = CarlaDataProvider.get_location(self.actor)\n super(AverageVelocityTest, self).initialise()\n\n def update(self):\n \"\"\"\n Check velocity\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self.actor is None:\n return new_status\n\n location = CarlaDataProvider.get_location(self.actor)\n\n if location is None:\n return new_status\n\n if self._last_location is None:\n self._last_location = location\n return new_status\n\n self._distance += location.distance(self._last_location)\n self._last_location = location\n\n elapsed_time = GameTime.get_time()\n if elapsed_time > 0.0:\n self.actual_value = self._distance / elapsed_time\n\n if self.actual_value > self.expected_value_success:\n self.test_status = \"SUCCESS\"\n elif (self.expected_value_acceptable is not None and\n self.actual_value > self.expected_value_acceptable):\n self.test_status = \"ACCEPTABLE\"\n else:\n self.test_status = \"RUNNING\"\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n Set final status\n \"\"\"\n if self.test_status == \"RUNNING\":\n self.test_status = \"FAILURE\"\n super(AverageVelocityTest, self).terminate(new_status)\n\n\nclass CollisionTest(Criterion):\n\n \"\"\"\n This class contains an atomic test for collisions.\n\n Args:\n - actor (carla.Actor): CARLA actor to be used for this test\n - other_actor (carla.Actor): only collisions with this actor will be registered\n - other_actor_type (str): only collisions with actors including this type_id will count.\n Additionally, the \"miscellaneous\" tag can also be used to include all static objects in the scene\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n MIN_AREA_OF_COLLISION = 3 # If closer than this distance, the collision is ignored\n MAX_AREA_OF_COLLISION = 5 # If further than this distance, the area is forgotten\n MAX_ID_TIME = 5 # Amount of time the last collision if is remembered\n\n def __init__(self, actor, other_actor=None, other_actor_type=None,\n optional=False, name=\"CollisionTest\", terminate_on_failure=False):\n \"\"\"\n Construction with sensor setup\n \"\"\"\n super(CollisionTest, self).__init__(name, actor, 0, None, optional, terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n world = self.actor.get_world()\n blueprint = world.get_blueprint_library().find('sensor.other.collision')\n self._collision_sensor = world.spawn_actor(blueprint, carla.Transform(), attach_to=self.actor)\n self._collision_sensor.listen(lambda event: self._count_collisions(weakref.ref(self), event))\n\n self.other_actor = other_actor\n self.other_actor_type = other_actor_type\n self.registered_collisions = []\n self.last_id = None\n self.collision_time = None\n\n def update(self):\n \"\"\"\n Check collision count\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n actor_location = CarlaDataProvider.get_location(self.actor)\n new_registered_collisions = []\n\n # Loops through all the previous registered collisions\n for collision_location in self.registered_collisions:\n\n # Get the distance to the collision point\n distance_vector = actor_location - collision_location\n distance = math.sqrt(math.pow(distance_vector.x, 2) + math.pow(distance_vector.y, 2))\n\n # If far away from a previous collision, forget it\n if distance <= self.MAX_AREA_OF_COLLISION:\n new_registered_collisions.append(collision_location)\n\n self.registered_collisions = new_registered_collisions\n\n if self.last_id and GameTime.get_time() - self.collision_time > self.MAX_ID_TIME:\n self.last_id = None\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n Cleanup sensor\n \"\"\"\n if self._collision_sensor is not None:\n self._collision_sensor.destroy()\n self._collision_sensor = None\n\n super(CollisionTest, self).terminate(new_status)\n\n @staticmethod\n def _count_collisions(weak_self, event): # pylint: disable=too-many-return-statements\n \"\"\"\n Callback to update collision count\n \"\"\"\n self = weak_self()\n if not self:\n return\n\n actor_location = CarlaDataProvider.get_location(self.actor)\n\n # Ignore the current one if it is the same id as before\n if self.last_id == event.other_actor.id:\n return\n\n # Filter to only a specific actor\n if self.other_actor and self.other_actor.id != event.other_actor.id:\n return\n\n # Filter to only a specific type\n if self.other_actor_type:\n if self.other_actor_type == \"miscellaneous\":\n if \"traffic\" not in event.other_actor.type_id \\\n and \"static\" not in event.other_actor.type_id:\n return\n else:\n if self.other_actor_type not in event.other_actor.type_id:\n return\n\n # Ignore it if its too close to a previous collision (avoid micro collisions)\n for collision_location in self.registered_collisions:\n\n distance_vector = actor_location - collision_location\n distance = math.sqrt(math.pow(distance_vector.x, 2) + math.pow(distance_vector.y, 2))\n\n if distance <= self.MIN_AREA_OF_COLLISION:\n return\n\n if ('static' in event.other_actor.type_id or 'traffic' in event.other_actor.type_id) \\\n and 'sidewalk' not in event.other_actor.type_id:\n actor_type = TrafficEventType.COLLISION_STATIC\n elif 'vehicle' in event.other_actor.type_id:\n actor_type = TrafficEventType.COLLISION_VEHICLE\n elif 'walker' in event.other_actor.type_id:\n actor_type = TrafficEventType.COLLISION_PEDESTRIAN\n else:\n return\n\n collision_event = TrafficEvent(event_type=actor_type)\n collision_event.set_dict({\n 'type': event.other_actor.type_id,\n 'id': event.other_actor.id,\n 'x': actor_location.x,\n 'y': actor_location.y,\n 'z': actor_location.z})\n collision_event.set_message(\n \"Agent collided against object with type={} and id={} at (x={}, y={}, z={}, t={})\".format(\n event.other_actor.type_id,\n event.other_actor.id,\n round(actor_location.x, 3),\n round(actor_location.y, 3),\n round(actor_location.z, 3),\n CarlaDataProvider.get_time_step()))\n\n self.test_status = \"FAILURE\"\n self.actual_value += 1\n self.collision_time = GameTime.get_time()\n\n self.registered_collisions.append(actor_location)\n self.list_traffic_events.append(collision_event)\n\n # Number 0: static objects -> ignore it\n if event.other_actor.id != 0:\n self.last_id = event.other_actor.id\n\n\nclass ActorSpeedAboveThresholdTest(Criterion):\n\n \"\"\"\n This test will fail if the actor has had its linear velocity lower than a specific value for\n a specific amount of time\n Important parameters:\n - actor: CARLA actor to be used for this test\n - speed_threshold: speed required\n - below_threshold_max_time: Maximum time (in seconds) the actor can remain under the speed threshold\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n\n def __init__(self, actor, speed_threshold, below_threshold_max_time,\n name=\"ActorSpeedAboveThresholdTest\", terminate_on_failure=False):\n \"\"\"\n Class constructor.\n \"\"\"\n super(ActorSpeedAboveThresholdTest, self).__init__(name, actor, 0, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._speed_threshold = speed_threshold\n self._below_threshold_max_time = below_threshold_max_time\n self._time_last_valid_state = None\n\n def update(self):\n \"\"\"\n Check if the actor speed is above the speed_threshold\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n linear_speed = CarlaDataProvider.get_velocity(self._actor)\n if linear_speed is not None:\n if linear_speed < self._speed_threshold and self._time_last_valid_state:\n if (GameTime.get_time() - self._time_last_valid_state) > self._below_threshold_max_time:\n # Game over. The actor has been \"blocked\" for too long\n self.test_status = \"FAILURE\"\n\n # record event\n vehicle_location = CarlaDataProvider.get_location(self._actor)\n blocked_event = TrafficEvent(event_type=TrafficEventType.VEHICLE_BLOCKED)\n ActorSpeedAboveThresholdTest._set_event_message(blocked_event, vehicle_location)\n ActorSpeedAboveThresholdTest._set_event_dict(blocked_event, vehicle_location)\n self.list_traffic_events.append(blocked_event)\n else:\n self._time_last_valid_state = GameTime.get_time()\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n @staticmethod\n def _set_event_message(event, location):\n \"\"\"\n Sets the message of the event\n \"\"\"\n\n event.set_message('Agent got blocked at (x={}, y={}, z={}, t={})'.format(round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3),\n CarlaDataProvider.get_time_step()))\n\n @staticmethod\n def _set_event_dict(event, location):\n \"\"\"\n Sets the dictionary of the event\n \"\"\"\n event.set_dict({\n 'x': location.x,\n 'y': location.y,\n 'z': location.z,\n })\n\n\nclass KeepLaneTest(Criterion):\n\n \"\"\"\n This class contains an atomic test for keeping lane.\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n def __init__(self, actor, optional=False, name=\"CheckKeepLane\"):\n \"\"\"\n Construction with sensor setup\n \"\"\"\n super(KeepLaneTest, self).__init__(name, actor, 0, None, optional)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n world = self.actor.get_world()\n blueprint = world.get_blueprint_library().find('sensor.other.lane_invasion')\n self._lane_sensor = world.spawn_actor(blueprint, carla.Transform(), attach_to=self.actor)\n self._lane_sensor.listen(lambda event: self._count_lane_invasion(weakref.ref(self), event))\n\n def update(self):\n \"\"\"\n Check lane invasion count\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self.actual_value > 0:\n self.test_status = \"FAILURE\"\n else:\n self.test_status = \"SUCCESS\"\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n Cleanup sensor\n \"\"\"\n if self._lane_sensor is not None:\n self._lane_sensor.destroy()\n self._lane_sensor = None\n super(KeepLaneTest, self).terminate(new_status)\n\n @staticmethod\n def _count_lane_invasion(weak_self, event):\n \"\"\"\n Callback to update lane invasion count\n \"\"\"\n self = weak_self()\n if not self:\n return\n self.actual_value += 1\n\n\nclass ReachedRegionTest(Criterion):\n\n \"\"\"\n This class contains the reached region test\n The test is a success if the actor reaches a specified region\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - min_x, max_x, min_y, max_y: Bounding box of the checked region\n \"\"\"\n\n def __init__(self, actor, min_x, max_x, min_y, max_y, name=\"ReachedRegionTest\"):\n \"\"\"\n Setup trigger region (rectangle provided by\n [min_x,min_y] and [max_x,max_y]\n \"\"\"\n super(ReachedRegionTest, self).__init__(name, actor, 0)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._min_x = min_x\n self._max_x = max_x\n self._min_y = min_y\n self._max_y = max_y\n\n def update(self):\n \"\"\"\n Check if the actor location is within trigger region\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n in_region = False\n if self.test_status != \"SUCCESS\":\n in_region = (location.x > self._min_x and location.x < self._max_x) and (\n location.y > self._min_y and location.y < self._max_y)\n if in_region:\n self.test_status = \"SUCCESS\"\n else:\n self.test_status = \"RUNNING\"\n\n if self.test_status == \"SUCCESS\":\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass OffRoadTest(Criterion):\n\n \"\"\"\n Atomic containing a test to detect when an actor deviates from the driving lanes. This atomic can\n fail when actor has spent a specific time outside driving lanes (defined by OpenDRIVE). Simplified\n version of OnSidewalkTest, and doesn't relly on waypoints with *Sidewalk* lane types\n\n Args:\n actor (carla.Actor): CARLA actor to be used for this test\n duration (float): Time spent at sidewalks before the atomic fails.\n If terminate_on_failure isn't active, this is ignored.\n optional (bool): If True, the result is not considered for an overall pass/fail result\n when using the output argument\n terminate_on_failure (bool): If True, the atomic will fail when the duration condition has been met.\n \"\"\"\n\n def __init__(self, actor, duration=0, optional=False, terminate_on_failure=False, name=\"OffRoadTest\"):\n \"\"\"\n Setup of the variables\n \"\"\"\n super(OffRoadTest, self).__init__(name, actor, 0, None, optional, terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n self._map = CarlaDataProvider.get_map()\n self._offroad = False\n\n self._duration = duration\n self._prev_time = None\n self._time_offroad = 0\n\n def update(self):\n \"\"\"\n First, transforms the actor's current position to its corresponding waypoint. This is\n filtered to only use waypoints of type Driving or Parking. Depending on these results,\n the actor will be considered to be outside (or inside) driving lanes.\n\n returns:\n py_trees.common.Status.FAILURE: when the actor has spent a given duration outside driving lanes\n py_trees.common.Status.RUNNING: the rest of the time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n current_location = CarlaDataProvider.get_location(self.actor)\n\n # Get the waypoint at the current location to see if the actor is offroad\n drive_waypoint = self._map.get_waypoint(\n current_location,\n project_to_road=False\n )\n park_waypoint = self._map.get_waypoint(\n current_location,\n project_to_road=False,\n lane_type=carla.LaneType.Parking\n )\n if drive_waypoint or park_waypoint:\n self._offroad = False\n else:\n self._offroad = True\n\n # Counts the time offroad\n if self._offroad:\n if self._prev_time is None:\n self._prev_time = GameTime.get_time()\n else:\n curr_time = GameTime.get_time()\n self._time_offroad += curr_time - self._prev_time\n self._prev_time = curr_time\n else:\n self._prev_time = None\n\n if self._time_offroad > self._duration:\n self.test_status = \"FAILURE\"\n\n if self._terminate_on_failure and self.test_status == \"FAILURE\":\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass EndofRoadTest(Criterion):\n\n \"\"\"\n Atomic containing a test to detect when an actor has changed to a different road\n\n Args:\n actor (carla.Actor): CARLA actor to be used for this test\n duration (float): Time spent after ending the road before the atomic fails.\n If terminate_on_failure isn't active, this is ignored.\n optional (bool): If True, the result is not considered for an overall pass/fail result\n when using the output argument\n terminate_on_failure (bool): If True, the atomic will fail when the duration condition has been met.\n \"\"\"\n\n def __init__(self, actor, duration=0, optional=False, terminate_on_failure=False, name=\"EndofRoadTest\"):\n \"\"\"\n Setup of the variables\n \"\"\"\n super(EndofRoadTest, self).__init__(name, actor, 0, None, optional, terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n self._map = CarlaDataProvider.get_map()\n self._end_of_road = False\n\n self._duration = duration\n self._start_time = None\n self._time_end_road = 0\n self._road_id = None\n\n def update(self):\n \"\"\"\n First, transforms the actor's current position to its corresponding waypoint. Then the road id\n is compared with the initial one and if that's the case, a time is started\n\n returns:\n py_trees.common.Status.FAILURE: when the actor has spent a given duration outside driving lanes\n py_trees.common.Status.RUNNING: the rest of the time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n current_location = CarlaDataProvider.get_location(self.actor)\n current_waypoint = self._map.get_waypoint(current_location)\n\n # Get the current road id\n if self._road_id is None:\n self._road_id = current_waypoint.road_id\n\n else:\n # Wait until the actor has left the road\n if self._road_id != current_waypoint.road_id or self._start_time:\n\n # Start counting\n if self._start_time is None:\n self._start_time = GameTime.get_time()\n return new_status\n\n curr_time = GameTime.get_time()\n self._time_end_road = curr_time - self._start_time\n\n if self._time_end_road > self._duration:\n self.test_status = \"FAILURE\"\n self.actual_value += 1\n return py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass OnSidewalkTest(Criterion):\n\n \"\"\"\n Atomic containing a test to detect sidewalk invasions of a specific actor. This atomic can\n fail when actor has spent a specific time outside driving lanes (defined by OpenDRIVE).\n\n Args:\n actor (carla.Actor): CARLA actor to be used for this test\n duration (float): Time spent at sidewalks before the atomic fails.\n If terminate_on_failure isn't active, this is ignored.\n optional (bool): If True, the result is not considered for an overall pass/fail result\n when using the output argument\n terminate_on_failure (bool): If True, the atomic will fail when the duration condition has been met.\n \"\"\"\n\n def __init__(self, actor, duration=0, optional=False, terminate_on_failure=False, name=\"OnSidewalkTest\"):\n \"\"\"\n Construction with sensor setup\n \"\"\"\n super(OnSidewalkTest, self).__init__(name, actor, 0, None, optional, terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n self._actor = actor\n self._map = CarlaDataProvider.get_map()\n self._onsidewalk_active = False\n self._outside_lane_active = False\n\n self._actor_location = self._actor.get_location()\n self._wrong_sidewalk_distance = 0\n self._wrong_outside_lane_distance = 0\n self._sidewalk_start_location = None\n self._outside_lane_start_location = None\n self._duration = duration\n self._prev_time = None\n self._time_outside_lanes = 0\n\n def update(self):\n \"\"\"\n First, transforms the actor's current position as well as its four corners to their\n corresponding waypoints. Depending on their lane type, the actor will be considered to be\n outside (or inside) driving lanes.\n\n returns:\n py_trees.common.Status.FAILURE: when the actor has spent a given duration outside\n driving lanes and terminate_on_failure is active\n py_trees.common.Status.RUNNING: the rest of the time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._terminate_on_failure and self.test_status == \"FAILURE\":\n new_status = py_trees.common.Status.FAILURE\n\n # Some of the vehicle parameters\n current_tra = CarlaDataProvider.get_transform(self._actor)\n current_loc = current_tra.location\n current_wp = self._map.get_waypoint(current_loc, lane_type=carla.LaneType.Any)\n\n # Case 1) Car center is at a sidewalk\n if current_wp.lane_type == carla.LaneType.Sidewalk:\n if not self._onsidewalk_active:\n self._onsidewalk_active = True\n self._sidewalk_start_location = current_loc\n\n # Case 2) Not inside allowed zones (Driving and Parking)\n elif current_wp.lane_type != carla.LaneType.Driving \\\n and current_wp.lane_type != carla.LaneType.Parking:\n\n # Get the vertices of the vehicle\n heading_vec = current_tra.get_forward_vector()\n heading_vec.z = 0\n heading_vec = heading_vec / math.sqrt(math.pow(heading_vec.x, 2) + math.pow(heading_vec.y, 2))\n perpendicular_vec = carla.Vector3D(-heading_vec.y, heading_vec.x, 0)\n\n extent = self.actor.bounding_box.extent\n x_boundary_vector = heading_vec * extent.x\n y_boundary_vector = perpendicular_vec * extent.y\n\n bbox = [\n current_loc + carla.Location(x_boundary_vector - y_boundary_vector),\n current_loc + carla.Location(x_boundary_vector + y_boundary_vector),\n current_loc + carla.Location(-1 * x_boundary_vector - y_boundary_vector),\n current_loc + carla.Location(-1 * x_boundary_vector + y_boundary_vector)]\n\n bbox_wp = [\n self._map.get_waypoint(bbox[0], lane_type=carla.LaneType.Any),\n self._map.get_waypoint(bbox[1], lane_type=carla.LaneType.Any),\n self._map.get_waypoint(bbox[2], lane_type=carla.LaneType.Any),\n self._map.get_waypoint(bbox[3], lane_type=carla.LaneType.Any)]\n\n # Case 2.1) Not quite outside yet\n if bbox_wp[0].lane_type == (carla.LaneType.Driving or carla.LaneType.Parking) \\\n or bbox_wp[1].lane_type == (carla.LaneType.Driving or carla.LaneType.Parking) \\\n or bbox_wp[2].lane_type == (carla.LaneType.Driving or carla.LaneType.Parking) \\\n or bbox_wp[3].lane_type == (carla.LaneType.Driving or carla.LaneType.Parking):\n\n self._onsidewalk_active = False\n self._outside_lane_active = False\n\n # Case 2.2) At the mini Shoulders between Driving and Sidewalk\n elif bbox_wp[0].lane_type == carla.LaneType.Sidewalk \\\n or bbox_wp[1].lane_type == carla.LaneType.Sidewalk \\\n or bbox_wp[2].lane_type == carla.LaneType.Sidewalk \\\n or bbox_wp[3].lane_type == carla.LaneType.Sidewalk:\n\n if not self._onsidewalk_active:\n self._onsidewalk_active = True\n self._sidewalk_start_location = current_loc\n\n else:\n distance_vehicle_wp = current_loc.distance(current_wp.transform.location)\n\n # Case 2.3) Outside lane\n if distance_vehicle_wp >= current_wp.lane_width / 2:\n\n if not self._outside_lane_active:\n self._outside_lane_active = True\n self._outside_lane_start_location = current_loc\n\n # Case 2.4) Very very edge case (but still inside driving lanes)\n else:\n self._onsidewalk_active = False\n self._outside_lane_active = False\n\n # Case 3) Driving and Parking conditions\n else:\n # Check for false positives at junctions\n if current_wp.is_junction:\n distance_vehicle_wp = math.sqrt(\n math.pow(current_wp.transform.location.x - current_loc.x, 2) +\n math.pow(current_wp.transform.location.y - current_loc.y, 2))\n\n if distance_vehicle_wp <= current_wp.lane_width / 2:\n self._onsidewalk_active = False\n self._outside_lane_active = False\n # Else, do nothing, the waypoint is too far to consider it a correct position\n else:\n\n self._onsidewalk_active = False\n self._outside_lane_active = False\n\n # Counts the time offroad\n if self._onsidewalk_active or self._outside_lane_active:\n if self._prev_time is None:\n self._prev_time = GameTime.get_time()\n else:\n curr_time = GameTime.get_time()\n self._time_outside_lanes += curr_time - self._prev_time\n self._prev_time = curr_time\n else:\n self._prev_time = None\n\n if self._time_outside_lanes > self._duration:\n self.test_status = \"FAILURE\"\n\n # Update the distances\n distance_vector = CarlaDataProvider.get_location(self._actor) - self._actor_location\n distance = math.sqrt(math.pow(distance_vector.x, 2) + math.pow(distance_vector.y, 2))\n\n if distance >= 0.02: # Used to avoid micro-changes adding to considerable sums\n self._actor_location = CarlaDataProvider.get_location(self._actor)\n\n if self._onsidewalk_active:\n self._wrong_sidewalk_distance += distance\n elif self._outside_lane_active:\n # Only add if car is outside the lane but ISN'T in a junction\n self._wrong_outside_lane_distance += distance\n\n # Register the sidewalk event\n if not self._onsidewalk_active and self._wrong_sidewalk_distance > 0:\n\n self.actual_value += 1\n\n onsidewalk_event = TrafficEvent(event_type=TrafficEventType.ON_SIDEWALK_INFRACTION)\n self._set_event_message(\n onsidewalk_event, self._sidewalk_start_location, self._wrong_sidewalk_distance)\n self._set_event_dict(\n onsidewalk_event, self._sidewalk_start_location, self._wrong_sidewalk_distance)\n\n self._onsidewalk_active = False\n self._wrong_sidewalk_distance = 0\n self.list_traffic_events.append(onsidewalk_event)\n\n # Register the outside of a lane event\n if not self._outside_lane_active and self._wrong_outside_lane_distance > 0:\n\n self.actual_value += 1\n\n outsidelane_event = TrafficEvent(event_type=TrafficEventType.OUTSIDE_LANE_INFRACTION)\n self._set_event_message(\n outsidelane_event, self._outside_lane_start_location, self._wrong_outside_lane_distance)\n self._set_event_dict(\n outsidelane_event, self._outside_lane_start_location, self._wrong_outside_lane_distance)\n\n self._outside_lane_active = False\n self._wrong_outside_lane_distance = 0\n self.list_traffic_events.append(outsidelane_event)\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n If there is currently an event running, it is registered\n \"\"\"\n # If currently at a sidewalk, register the event\n if self._onsidewalk_active:\n\n self.actual_value += 1\n\n onsidewalk_event = TrafficEvent(event_type=TrafficEventType.ON_SIDEWALK_INFRACTION)\n self._set_event_message(\n onsidewalk_event, self._sidewalk_start_location, self._wrong_sidewalk_distance)\n self._set_event_dict(\n onsidewalk_event, self._sidewalk_start_location, self._wrong_sidewalk_distance)\n\n self._onsidewalk_active = False\n self._wrong_sidewalk_distance = 0\n self.list_traffic_events.append(onsidewalk_event)\n\n # If currently outside of our lane, register the event\n if self._outside_lane_active:\n\n self.actual_value += 1\n\n outsidelane_event = TrafficEvent(event_type=TrafficEventType.OUTSIDE_LANE_INFRACTION)\n self._set_event_message(\n outsidelane_event, self._outside_lane_start_location, self._wrong_outside_lane_distance)\n self._set_event_dict(\n outsidelane_event, self._outside_lane_start_location, self._wrong_outside_lane_distance)\n\n self._outside_lane_active = False\n self._wrong_outside_lane_distance = 0\n self.list_traffic_events.append(outsidelane_event)\n\n super(OnSidewalkTest, self).terminate(new_status)\n\n def _set_event_message(self, event, location, distance):\n \"\"\"\n Sets the message of the event\n \"\"\"\n if event.get_type() == TrafficEventType.ON_SIDEWALK_INFRACTION:\n message_start = 'Agent invaded the sidewalk'\n else:\n message_start = 'Agent went outside the lane'\n\n event.set_message(\n '{} for about {} meters, starting at (x={}, y={}, z={}, t={})'.format(\n message_start,\n round(distance, 3),\n round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3),\n CarlaDataProvider.get_time_step()))\n\n def _set_event_dict(self, event, location, distance):\n \"\"\"\n Sets the dictionary of the event\n \"\"\"\n event.set_dict({\n 'x': location.x,\n 'y': location.y,\n 'z': location.z,\n 'distance': distance})\n\n\nclass OutsideRouteLanesTest(Criterion):\n\n \"\"\"\n Atomic to detect if the vehicle is either on a sidewalk or at a wrong lane. The distance spent outside\n is computed and it is returned as a percentage of the route distance traveled.\n\n Args:\n actor (carla.ACtor): CARLA actor to be used for this test\n route (list [carla.Location, connection]): series of locations representing the route waypoints\n optional (bool): If True, the result is not considered for an overall pass/fail result\n \"\"\"\n\n ALLOWED_OUT_DISTANCE = 1.3 # At least 0.5, due to the mini-shoulder between lanes and sidewalks\n MAX_ALLOWED_VEHICLE_ANGLE = 120.0 # Maximum angle between the yaw and waypoint lane\n MAX_ALLOWED_WAYPOINT_ANGLE = 150.0 # Maximum change between the yaw-lane angle between frames\n WINDOWS_SIZE = 3 # Amount of additional waypoints checked (in case the first on fails)\n\n def __init__(self, actor, route, optional=False, name=\"OutsideRouteLanesTest\"):\n \"\"\"\n Constructor\n \"\"\"\n super(OutsideRouteLanesTest, self).__init__(name, actor, 0, None, optional)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n self._actor = actor\n self._route = route\n self._current_index = 0\n self._route_length = len(self._route)\n self._waypoints, _ = zip(*self._route)\n\n self._map = CarlaDataProvider.get_map()\n self._pre_ego_waypoint = self._map.get_waypoint(self._actor.get_location())\n\n self._outside_lane_active = False\n self._wrong_lane_active = False\n self._last_road_id = None\n self._last_lane_id = None\n self._total_distance = 0\n self._wrong_distance = 0\n\n def update(self):\n \"\"\"\n Transforms the actor location and its four corners to waypoints. Depending on its types,\n the actor will be considered to be at driving lanes, sidewalk or offroad.\n\n returns:\n py_trees.common.Status.FAILURE: when the actor has left driving and terminate_on_failure is active\n py_trees.common.Status.RUNNING: the rest of the time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n # Some of the vehicle parameters\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n # 1) Check if outside route lanes\n self._is_outside_driving_lanes(location)\n self._is_at_wrong_lane(location)\n\n if self._outside_lane_active or self._wrong_lane_active:\n self.test_status = \"FAILURE\"\n\n # 2) Get the traveled distance\n for index in range(self._current_index + 1,\n min(self._current_index + self.WINDOWS_SIZE + 1, self._route_length)):\n # Get the dot product to know if it has passed this location\n index_location = self._waypoints[index]\n index_waypoint = self._map.get_waypoint(index_location)\n\n wp_dir = index_waypoint.transform.get_forward_vector() # Waypoint's forward vector\n wp_veh = location - index_location # vector waypoint - vehicle\n dot_ve_wp = wp_veh.x * wp_dir.x + wp_veh.y * wp_dir.y + wp_veh.z * wp_dir.z\n\n if dot_ve_wp > 0:\n # Get the distance traveled\n index_location = self._waypoints[index]\n current_index_location = self._waypoints[self._current_index]\n new_dist = current_index_location.distance(index_location)\n\n # Add it to the total distance\n self._current_index = index\n self._total_distance += new_dist\n\n # And to the wrong one if outside route lanes\n if self._outside_lane_active or self._wrong_lane_active:\n self._wrong_distance += new_dist\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def _is_outside_driving_lanes(self, location):\n \"\"\"\n Detects if the ego_vehicle is outside driving lanes\n \"\"\"\n\n current_driving_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True)\n current_parking_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Parking, project_to_road=True)\n\n driving_distance = location.distance(current_driving_wp.transform.location)\n if current_parking_wp is not None: # Some towns have no parking\n parking_distance = location.distance(current_parking_wp.transform.location)\n else:\n parking_distance = float('inf')\n\n if driving_distance >= parking_distance:\n distance = parking_distance\n lane_width = current_parking_wp.lane_width\n else:\n distance = driving_distance\n lane_width = current_driving_wp.lane_width\n\n self._outside_lane_active = bool(distance > (lane_width / 2 + self.ALLOWED_OUT_DISTANCE))\n\n def _is_at_wrong_lane(self, location):\n \"\"\"\n Detects if the ego_vehicle has invaded a wrong lane\n \"\"\"\n\n current_waypoint = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True)\n current_lane_id = current_waypoint.lane_id\n current_road_id = current_waypoint.road_id\n\n # Lanes and roads are too chaotic at junctions\n if current_waypoint.is_junction:\n self._wrong_lane_active = False\n elif self._last_road_id != current_road_id or self._last_lane_id != current_lane_id:\n\n # Route direction can be considered continuous, except after exiting a junction.\n if self._pre_ego_waypoint.is_junction:\n yaw_waypt = current_waypoint.transform.rotation.yaw % 360\n yaw_actor = self._actor.get_transform().rotation.yaw % 360\n\n vehicle_lane_angle = (yaw_waypt - yaw_actor) % 360\n\n if vehicle_lane_angle < self.MAX_ALLOWED_VEHICLE_ANGLE \\\n or vehicle_lane_angle > (360 - self.MAX_ALLOWED_VEHICLE_ANGLE):\n self._wrong_lane_active = False\n else:\n self._wrong_lane_active = True\n\n else:\n # Check for a big gap in waypoint directions.\n yaw_pre_wp = self._pre_ego_waypoint.transform.rotation.yaw % 360\n yaw_cur_wp = current_waypoint.transform.rotation.yaw % 360\n\n waypoint_angle = (yaw_pre_wp - yaw_cur_wp) % 360\n\n if waypoint_angle >= self.MAX_ALLOWED_WAYPOINT_ANGLE \\\n and waypoint_angle <= (360 - self.MAX_ALLOWED_WAYPOINT_ANGLE):\n\n # Is the ego vehicle going back to the lane, or going out? Take the opposite\n self._wrong_lane_active = not bool(self._wrong_lane_active)\n else:\n\n # Changing to a lane with the same direction\n self._wrong_lane_active = False\n\n # Remember the last state\n self._last_lane_id = current_lane_id\n self._last_road_id = current_road_id\n self._pre_ego_waypoint = current_waypoint\n\n def terminate(self, new_status):\n \"\"\"\n If there is currently an event running, it is registered\n \"\"\"\n\n if self._wrong_distance > 0:\n\n percentage = self._wrong_distance / self._total_distance * 100\n\n outside_lane = TrafficEvent(event_type=TrafficEventType.OUTSIDE_ROUTE_LANES_INFRACTION)\n outside_lane.set_message(\n \"Agent went outside its route lanes for about {} meters \"\n \"({}% of the completed route, t={})\".format(\n round(self._wrong_distance, 3),\n round(percentage, 2),\n CarlaDataProvider.get_time_step()))\n\n outside_lane.set_dict({\n 'distance': self._wrong_distance,\n 'percentage': percentage\n })\n\n self._wrong_distance = 0\n self.list_traffic_events.append(outside_lane)\n self.actual_value = round(percentage, 2)\n\n super(OutsideRouteLanesTest, self).terminate(new_status)\n\n\nclass WrongLaneTest(Criterion):\n\n \"\"\"\n This class contains an atomic test to detect invasions to wrong direction lanes.\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - optional [optional]: If True, the result is not considered for an overall pass/fail result\n \"\"\"\n MAX_ALLOWED_ANGLE = 120.0\n MAX_ALLOWED_WAYPOINT_ANGLE = 150.0\n\n def __init__(self, actor, optional=False, name=\"WrongLaneTest\"):\n \"\"\"\n Construction with sensor setup\n \"\"\"\n super(WrongLaneTest, self).__init__(name, actor, 0, None, optional)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n self._actor = actor\n self._map = CarlaDataProvider.get_map()\n self._last_lane_id = None\n self._last_road_id = None\n\n self._in_lane = True\n self._wrong_distance = 0\n self._actor_location = self._actor.get_location()\n self._previous_lane_waypoint = self._map.get_waypoint(self._actor.get_location())\n self._wrong_lane_start_location = None\n\n def update(self):\n \"\"\"\n Check lane invasion count\n \"\"\"\n\n new_status = py_trees.common.Status.RUNNING\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n lane_waypoint = self._map.get_waypoint(self._actor.get_location())\n current_lane_id = lane_waypoint.lane_id\n current_road_id = lane_waypoint.road_id\n\n if (self._last_road_id != current_road_id or self._last_lane_id != current_lane_id) \\\n and not lane_waypoint.is_junction:\n next_waypoint = lane_waypoint.next(2.0)[0]\n\n if not next_waypoint:\n return new_status\n\n # The waypoint route direction can be considered continuous.\n # Therefore just check for a big gap in waypoint directions.\n previous_lane_direction = self._previous_lane_waypoint.transform.get_forward_vector()\n current_lane_direction = lane_waypoint.transform.get_forward_vector()\n\n p_lane_vector = np.array([previous_lane_direction.x, previous_lane_direction.y])\n c_lane_vector = np.array([current_lane_direction.x, current_lane_direction.y])\n\n waypoint_angle = math.degrees(\n math.acos(np.clip(np.dot(p_lane_vector, c_lane_vector) /\n (np.linalg.norm(p_lane_vector) * np.linalg.norm(c_lane_vector)), -1.0, 1.0)))\n\n if waypoint_angle > self.MAX_ALLOWED_WAYPOINT_ANGLE and self._in_lane:\n\n self.test_status = \"FAILURE\"\n self._in_lane = False\n self.actual_value += 1\n self._wrong_lane_start_location = self._actor_location\n\n else:\n # Reset variables\n self._in_lane = True\n\n # Continuity is broken after a junction so check vehicle-lane angle instead\n if self._previous_lane_waypoint.is_junction:\n\n vector_wp = np.array([next_waypoint.transform.location.x - lane_waypoint.transform.location.x,\n next_waypoint.transform.location.y - lane_waypoint.transform.location.y])\n\n vector_actor = np.array([math.cos(math.radians(self._actor.get_transform().rotation.yaw)),\n math.sin(math.radians(self._actor.get_transform().rotation.yaw))])\n\n vehicle_lane_angle = math.degrees(\n math.acos(np.clip(np.dot(vector_actor, vector_wp) / (np.linalg.norm(vector_wp)), -1.0, 1.0)))\n\n if vehicle_lane_angle > self.MAX_ALLOWED_ANGLE:\n\n self.test_status = \"FAILURE\"\n self._in_lane = False\n self.actual_value += 1\n self._wrong_lane_start_location = self._actor.get_location()\n\n # Keep adding \"meters\" to the counter\n distance_vector = self._actor.get_location() - self._actor_location\n distance = math.sqrt(math.pow(distance_vector.x, 2) + math.pow(distance_vector.y, 2))\n\n if distance >= 0.02: # Used to avoid micro-changes adding add to considerable sums\n self._actor_location = CarlaDataProvider.get_location(self._actor)\n\n if not self._in_lane and not lane_waypoint.is_junction:\n self._wrong_distance += distance\n\n # Register the event\n if self._in_lane and self._wrong_distance > 0:\n\n wrong_way_event = TrafficEvent(event_type=TrafficEventType.WRONG_WAY_INFRACTION)\n self._set_event_message(wrong_way_event, self._wrong_lane_start_location,\n self._wrong_distance, current_road_id, current_lane_id)\n self._set_event_dict(wrong_way_event, self._wrong_lane_start_location,\n self._wrong_distance, current_road_id, current_lane_id)\n\n self.list_traffic_events.append(wrong_way_event)\n self._wrong_distance = 0\n\n # Remember the last state\n self._last_lane_id = current_lane_id\n self._last_road_id = current_road_id\n self._previous_lane_waypoint = lane_waypoint\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n If there is currently an event running, it is registered\n \"\"\"\n if not self._in_lane:\n\n lane_waypoint = self._map.get_waypoint(self._actor.get_location())\n current_lane_id = lane_waypoint.lane_id\n current_road_id = lane_waypoint.road_id\n\n wrong_way_event = TrafficEvent(event_type=TrafficEventType.WRONG_WAY_INFRACTION)\n self._set_event_message(wrong_way_event, self._wrong_lane_start_location,\n self._wrong_distance, current_road_id, current_lane_id)\n self._set_event_dict(wrong_way_event, self._wrong_lane_start_location,\n self._wrong_distance, current_road_id, current_lane_id)\n\n self._wrong_distance = 0\n self._in_lane = True\n self.list_traffic_events.append(wrong_way_event)\n\n super(WrongLaneTest, self).terminate(new_status)\n\n def _set_event_message(self, event, location, distance, road_id, lane_id):\n \"\"\"\n Sets the message of the event\n \"\"\"\n\n event.set_message(\n \"Agent invaded a lane in opposite direction for {} meters, starting at (x={}, y={}, z={}, t={}). \"\n \"road_id={}, lane_id={}\".format(\n round(distance, 3),\n round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3),\n CarlaDataProvider.get_time_step(),\n road_id,\n lane_id))\n\n def _set_event_dict(self, event, location, distance, road_id, lane_id):\n \"\"\"\n Sets the dictionary of the event\n \"\"\"\n event.set_dict({\n 'x': location.x,\n 'y': location.y,\n 'z': location.y,\n 'distance': distance,\n 'road_id': road_id,\n 'lane_id': lane_id})\n\n\nclass InRadiusRegionTest(Criterion):\n\n \"\"\"\n The test is a success if the actor is within a given radius of a specified region\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - x, y, radius: Position (x,y) and radius (in meters) used to get the checked region\n \"\"\"\n\n def __init__(self, actor, x, y, radius, name=\"InRadiusRegionTest\"):\n \"\"\"\n \"\"\"\n super(InRadiusRegionTest, self).__init__(name, actor, 0)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._x = x # pylint: disable=invalid-name\n self._y = y # pylint: disable=invalid-name\n self._radius = radius\n\n def update(self):\n \"\"\"\n Check if the actor location is within trigger region\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n if self.test_status != \"SUCCESS\":\n in_radius = math.sqrt(((location.x - self._x)**2) + ((location.y - self._y)**2)) < self._radius\n if in_radius:\n route_completion_event = TrafficEvent(event_type=TrafficEventType.ROUTE_COMPLETED)\n route_completion_event.set_message(\"Destination was successfully reached\")\n self.list_traffic_events.append(route_completion_event)\n self.test_status = \"SUCCESS\"\n else:\n self.test_status = \"RUNNING\"\n\n if self.test_status == \"SUCCESS\":\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InRouteTest(Criterion):\n\n \"\"\"\n The test is a success if the actor is never outside route. The actor can go outside of the route\n but only for a certain amount of distance\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - route: Route to be checked\n - offroad_max: Maximum distance (in meters) the actor can deviate from the route\n - offroad_min: Maximum safe distance (in meters). Might eventually cause failure\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n MAX_ROUTE_PERCENTAGE = 30 # %\n WINDOWS_SIZE = 5 # Amount of additional waypoints checked\n\n def __init__(self, actor, route, offroad_min=-1, offroad_max=30, name=\"InRouteTest\", terminate_on_failure=False):\n \"\"\"\n \"\"\"\n super(InRouteTest, self).__init__(name, actor, 0, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._route = route\n self._offroad_max = offroad_max\n # Unless specified, halve of the max value\n if offroad_min == -1:\n self._offroad_min = self._offroad_max / 2\n else:\n self._offroad_min = self._offroad_min\n\n self._world = CarlaDataProvider.get_world()\n self._waypoints, _ = zip(*self._route)\n self._route_length = len(self._route)\n self._current_index = 0\n self._out_route_distance = 0\n self._in_safe_route = True\n\n self._accum_meters = []\n prev_wp = self._waypoints[0]\n for i, wp in enumerate(self._waypoints):\n d = wp.distance(prev_wp)\n if i > 0:\n accum = self._accum_meters[i - 1]\n else:\n accum = 0\n\n self._accum_meters.append(d + accum)\n prev_wp = wp\n\n # Blackboard variable\n blackv = py_trees.blackboard.Blackboard()\n _ = blackv.set(\"InRoute\", True)\n\n def update(self):\n \"\"\"\n Check if the actor location is within trigger region\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n elif self.test_status == \"RUNNING\" or self.test_status == \"INIT\":\n\n off_route = True\n\n shortest_distance = float('inf')\n closest_index = -1\n\n # Get the closest distance\n for index in range(self._current_index,\n min(self._current_index + self.WINDOWS_SIZE + 1, self._route_length)):\n ref_waypoint = self._waypoints[index]\n distance = math.sqrt(((location.x - ref_waypoint.x) ** 2) + ((location.y - ref_waypoint.y) ** 2))\n if distance <= shortest_distance:\n closest_index = index\n shortest_distance = distance\n\n if closest_index == -1 or shortest_distance == float('inf'):\n return new_status\n\n # Check if the actor is out of route\n if shortest_distance < self._offroad_max:\n off_route = False\n self._in_safe_route = bool(shortest_distance < self._offroad_min)\n\n # If actor advanced a step, record the distance\n if self._current_index != closest_index:\n\n new_dist = self._accum_meters[closest_index] - self._accum_meters[self._current_index]\n\n # If too far from the route, add it and check if its value\n if not self._in_safe_route:\n self._out_route_distance += new_dist\n out_route_percentage = 100 * self._out_route_distance / self._accum_meters[-1]\n if out_route_percentage > self.MAX_ROUTE_PERCENTAGE:\n off_route = True\n\n self._current_index = closest_index\n\n if off_route:\n # Blackboard variable\n blackv = py_trees.blackboard.Blackboard()\n _ = blackv.set(\"InRoute\", False)\n\n route_deviation_event = TrafficEvent(event_type=TrafficEventType.ROUTE_DEVIATION)\n route_deviation_event.set_message(\n \"Agent deviated from the route at (x={}, y={}, z={}, t={})\".format(\n round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3),\n CarlaDataProvider.get_time_step()))\n route_deviation_event.set_dict({\n 'x': location.x,\n 'y': location.y,\n 'z': location.z})\n\n self.list_traffic_events.append(route_deviation_event)\n\n self.test_status = \"FAILURE\"\n self.actual_value += 1\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass RouteCompletionTest(Criterion):\n\n \"\"\"\n Check at which stage of the route is the actor at each tick\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - route: Route to be checked\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n DISTANCE_THRESHOLD = 10.0 # meters\n WINDOWS_SIZE = 2\n\n def __init__(self, actor, route, name=\"RouteCompletionTest\", terminate_on_failure=False):\n \"\"\"\n \"\"\"\n super(RouteCompletionTest, self).__init__(name, actor, 100, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._route = route\n self._map = CarlaDataProvider.get_map()\n\n self._wsize = self.WINDOWS_SIZE\n self._current_index = 0\n self._route_length = len(self._route)\n self._waypoints, _ = zip(*self._route)\n self.target = self._waypoints[-1]\n\n self._accum_meters = []\n prev_wp = self._waypoints[0]\n for i, wp in enumerate(self._waypoints):\n d = wp.distance(prev_wp)\n if i > 0:\n accum = self._accum_meters[i - 1]\n else:\n accum = 0\n\n self._accum_meters.append(d + accum)\n prev_wp = wp\n\n self._traffic_event = TrafficEvent(event_type=TrafficEventType.ROUTE_COMPLETION)\n self.list_traffic_events.append(self._traffic_event)\n self._percentage_route_completed = 0.0\n\n def update(self):\n \"\"\"\n Check if the actor location is within trigger region\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n if location is None:\n return new_status\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n elif self.test_status == \"RUNNING\" or self.test_status == \"INIT\":\n\n for index in range(self._current_index, min(self._current_index + self._wsize + 1, self._route_length)):\n # Get the dot product to know if it has passed this location\n ref_waypoint = self._waypoints[index]\n wp = self._map.get_waypoint(ref_waypoint)\n wp_dir = wp.transform.get_forward_vector() # Waypoint's forward vector\n wp_veh = location - ref_waypoint # vector waypoint - vehicle\n dot_ve_wp = wp_veh.x * wp_dir.x + wp_veh.y * wp_dir.y + wp_veh.z * wp_dir.z\n\n if dot_ve_wp > 0:\n # good! segment completed!\n self._current_index = index\n self._percentage_route_completed = 100.0 * float(self._accum_meters[self._current_index]) \\\n / float(self._accum_meters[-1])\n self._traffic_event.set_dict({\n 'route_completed': self._percentage_route_completed})\n self._traffic_event.set_message(\n \"Agent has completed > {:.2f}% of the route\".format(\n self._percentage_route_completed))\n\n if self._percentage_route_completed > 99.0 and location.distance(self.target) < self.DISTANCE_THRESHOLD:\n route_completion_event = TrafficEvent(event_type=TrafficEventType.ROUTE_COMPLETED)\n route_completion_event.set_message(\"Destination was successfully reached\")\n self.list_traffic_events.append(route_completion_event)\n self.test_status = \"SUCCESS\"\n self._percentage_route_completed = 100\n\n elif self.test_status == \"SUCCESS\":\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def terminate(self, new_status):\n \"\"\"\n Set test status to failure if not successful and terminate\n \"\"\"\n self.actual_value = round(self._percentage_route_completed, 2)\n\n if self.test_status == \"INIT\":\n self.test_status = \"FAILURE\"\n super(RouteCompletionTest, self).terminate(new_status)\n\n\nclass RunningRedLightTest(Criterion):\n\n \"\"\"\n Check if an actor is running a red light\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n DISTANCE_LIGHT = 15 # m\n\n def __init__(self, actor, name=\"RunningRedLightTest\", terminate_on_failure=False):\n \"\"\"\n Init\n \"\"\"\n super(RunningRedLightTest, self).__init__(name, actor, 0, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._world = actor.get_world()\n self._map = CarlaDataProvider.get_map()\n self._list_traffic_lights = []\n self._last_red_light_id = None\n self.actual_value = 0\n self.debug = False\n\n all_actors = self._world.get_actors()\n for _actor in all_actors:\n if 'traffic_light' in _actor.type_id:\n center, waypoints = self.get_traffic_light_waypoints(_actor)\n self._list_traffic_lights.append((_actor, center, waypoints))\n\n # pylint: disable=no-self-use\n def is_vehicle_crossing_line(self, seg1, seg2):\n \"\"\"\n check if vehicle crosses a line segment\n \"\"\"\n line1 = shapely.geometry.LineString([(seg1[0].x, seg1[0].y), (seg1[1].x, seg1[1].y)])\n line2 = shapely.geometry.LineString([(seg2[0].x, seg2[0].y), (seg2[1].x, seg2[1].y)])\n inter = line1.intersection(line2)\n\n return not inter.is_empty\n\n def update(self):\n \"\"\"\n Check if the actor is running a red light\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n transform = CarlaDataProvider.get_transform(self._actor)\n location = transform.location\n if location is None:\n return new_status\n\n veh_extent = self._actor.bounding_box.extent.x\n\n tail_close_pt = self.rotate_point(carla.Vector3D(-0.8 * veh_extent, 0.0, location.z), transform.rotation.yaw)\n tail_close_pt = location + carla.Location(tail_close_pt)\n\n tail_far_pt = self.rotate_point(carla.Vector3D(-veh_extent - 1, 0.0, location.z), transform.rotation.yaw)\n tail_far_pt = location + carla.Location(tail_far_pt)\n\n for traffic_light, center, waypoints in self._list_traffic_lights:\n\n if self.debug:\n z = 2.1\n if traffic_light.state == carla.TrafficLightState.Red:\n color = carla.Color(155, 0, 0)\n elif traffic_light.state == carla.TrafficLightState.Green:\n color = carla.Color(0, 155, 0)\n else:\n color = carla.Color(155, 155, 0)\n self._world.debug.draw_point(center + carla.Location(z=z), size=0.2, color=color, life_time=0.01)\n for wp in waypoints:\n text = \"{}.{}\".format(wp.road_id, wp.lane_id)\n self._world.debug.draw_string(\n wp.transform.location + carla.Location(x=1, z=z), text, color=color, life_time=0.01)\n self._world.debug.draw_point(\n wp.transform.location + carla.Location(z=z), size=0.1, color=color, life_time=0.01)\n\n center_loc = carla.Location(center)\n\n if self._last_red_light_id and self._last_red_light_id == traffic_light.id:\n continue\n if center_loc.distance(location) > self.DISTANCE_LIGHT:\n continue\n if traffic_light.state != carla.TrafficLightState.Red:\n continue\n\n for wp in waypoints:\n\n tail_wp = self._map.get_waypoint(tail_far_pt)\n\n # Calculate the dot product (Might be unscaled, as only its sign is important)\n ve_dir = CarlaDataProvider.get_transform(self._actor).get_forward_vector()\n wp_dir = wp.transform.get_forward_vector()\n dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z\n\n # Check the lane until all the \"tail\" has passed\n if tail_wp.road_id == wp.road_id and tail_wp.lane_id == wp.lane_id and dot_ve_wp > 0:\n # This light is red and is affecting our lane\n yaw_wp = wp.transform.rotation.yaw\n lane_width = wp.lane_width\n location_wp = wp.transform.location\n\n lft_lane_wp = self.rotate_point(carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), yaw_wp + 90)\n lft_lane_wp = location_wp + carla.Location(lft_lane_wp)\n rgt_lane_wp = self.rotate_point(carla.Vector3D(0.4 * lane_width, 0.0, location_wp.z), yaw_wp - 90)\n rgt_lane_wp = location_wp + carla.Location(rgt_lane_wp)\n\n # Is the vehicle traversing the stop line?\n if self.is_vehicle_crossing_line((tail_close_pt, tail_far_pt), (lft_lane_wp, rgt_lane_wp)):\n\n self.test_status = \"FAILURE\"\n self.actual_value += 1\n location = traffic_light.get_transform().location\n red_light_event = TrafficEvent(event_type=TrafficEventType.TRAFFIC_LIGHT_INFRACTION)\n red_light_event.set_message(\n \"Agent ran a red light {} at (x={}, y={}, z={}, t={})\".format(\n traffic_light.id,\n round(location.x, 3),\n round(location.y, 3),\n round(location.z, 3),\n CarlaDataProvider.get_time_step()))\n red_light_event.set_dict({\n 'id': traffic_light.id,\n 'x': location.x,\n 'y': location.y,\n 'z': location.z})\n\n self.list_traffic_events.append(red_light_event)\n self._last_red_light_id = traffic_light.id\n break\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n def rotate_point(self, point, angle):\n \"\"\"\n rotate a given point by a given angle\n \"\"\"\n x_ = math.cos(math.radians(angle)) * point.x - math.sin(math.radians(angle)) * point.y\n y_ = math.sin(math.radians(angle)) * point.x + math.cos(math.radians(angle)) * point.y\n return carla.Vector3D(x_, y_, point.z)\n\n def get_traffic_light_waypoints(self, traffic_light):\n \"\"\"\n get area of a given traffic light\n \"\"\"\n base_transform = traffic_light.get_transform()\n base_rot = base_transform.rotation.yaw\n area_loc = base_transform.transform(traffic_light.trigger_volume.location)\n\n # Discretize the trigger box into points\n area_ext = traffic_light.trigger_volume.extent\n x_values = np.arange(-0.9 * area_ext.x, 0.9 * area_ext.x, 1.0) # 0.9 to avoid crossing to adjacent lanes\n\n area = []\n for x in x_values:\n point = self.rotate_point(carla.Vector3D(x, 0, area_ext.z), base_rot)\n point_location = area_loc + carla.Location(x=point.x, y=point.y)\n area.append(point_location)\n\n # Get the waypoints of these points, removing duplicates\n ini_wps = []\n for pt in area:\n wpx = self._map.get_waypoint(pt)\n # As x_values are arranged in order, only the last one has to be checked\n if not ini_wps or ini_wps[-1].road_id != wpx.road_id or ini_wps[-1].lane_id != wpx.lane_id:\n ini_wps.append(wpx)\n\n # Advance them until the intersection\n wps = []\n for wpx in ini_wps:\n while not wpx.is_intersection:\n next_wp = wpx.next(0.5)[0]\n if next_wp and not next_wp.is_intersection:\n wpx = next_wp\n else:\n break\n wps.append(wpx)\n\n return area_loc, wps\n\n\nclass RunningStopTest(Criterion):\n\n \"\"\"\n Check if an actor is running a stop sign\n\n Important parameters:\n - actor: CARLA actor to be used for this test\n - terminate_on_failure [optional]: If True, the complete scenario will terminate upon failure of this test\n \"\"\"\n PROXIMITY_THRESHOLD = 50.0 # meters\n SPEED_THRESHOLD = 0.1\n WAYPOINT_STEP = 1.0 # meters\n\n def __init__(self, actor, name=\"RunningStopTest\", terminate_on_failure=False):\n \"\"\"\n \"\"\"\n super(RunningStopTest, self).__init__(name, actor, 0, terminate_on_failure=terminate_on_failure)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._world = CarlaDataProvider.get_world()\n self._map = CarlaDataProvider.get_map()\n self._list_stop_signs = []\n self._target_stop_sign = None\n self._stop_completed = False\n self._affected_by_stop = False\n self.actual_value = 0\n\n all_actors = self._world.get_actors()\n for _actor in all_actors:\n if 'traffic.stop' in _actor.type_id:\n self._list_stop_signs.append(_actor)\n\n @staticmethod\n def point_inside_boundingbox(point, bb_center, bb_extent):\n \"\"\"\n X\n :param point:\n :param bb_center:\n :param bb_extent:\n :return:\n \"\"\"\n\n # pylint: disable=invalid-name\n A = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y - bb_extent.y)\n B = carla.Vector2D(bb_center.x + bb_extent.x, bb_center.y - bb_extent.y)\n D = carla.Vector2D(bb_center.x - bb_extent.x, bb_center.y + bb_extent.y)\n M = carla.Vector2D(point.x, point.y)\n\n AB = B - A\n AD = D - A\n AM = M - A\n am_ab = AM.x * AB.x + AM.y * AB.y\n ab_ab = AB.x * AB.x + AB.y * AB.y\n am_ad = AM.x * AD.x + AM.y * AD.y\n ad_ad = AD.x * AD.x + AD.y * AD.y\n\n return am_ab > 0 and am_ab < ab_ab and am_ad > 0 and am_ad < ad_ad\n\n def is_actor_affected_by_stop(self, actor, stop, multi_step=20):\n \"\"\"\n Check if the given actor is affected by the stop\n \"\"\"\n affected = False\n # first we run a fast coarse test\n current_location = actor.get_location()\n stop_location = stop.get_transform().location\n if stop_location.distance(current_location) > self.PROXIMITY_THRESHOLD:\n return affected\n\n stop_t = stop.get_transform()\n transformed_tv = stop_t.transform(stop.trigger_volume.location)\n\n # slower and accurate test based on waypoint's horizon and geometric test\n list_locations = [current_location]\n waypoint = self._map.get_waypoint(current_location)\n for _ in range(multi_step):\n if waypoint:\n next_wps = waypoint.next(self.WAYPOINT_STEP)\n if not next_wps:\n break\n waypoint = next_wps[0]\n if not waypoint:\n break\n list_locations.append(waypoint.transform.location)\n\n for actor_location in list_locations:\n if self.point_inside_boundingbox(actor_location, transformed_tv, stop.trigger_volume.extent):\n affected = True\n\n return affected\n\n def _scan_for_stop_sign(self):\n target_stop_sign = None\n\n ve_tra = CarlaDataProvider.get_transform(self._actor)\n ve_dir = ve_tra.get_forward_vector()\n\n wp = self._map.get_waypoint(ve_tra.location)\n wp_dir = wp.transform.get_forward_vector()\n\n dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z\n\n if dot_ve_wp > 0: # Ignore all when going in a wrong lane\n for stop_sign in self._list_stop_signs:\n if self.is_actor_affected_by_stop(self._actor, stop_sign):\n # this stop sign is affecting the vehicle\n target_stop_sign = stop_sign\n break\n\n return target_stop_sign\n\n def update(self):\n \"\"\"\n Check if the actor is running a red light\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = self._actor.get_location()\n if location is None:\n return new_status\n\n if not self._target_stop_sign:\n # scan for stop signs\n self._target_stop_sign = self._scan_for_stop_sign()\n else:\n # we were in the middle of dealing with a stop sign\n if not self._stop_completed:\n # did the ego-vehicle stop?\n current_speed = CarlaDataProvider.get_velocity(self._actor)\n if current_speed < self.SPEED_THRESHOLD:\n self._stop_completed = True\n\n if not self._affected_by_stop:\n stop_location = self._target_stop_sign.get_location()\n stop_extent = self._target_stop_sign.trigger_volume.extent\n\n if self.point_inside_boundingbox(location, stop_location, stop_extent):\n self._affected_by_stop = True\n\n if not self.is_actor_affected_by_stop(self._actor, self._target_stop_sign):\n # is the vehicle out of the influence of this stop sign now?\n if not self._stop_completed and self._affected_by_stop:\n # did we stop?\n self.actual_value += 1\n self.test_status = \"FAILURE\"\n stop_location = self._target_stop_sign.get_transform().location\n running_stop_event = TrafficEvent(event_type=TrafficEventType.STOP_INFRACTION)\n running_stop_event.set_message(\n \"Agent ran a stop with id={} at (x={}, y={}, z={}, t={})\".format(\n self._target_stop_sign.id,\n round(stop_location.x, 3),\n round(stop_location.y, 3),\n round(stop_location.z, 3),\n CarlaDataProvider.get_time_step()))\n running_stop_event.set_dict({\n 'id': self._target_stop_sign.id,\n 'x': stop_location.x,\n 'y': stop_location.y,\n 'z': stop_location.z})\n\n self.list_traffic_events.append(running_stop_event)\n\n # reset state\n self._target_stop_sign = None\n self._stop_completed = False\n self._affected_by_stop = False\n\n if self._terminate_on_failure and (self.test_status == \"FAILURE\"):\n new_status = py_trees.common.Status.FAILURE\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n" }, { "path": "scenario_runner/srunner/scenariomanager/scenarioatomics/atomic_trigger_conditions.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides all atomic scenario behaviors that reflect\ntrigger conditions to either activate another behavior, or to stop\nanother behavior.\n\nFor example, such a condition could be \"InTriggerRegion\", which checks\nthat a given actor reached a certain region on the map, and then starts/stops\na behavior of this actor.\n\nThe atomics are implemented with py_trees and make use of the AtomicCondition\nbase class\n\"\"\"\n\nfrom __future__ import print_function\n\nimport operator\nimport datetime\nimport math\nimport py_trees\nimport carla\n\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import calculate_distance\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.tools.scenario_helper import get_distance_along_route\n\nimport srunner.tools\n\nEPSILON = 0.001\n\n\nclass AtomicCondition(py_trees.behaviour.Behaviour):\n\n \"\"\"\n Base class for all atomic conditions used to setup a scenario\n\n *All behaviors should use this class as parent*\n\n Important parameters:\n - name: Name of the atomic condition\n \"\"\"\n\n def __init__(self, name):\n \"\"\"\n Default init. Has to be called via super from derived class\n \"\"\"\n super(AtomicCondition, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self.name = name\n\n def setup(self, unused_timeout=15):\n \"\"\"\n Default setup\n \"\"\"\n self.logger.debug(\"%s.setup()\" % (self.__class__.__name__))\n return True\n\n def initialise(self):\n \"\"\"\n Initialise setup\n \"\"\"\n self.logger.debug(\"%s.initialise()\" % (self.__class__.__name__))\n\n def terminate(self, new_status):\n \"\"\"\n Default terminate. Can be extended in derived class\n \"\"\"\n self.logger.debug(\"%s.terminate()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n\nclass InTriggerDistanceToOSCPosition(AtomicCondition):\n\n \"\"\"\n OpenSCENARIO atomic\n This class contains the trigger condition for a distance to an OpenSCENARIO position\n\n Args:\n actor (carla.Actor): CARLA actor to execute the behavior\n osc_position (str): OpenSCENARIO position\n distance (float): Trigger distance between the actor and the target location in meters\n name (str): Name of the condition\n\n The condition terminates with SUCCESS, when the actor reached the target distance to the openSCENARIO position\n \"\"\"\n\n def __init__(self, actor, osc_position, distance, along_route=False,\n comparison_operator=operator.lt, name=\"InTriggerDistanceToOSCPosition\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(InTriggerDistanceToOSCPosition, self).__init__(name)\n self._actor = actor\n self._osc_position = osc_position\n self._distance = distance\n self._along_route = along_route\n self._comparison_operator = comparison_operator\n self._map = CarlaDataProvider.get_map()\n\n if self._along_route:\n # Get the global route planner, used to calculate the route\n dao = GlobalRoutePlannerDAO(self._map, 0.5)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n self._grp = grp\n else:\n self._grp = None\n\n def initialise(self):\n if self._distance < 0:\n raise ValueError(\"distance value must be positive\")\n\n def update(self):\n \"\"\"\n Check if actor is in trigger distance\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n # calculate transform with method in openscenario_parser.py\n osc_transform = srunner.tools.openscenario_parser.OpenScenarioParser.convert_position_to_transform(\n self._osc_position)\n\n if osc_transform is not None:\n osc_location = osc_transform.location\n actor_location = CarlaDataProvider.get_location(self._actor)\n\n if self._along_route:\n # Global planner needs a location at a driving lane\n actor_location = self._map.get_waypoint(actor_location).transform.location\n osc_location = self._map.get_waypoint(osc_location).transform.location\n\n distance = calculate_distance(actor_location, osc_location, self._grp)\n\n if self._comparison_operator(distance, self._distance):\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass InTimeToArrivalToOSCPosition(AtomicCondition):\n\n \"\"\"\n OpenSCENARIO atomic\n This class contains a trigger if an actor arrives within a given time to an OpenSCENARIO position\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - osc_position: OpenSCENARIO position\n - time: The behavior is successful, if TTA is less than _time_ in seconds\n - name: Name of the condition\n\n The condition terminates with SUCCESS, when the actor can reach the position within the given time\n \"\"\"\n\n def __init__(self, actor, osc_position, time, along_route=False,\n comparison_operator=operator.lt, name=\"InTimeToArrivalToOSCPosition\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(InTimeToArrivalToOSCPosition, self).__init__(name)\n self._map = CarlaDataProvider.get_map()\n self._actor = actor\n self._osc_position = osc_position\n self._time = float(time)\n self._along_route = along_route\n self._comparison_operator = comparison_operator\n\n if self._along_route:\n # Get the global route planner, used to calculate the route\n dao = GlobalRoutePlannerDAO(self._map, 0.5)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n self._grp = grp\n else:\n self._grp = None\n\n def initialise(self):\n if self._time < 0:\n raise ValueError(\"time value must be positive\")\n\n def update(self):\n \"\"\"\n Check if actor can arrive within trigger time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n # calculate transform with method in openscenario_parser.py\n try:\n osc_transform = srunner.tools.openscenario_parser.OpenScenarioParser.convert_position_to_transform(\n self._osc_position)\n except AttributeError:\n return py_trees.common.Status.FAILURE\n target_location = osc_transform.location\n actor_location = CarlaDataProvider.get_location(self._actor)\n\n if target_location is None or actor_location is None:\n return new_status\n\n if self._along_route:\n # Global planner needs a location at a driving lane\n actor_location = self._map.get_waypoint(actor_location).transform.location\n target_location = self._map.get_waypoint(target_location).transform.location\n\n distance = calculate_distance(actor_location, target_location, self._grp)\n\n actor_velocity = CarlaDataProvider.get_velocity(self._actor)\n\n # time to arrival\n if actor_velocity > 0:\n time_to_arrival = distance / actor_velocity\n elif distance == 0:\n time_to_arrival = 0\n else:\n time_to_arrival = float('inf')\n\n if self._comparison_operator(time_to_arrival, self._time):\n new_status = py_trees.common.Status.SUCCESS\n return new_status\n\n\nclass StandStill(AtomicCondition):\n\n \"\"\"\n This class contains a standstill behavior of a scenario\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - duration: Duration of the behavior in seconds\n\n The condition terminates with SUCCESS, when the actor does not move\n \"\"\"\n\n def __init__(self, actor, name, duration=float(\"inf\")):\n \"\"\"\n Setup actor\n \"\"\"\n super(StandStill, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n\n self._duration = duration\n self._start_time = 0\n\n def initialise(self):\n \"\"\"\n Initialize the start time of this condition\n \"\"\"\n self._start_time = GameTime.get_time()\n super(StandStill, self).initialise()\n\n def update(self):\n \"\"\"\n Check if the _actor stands still (v=0)\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n velocity = CarlaDataProvider.get_velocity(self._actor)\n\n if velocity > EPSILON:\n self._start_time = GameTime.get_time()\n\n if GameTime.get_time() - self._start_time > self._duration:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass RelativeVelocityToOtherActor(AtomicCondition):\n\n \"\"\"\n Atomic containing a comparison between an actor's velocity\n and another actor's one. The behavior returns SUCCESS when the\n expected comparison (greater than / less than / equal to) is achieved\n\n Args:\n actor (carla.Actor): actor from which the velocity is taken\n other_actor (carla.Actor): The actor with the reference velocity\n speed (float): Difference of speed between the actors\n name (string): Name of the condition\n comparison_operator: Type \"operator\", used to compare the two velocities\n \"\"\"\n\n def __init__(self, actor, other_actor, speed, comparison_operator=operator.gt,\n name=\"RelativeVelocityToOtherActor\"):\n \"\"\"\n Setup the parameters\n \"\"\"\n super(RelativeVelocityToOtherActor, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._other_actor = other_actor\n self._relative_speed = speed\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Gets the speed of the two actors and compares them according to the comparison operator\n\n returns:\n py_trees.common.Status.RUNNING when the comparison fails and\n py_trees.common.Status.SUCCESS when it succeeds\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n curr_speed = CarlaDataProvider.get_velocity(self._actor)\n other_speed = CarlaDataProvider.get_velocity(self._other_actor)\n\n relative_speed = curr_speed - other_speed\n\n if self._comparison_operator(relative_speed, self._relative_speed):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass TriggerVelocity(AtomicCondition):\n\n \"\"\"\n Atomic containing a comparison between an actor's speed and a reference one.\n The behavior returns SUCCESS when the expected comparison (greater than /\n less than / equal to) is achieved.\n\n Args:\n actor (carla.Actor): CARLA actor from which the speed will be taken.\n name (string): Name of the atomic\n target_velocity (float): velcoity to be compared with the actor's one\n comparison_operator: Type \"operator\", used to compare the two velocities\n \"\"\"\n\n def __init__(self, actor, target_velocity, comparison_operator=operator.gt, name=\"TriggerVelocity\"):\n \"\"\"\n Setup the atomic parameters\n \"\"\"\n super(TriggerVelocity, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._target_velocity = target_velocity\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Gets the speed of the actor and compares it with the reference one\n\n returns:\n py_trees.common.Status.RUNNING when the comparison fails and\n py_trees.common.Status.SUCCESS when it succeeds\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n actor_speed = CarlaDataProvider.get_velocity(self._actor)\n\n if self._comparison_operator(actor_speed, self._target_velocity):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass TriggerAcceleration(AtomicCondition):\n\n \"\"\"\n Atomic containing a comparison between an actor's acceleration\n and a reference one. The behavior returns SUCCESS when the\n expected comparison (greater than / less than / equal to) is achieved\n\n Args:\n actor (carla.Actor): CARLA actor to execute the behavior\n name (str): Name of the condition\n target_acceleration (float): Acceleration reference (in m/s^2) on which the success is dependent\n comparison_operator (operator): Type \"operator\", used to compare the two acceleration\n \"\"\"\n\n def __init__(self, actor, target_acceleration, comparison_operator=operator.gt, name=\"TriggerAcceleration\"):\n \"\"\"\n Setup trigger acceleration\n \"\"\"\n super(TriggerAcceleration, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._target_acceleration = target_acceleration\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Gets the accleration of the actor and compares it with the reference one\n\n returns:\n py_trees.common.Status.RUNNING when the comparison fails and\n py_trees.common.Status.SUCCESS when it succeeds\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n acceleration = self._actor.get_acceleration()\n linear_accel = math.sqrt(math.pow(acceleration.x, 2) +\n math.pow(acceleration.y, 2) +\n math.pow(acceleration.z, 2))\n\n if self._comparison_operator(linear_accel, self._target_acceleration):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass TimeOfDayComparison(AtomicCondition):\n\n \"\"\"\n Atomic containing a comparison between the current time of day of the simulation\n and a given one. The behavior returns SUCCESS when the\n expected comparison (greater than / less than / equal to) is achieved\n\n Args:\n datetime (datetime): CARLA actor to execute the behavior\n name (str): Name of the condition\n target_acceleration (float): Acceleration reference (in m/s^2) on which the success is dependent\n comparison_operator (operator): Type \"operator\", used to compare the two acceleration\n \"\"\"\n\n def __init__(self, dattime, comparison_operator=operator.gt, name=\"TimeOfDayComparison\"):\n \"\"\"\n \"\"\"\n super(TimeOfDayComparison, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._datetime = datetime.datetime.strptime(dattime, \"%Y-%m-%dT%H:%M:%S\")\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Gets the time of day of the simulation and compares it with the reference one\n\n returns:\n py_trees.common.Status.RUNNING when the comparison fails and\n py_trees.common.Status.SUCCESS when it succeeds\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n try:\n check_dtime = operator.attrgetter(\"Datetime\")\n dtime = check_dtime(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if self._comparison_operator(dtime, self._datetime):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass OSCStartEndCondition(AtomicCondition):\n\n \"\"\"\n This class contains a check if a named story element has started/terminated.\n\n Important parameters:\n - element_name: The story element's name attribute\n - element_type: The element type [act,scene,maneuver,event,action]\n - rule: Either START or END\n\n The condition terminates with SUCCESS, when the named story element starts\n \"\"\"\n\n def __init__(self, element_type, element_name, rule, name=\"OSCStartEndCondition\"):\n \"\"\"\n Setup element details\n \"\"\"\n super(OSCStartEndCondition, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._element_type = element_type.upper()\n self._element_name = element_name\n self._rule = rule.upper()\n self._start_time = None\n self._blackboard = py_trees.blackboard.Blackboard()\n\n def initialise(self):\n \"\"\"\n Initialize the start time of this condition\n \"\"\"\n self._start_time = GameTime.get_time()\n super(OSCStartEndCondition, self).initialise()\n\n def update(self):\n \"\"\"\n Check if the specified story element has started/ended since the beginning of the condition\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n if new_status == py_trees.common.Status.RUNNING:\n blackboard_variable_name = \"({}){}-{}\".format(self._element_type, self._element_name, self._rule)\n element_start_time = self._blackboard.get(blackboard_variable_name)\n if element_start_time and element_start_time >= self._start_time:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTriggerRegion(AtomicCondition):\n\n \"\"\"\n This class contains the trigger region (condition) of a scenario\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - min_x, max_x, min_y, max_y: bounding box of the trigger region\n\n The condition terminates with SUCCESS, when the actor reached the target region\n \"\"\"\n\n def __init__(self, actor, min_x, max_x, min_y, max_y, name=\"TriggerRegion\"):\n \"\"\"\n Setup trigger region (rectangle provided by\n [min_x,min_y] and [max_x,max_y]\n \"\"\"\n super(InTriggerRegion, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._min_x = min_x\n self._max_x = max_x\n self._min_y = min_y\n self._max_y = max_y\n\n def update(self):\n \"\"\"\n Check if the _actor location is within trigger region\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n\n if location is None:\n return new_status\n\n not_in_region = (location.x < self._min_x or location.x > self._max_x) or (\n location.y < self._min_y or location.y > self._max_y)\n if not not_in_region:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTriggerDistanceToVehicle(AtomicCondition):\n\n \"\"\"\n This class contains the trigger distance (condition) between to actors\n of a scenario\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - reference_actor: Reference CARLA actor\n - name: Name of the condition\n - distance: Trigger distance between the two actors in meters\n - dx, dy, dz: distance to reference_location (location of reference_actor)\n\n The condition terminates with SUCCESS, when the actor reached the target distance to the other actor\n \"\"\"\n\n def __init__(self, reference_actor, actor, distance, comparison_operator=operator.lt,\n name=\"TriggerDistanceToVehicle\"):\n \"\"\"\n Setup trigger distance\n \"\"\"\n super(InTriggerDistanceToVehicle, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._reference_actor = reference_actor\n self._actor = actor\n self._distance = distance\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Check if the ego vehicle is within trigger distance to other actor\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n reference_location = CarlaDataProvider.get_location(self._reference_actor)\n\n if location is None or reference_location is None:\n return new_status\n\n if self._comparison_operator(calculate_distance(location, reference_location), self._distance):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTriggerDistanceToLocation(AtomicCondition):\n\n \"\"\"\n This class contains the trigger (condition) for a distance to a fixed\n location of a scenario\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - target_location: Reference location (carla.location)\n - name: Name of the condition\n - distance: Trigger distance between the actor and the target location in meters\n\n The condition terminates with SUCCESS, when the actor reached the target distance to the given location\n \"\"\"\n\n def __init__(self,\n actor,\n target_location,\n distance,\n comparison_operator=operator.lt,\n name=\"InTriggerDistanceToLocation\"):\n \"\"\"\n Setup trigger distance\n \"\"\"\n super(InTriggerDistanceToLocation, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._target_location = target_location\n self._actor = actor\n self._distance = distance\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Check if the actor is within trigger distance to the target location\n \"\"\"\n\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n\n if location is None:\n return new_status\n\n if self._comparison_operator(calculate_distance(\n location, self._target_location), self._distance):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTriggerDistanceToNextIntersection(AtomicCondition):\n\n \"\"\"\n This class contains the trigger (condition) for a distance to the\n next intersection of a scenario\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - distance: Trigger distance between the actor and the next intersection in meters\n\n The condition terminates with SUCCESS, when the actor reached the target distance to the next intersection\n \"\"\"\n\n def __init__(self, actor, distance, name=\"InTriggerDistanceToNextIntersection\"):\n \"\"\"\n Setup trigger distance\n \"\"\"\n super(InTriggerDistanceToNextIntersection, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._distance = distance\n self._map = CarlaDataProvider.get_map()\n\n waypoint = self._map.get_waypoint(self._actor.get_location())\n while waypoint and not waypoint.is_intersection:\n waypoint = waypoint.next(1)[-1]\n\n self._final_location = waypoint.transform.location\n\n def update(self):\n \"\"\"\n Check if the actor is within trigger distance to the intersection\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n current_waypoint = self._map.get_waypoint(CarlaDataProvider.get_location(self._actor))\n distance = calculate_distance(current_waypoint.transform.location, self._final_location)\n\n if distance < self._distance:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTriggerDistanceToLocationAlongRoute(AtomicCondition):\n\n \"\"\"\n Implementation for a behavior that will check if a given actor\n is within a given distance to a given location considering a given route\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - distance: Trigger distance between the actor and the next intersection in meters\n - route: Route to be checked\n - location: Location on the route to be checked\n\n The condition terminates with SUCCESS, when the actor reached the target distance\n along its route to the given location\n \"\"\"\n\n def __init__(self, actor, route, location, distance, name=\"InTriggerDistanceToLocationAlongRoute\"):\n \"\"\"\n Setup class members\n \"\"\"\n super(InTriggerDistanceToLocationAlongRoute, self).__init__(name)\n self._map = CarlaDataProvider.get_map()\n self._actor = actor\n self._location = location\n self._route = route\n self._distance = distance\n\n self._location_distance, _ = get_distance_along_route(self._route, self._location)\n\n def update(self):\n new_status = py_trees.common.Status.RUNNING\n\n current_location = CarlaDataProvider.get_location(self._actor)\n\n if current_location is None:\n return new_status\n\n if current_location.distance(self._location) < self._distance + 20:\n\n actor_distance, _ = get_distance_along_route(self._route, current_location)\n\n # If closer than self._distance and hasn't passed the trigger point\n if (self._location_distance < actor_distance + self._distance and\n actor_distance < self._location_distance) or \\\n self._location_distance < 1.0:\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass InTimeToArrivalToLocation(AtomicCondition):\n\n \"\"\"\n This class contains a check if a actor arrives within a given time\n at a given location.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - time: The behavior is successful, if TTA is less than _time_ in seconds\n - location: Location to be checked in this behavior\n\n The condition terminates with SUCCESS, when the actor can reach the target location within the given time\n \"\"\"\n\n _max_time_to_arrival = float('inf') # time to arrival in seconds\n\n def __init__(self, actor, time, location, comparison_operator=operator.lt, name=\"TimeToArrival\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(InTimeToArrivalToLocation, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._time = time\n self._target_location = location\n self._comparison_operator = comparison_operator\n\n def update(self):\n \"\"\"\n Check if the actor can arrive at target_location within time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n current_location = CarlaDataProvider.get_location(self._actor)\n\n if current_location is None:\n return new_status\n\n distance = calculate_distance(current_location, self._target_location)\n velocity = CarlaDataProvider.get_velocity(self._actor)\n\n # if velocity is too small, simply use a large time to arrival\n time_to_arrival = self._max_time_to_arrival\n if velocity > EPSILON:\n time_to_arrival = distance / velocity\n\n if self._comparison_operator(time_to_arrival, self._time):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTimeToArrivalToVehicle(AtomicCondition):\n\n \"\"\"\n This class contains a check if a actor arrives within a given time\n at another actor.\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - time: The behavior is successful, if TTA is less than _time_ in seconds\n - other_actor: Reference actor used in this behavior\n\n The condition terminates with SUCCESS, when the actor can reach the other vehicle within the given time\n \"\"\"\n\n _max_time_to_arrival = float('inf') # time to arrival in seconds\n\n def __init__(self, actor, other_actor, time, along_route=False,\n comparison_operator=operator.lt, name=\"TimeToArrival\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(InTimeToArrivalToVehicle, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._map = CarlaDataProvider.get_map()\n self._actor = actor\n self._other_actor = other_actor\n self._time = time\n self._along_route = along_route\n self._comparison_operator = comparison_operator\n\n if self._along_route:\n # Get the global route planner, used to calculate the route\n dao = GlobalRoutePlannerDAO(self._map, 0.5)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n self._grp = grp\n else:\n self._grp = None\n\n def update(self):\n \"\"\"\n Check if the ego vehicle can arrive at other actor within time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n current_location = CarlaDataProvider.get_location(self._actor)\n other_location = CarlaDataProvider.get_location(self._other_actor)\n\n if current_location is None or other_location is None:\n return new_status\n\n current_velocity = CarlaDataProvider.get_velocity(self._actor)\n other_velocity = CarlaDataProvider.get_velocity(self._other_actor)\n\n if self._along_route:\n # Global planner needs a location at a driving lane\n current_location = self._map.get_waypoint(current_location).transform.location\n other_location = self._map.get_waypoint(other_location).transform.location\n\n distance = calculate_distance(current_location, other_location, self._grp)\n\n # if velocity is too small, simply use a large time to arrival\n time_to_arrival = self._max_time_to_arrival\n\n if current_velocity > other_velocity:\n time_to_arrival = 2 * distance / (current_velocity - other_velocity)\n\n if self._comparison_operator(time_to_arrival, self._time):\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass InTimeToArrivalToVehicleSideLane(InTimeToArrivalToLocation):\n\n \"\"\"\n This class contains a check if a actor arrives within a given time\n at another actor's side lane. Inherits from InTimeToArrivalToLocation\n\n Important parameters:\n - actor: CARLA actor to execute the behavior\n - name: Name of the condition\n - time: The behavior is successful, if TTA is less than _time_ in seconds\n - cut_in_lane: the lane from where the other_actor will do the cut in\n - other_actor: Reference actor used in this behavior\n\n The condition terminates with SUCCESS, when the actor can reach the other vehicle within the given time\n \"\"\"\n\n _max_time_to_arrival = float('inf') # time to arrival in seconds\n\n def __init__(self, actor, other_actor, time, side_lane,\n comparison_operator=operator.lt, name=\"InTimeToArrivalToVehicleSideLane\"):\n \"\"\"\n Setup parameters\n \"\"\"\n self._other_actor = other_actor\n self._side_lane = side_lane\n\n self._world = CarlaDataProvider.get_world()\n self._map = CarlaDataProvider.get_map(self._world)\n\n other_location = other_actor.get_transform().location\n other_waypoint = self._map.get_waypoint(other_location)\n\n if self._side_lane == 'right':\n other_side_waypoint = other_waypoint.get_left_lane()\n elif self._side_lane == 'left':\n other_side_waypoint = other_waypoint.get_right_lane()\n else:\n raise Exception(\"cut_in_lane must be either 'left' or 'right'\")\n\n other_side_location = other_side_waypoint.transform.location\n\n super(InTimeToArrivalToVehicleSideLane, self).__init__(\n actor, time, other_side_location, comparison_operator, name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n \"\"\"\n Check if the ego vehicle can arrive at other actor within time\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n other_location = CarlaDataProvider.get_location(self._other_actor)\n other_waypoint = self._map.get_waypoint(other_location)\n\n if self._side_lane == 'right':\n other_side_waypoint = other_waypoint.get_left_lane()\n elif self._side_lane == 'left':\n other_side_waypoint = other_waypoint.get_right_lane()\n else:\n raise Exception(\"cut_in_lane must be either 'left' or 'right'\")\n if other_side_waypoint is None:\n return new_status\n\n self._target_location = other_side_waypoint.transform.location\n\n if self._target_location is None:\n return new_status\n\n new_status = super(InTimeToArrivalToVehicleSideLane, self).update()\n\n return new_status\n\n\nclass WaitUntilInFront(AtomicCondition):\n\n \"\"\"\n Behavior that support the creation of cut ins. It waits until the actor has passed another actor\n\n Parameters:\n - actor: the one getting in front of the other actor\n - other_actor: the reference vehicle that the actor will have to get in front of\n - factor: How much in front the actor will have to get (from 0 to infinity):\n 0: They are right next to each other\n 1: The front of the other_actor and the back of the actor are right next to each other\n \"\"\"\n\n def __init__(self, actor, other_actor, factor=1, check_distance=True, name=\"WaitUntilInFront\"):\n \"\"\"\n Init\n \"\"\"\n super(WaitUntilInFront, self).__init__(name)\n self._actor = actor\n self._other_actor = other_actor\n self._distance = 10\n self._factor = max(EPSILON, factor) # Must be > 0\n self._check_distance = check_distance\n\n self._world = CarlaDataProvider.get_world()\n self._map = CarlaDataProvider.get_map(self._world)\n\n actor_extent = self._actor.bounding_box.extent.x\n other_extent = self._other_actor.bounding_box.extent.x\n self._length = self._factor * (actor_extent + other_extent)\n\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n \"\"\"\n Checks if the two actors meet the requirements\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n in_front = False\n close_by = False\n\n # Location of our actor\n actor_location = CarlaDataProvider.get_location(self._actor)\n if actor_location is None:\n return new_status\n\n # Waypoint in front of the other actor\n other_location = CarlaDataProvider.get_location(self._other_actor)\n other_waypoint = self._map.get_waypoint(other_location)\n if other_waypoint is None:\n return new_status\n other_next_waypoints = other_waypoint.next(self._length)\n if other_next_waypoints is None:\n return new_status\n other_next_waypoint = other_next_waypoints[0]\n\n # Wait for the vehicle to be in front\n other_dir = other_next_waypoint.transform.get_forward_vector()\n act_other_dir = actor_location - other_next_waypoint.transform.location\n dot_ve_wp = other_dir.x * act_other_dir.x + other_dir.y * act_other_dir.y + other_dir.z * act_other_dir.z\n\n if dot_ve_wp > 0.0:\n in_front = True\n\n # Wait for it to be close-by\n if not self._check_distance:\n close_by = True\n elif actor_location.distance(other_next_waypoint.transform.location) < self._distance:\n close_by = True\n\n if in_front and close_by:\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass DriveDistance(AtomicCondition):\n\n \"\"\"\n This class contains an atomic behavior to drive a certain distance.\n\n Important parameters:\n - actor: CARLA actor to execute the condition\n - distance: Distance for this condition in meters\n\n The condition terminates with SUCCESS, when the actor drove at least the given distance\n \"\"\"\n\n def __init__(self, actor, distance, name=\"DriveDistance\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(DriveDistance, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._target_distance = distance\n self._distance = 0\n self._location = None\n self._actor = actor\n\n def initialise(self):\n self._location = CarlaDataProvider.get_location(self._actor)\n super(DriveDistance, self).initialise()\n\n def update(self):\n \"\"\"\n Check driven distance\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n new_location = CarlaDataProvider.get_location(self._actor)\n self._distance += calculate_distance(self._location, new_location)\n self._location = new_location\n\n if self._distance > self._target_distance:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n\nclass AtRightmostLane(AtomicCondition):\n\n \"\"\"\n This class contains an atomic behavior to check if the actor is at the rightest driving lane.\n\n Important parameters:\n - actor: CARLA actor to execute the condition\n\n The condition terminates with SUCCESS, when the actor enters the rightest lane\n \"\"\"\n\n def __init__(self, actor, name=\"AtRightmostLane\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(AtRightmostLane, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor\n self._map = CarlaDataProvider.get_map()\n\n def update(self):\n \"\"\"\n Check actor waypoints\n \"\"\"\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self._actor)\n waypoint = self._map.get_waypoint(location)\n if waypoint is None:\n return new_status\n right_waypoint = waypoint.get_right_lane()\n if right_waypoint is None:\n return new_status\n lane_type = right_waypoint.lane_type\n\n if lane_type != carla.LaneType.Driving:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n return new_status\n\n\nclass WaitForTrafficLightState(AtomicCondition):\n\n \"\"\"\n This class contains an atomic behavior to wait for a given traffic light\n to have the desired state.\n\n Args:\n actor (carla.TrafficLight): CARLA traffic light to execute the condition\n state (carla.TrafficLightState): State to be checked in this condition\n\n The condition terminates with SUCCESS, when the traffic light switches to the desired state\n \"\"\"\n\n def __init__(self, actor, state, name=\"WaitForTrafficLightState\"):\n \"\"\"\n Setup traffic_light\n \"\"\"\n super(WaitForTrafficLightState, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._actor = actor if \"traffic_light\" in actor.type_id else None\n self._state = state\n\n def update(self):\n \"\"\"\n Set status to SUCCESS, when traffic light state matches the expected one\n \"\"\"\n if self._actor is None:\n return py_trees.common.Status.FAILURE\n\n new_status = py_trees.common.Status.RUNNING\n\n if self._actor.state == self._state:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass WaitEndIntersection(AtomicCondition):\n\n \"\"\"\n Atomic behavior that waits until the vehicles has gone outside the junction.\n If currently inside no intersection, it will wait until one is found\n \"\"\"\n\n def __init__(self, actor, debug=False, name=\"WaitEndIntersection\"):\n super(WaitEndIntersection, self).__init__(name)\n self.actor = actor\n self.debug = debug\n self.inside_junction = False\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n def update(self):\n\n new_status = py_trees.common.Status.RUNNING\n\n location = CarlaDataProvider.get_location(self.actor)\n waypoint = CarlaDataProvider.get_map().get_waypoint(location)\n\n # Wait for the actor to enter a junction\n if not self.inside_junction and waypoint.is_junction:\n self.inside_junction = True\n\n # And to leave it\n if self.inside_junction and not waypoint.is_junction:\n if self.debug:\n print(\"--- Leaving the junction\")\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n\n\nclass WaitForBlackboardVariable(AtomicCondition):\n\n \"\"\"\n Atomic behavior that keeps running until the blackboard variable is set to the corresponding value.\n Used to avoid returning FAILURE if the blackboard comparison fails.\n\n It also initially sets the variable to a given value, if given\n \"\"\"\n\n def __init__(self, variable_name, variable_value, var_init_value=None,\n debug=False, name=\"WaitForBlackboardVariable\"):\n super(WaitForBlackboardVariable, self).__init__(name)\n self._debug = debug\n self._variable_name = variable_name\n self._variable_value = variable_value\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n\n if var_init_value is not None:\n blackboard = py_trees.blackboard.Blackboard()\n _ = blackboard.set(variable_name, var_init_value)\n\n def update(self):\n\n new_status = py_trees.common.Status.RUNNING\n\n blackv = py_trees.blackboard.Blackboard()\n value = blackv.get(self._variable_name)\n if value == self._variable_value:\n if self._debug:\n print(\"Blackboard variable {} set to True\".format(self._variable_name))\n new_status = py_trees.common.Status.SUCCESS\n\n return new_status\n" }, { "path": "scenario_runner/srunner/scenariomanager/timer.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides access to the CARLA game time and contains a py_trees\ntimeout behavior using the CARLA game time\n\"\"\"\n\nimport datetime\nimport py_trees\n\n\nclass GameTime(object):\n\n \"\"\"\n This (static) class provides access to the CARLA game time.\n\n The elapsed game time can be simply retrieved by calling:\n GameTime.get_time()\n \"\"\"\n\n _current_game_time = 0.0 # Elapsed game time after starting this Timer\n _carla_time = 0.0\n _last_frame = 0\n _platform_timestamp = 0\n _init = False\n\n @staticmethod\n def on_carla_tick(timestamp):\n \"\"\"\n Callback receiving the CARLA time\n Update time only when frame is more recent that last frame\n \"\"\"\n if GameTime._last_frame < timestamp.frame:\n frames = timestamp.frame - GameTime._last_frame if GameTime._init else 1\n GameTime._current_game_time += timestamp.delta_seconds * frames\n GameTime._last_frame = timestamp.frame\n GameTime._platform_timestamp = datetime.datetime.now()\n GameTime._init = True\n GameTime._carla_time = timestamp.elapsed_seconds\n\n @staticmethod\n def restart():\n \"\"\"\n Reset game timer to 0\n \"\"\"\n GameTime._current_game_time = 0.0\n GameTime._init = False\n\n @staticmethod\n def get_time():\n \"\"\"\n Returns elapsed game time\n \"\"\"\n return GameTime._current_game_time\n\n @staticmethod\n def get_carla_time():\n \"\"\"\n Returns elapsed game time\n \"\"\"\n return GameTime._carla_time\n\n @staticmethod\n def get_wallclocktime():\n \"\"\"\n Returns elapsed game time\n \"\"\"\n return GameTime._platform_timestamp\n\n @staticmethod\n def get_frame():\n \"\"\"\n Returns elapsed game time\n \"\"\"\n return GameTime._last_frame\n\n\nclass SimulationTimeCondition(py_trees.behaviour.Behaviour):\n\n \"\"\"\n This class contains an atomic simulation time condition behavior.\n It uses the CARLA game time, not the system time which is used by\n the py_trees timer.\n\n Returns, if the provided success_rule (greaterThan, lessThan, equalTo)\n was successfully evaluated\n \"\"\"\n\n def __init__(self, timeout, success_rule=\"greaterThan\", name=\"SimulationTimeCondition\"):\n \"\"\"\n Setup timeout\n \"\"\"\n super(SimulationTimeCondition, self).__init__(name)\n self.logger.debug(\"%s.__init__()\" % (self.__class__.__name__))\n self._timeout_value = timeout\n self._start_time = 0.0\n self._success_rule = success_rule\n self._ops = {\"greaterThan\": (lambda x, y: x > y),\n \"equalTo\": (lambda x, y: x == y),\n \"lessThan\": (lambda x, y: x < y)}\n\n def initialise(self):\n \"\"\"\n Set start_time to current GameTime\n \"\"\"\n self._start_time = GameTime.get_time()\n self.logger.debug(\"%s.initialise()\" % (self.__class__.__name__))\n\n def update(self):\n \"\"\"\n Get current game time, and compare it to the timeout value\n Upon successfully comparison using the provided success_rule operator,\n the status changes to SUCCESS\n \"\"\"\n\n elapsed_time = GameTime.get_time() - self._start_time\n\n if not self._ops[self._success_rule](elapsed_time, self._timeout_value):\n new_status = py_trees.common.Status.RUNNING\n else:\n new_status = py_trees.common.Status.SUCCESS\n\n self.logger.debug(\"%s.update()[%s->%s]\" % (self.__class__.__name__, self.status, new_status))\n\n return new_status\n\n\nclass TimeOut(SimulationTimeCondition):\n\n \"\"\"\n This class contains an atomic timeout behavior.\n It uses the CARLA game time, not the system time which is used by\n the py_trees timer.\n \"\"\"\n\n def __init__(self, timeout, name=\"TimeOut\"):\n \"\"\"\n Setup timeout\n \"\"\"\n super(TimeOut, self).__init__(timeout, name=name)\n self.timeout = False\n\n def update(self):\n \"\"\"\n Upon reaching the timeout value the status changes to SUCCESS\n \"\"\"\n\n new_status = super(TimeOut, self).update()\n\n if new_status == py_trees.common.Status.SUCCESS:\n self.timeout = True\n\n return new_status\n" }, { "path": "scenario_runner/srunner/scenariomanager/traffic_events.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nCollection of TrafficEvents\n\"\"\"\n\nfrom enum import Enum\n\n\nclass TrafficEventType(Enum):\n\n \"\"\"\n This enum represents different traffic events that occur during driving.\n \"\"\"\n\n NORMAL_DRIVING = 0\n COLLISION_STATIC = 1\n COLLISION_VEHICLE = 2\n COLLISION_PEDESTRIAN = 3\n ROUTE_DEVIATION = 4\n ROUTE_COMPLETION = 5\n ROUTE_COMPLETED = 6\n TRAFFIC_LIGHT_INFRACTION = 7\n WRONG_WAY_INFRACTION = 8\n ON_SIDEWALK_INFRACTION = 9\n STOP_INFRACTION = 10\n OUTSIDE_LANE_INFRACTION = 11\n OUTSIDE_ROUTE_LANES_INFRACTION = 12\n VEHICLE_BLOCKED = 13\n\n\nclass TrafficEvent(object):\n\n \"\"\"\n TrafficEvent definition\n \"\"\"\n\n def __init__(self, event_type, message=None, dictionary=None):\n \"\"\"\n Initialize object\n\n :param event_type: TrafficEventType defining the type of traffic event\n :param message: optional message to inform users of the event\n :param dictionary: optional dictionary with arbitrary keys and values\n \"\"\"\n self._type = event_type\n self._message = message\n self._dict = dictionary\n\n def get_type(self):\n \"\"\"\n @return type\n \"\"\"\n return self._type\n\n def get_message(self):\n \"\"\"\n @return message\n \"\"\"\n if self._message:\n return self._message\n\n return \"\"\n\n def set_message(self, message):\n \"\"\"\n Set message\n \"\"\"\n self._message = message\n\n def get_dict(self):\n \"\"\"\n @return dictionary\n \"\"\"\n return self._dict\n\n def set_dict(self, dictionary):\n \"\"\"\n Set dictionary\n \"\"\"\n self._dict = dictionary\n" }, { "path": "scenario_runner/srunner/scenariomanager/watchdog.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a simple watchdog timer to detect timeouts\nIt is for example used in the ScenarioManager\n\"\"\"\nfrom __future__ import print_function\n\nfrom threading import Timer\ntry:\n import thread\nexcept ImportError:\n import _thread as thread\n\n\nclass Watchdog(object):\n\n \"\"\"\n Simple watchdog timer to detect timeouts\n\n Args:\n timeout (float): Timeout value of the watchdog [seconds].\n If it is not reset before exceeding this value, a KayboardInterrupt is raised.\n\n Attributes:\n _timeout (float): Timeout value of the watchdog [seconds].\n _failed (bool): True if watchdog exception occured, false otherwise\n \"\"\"\n\n def __init__(self, timeout=1.0):\n \"\"\"\n Class constructor\n \"\"\"\n self._timeout = timeout + 1.0 # Let's add one second here to avoid overlap with other CARLA timeouts\n self._failed = False\n self._timer = None\n\n def start(self):\n \"\"\"\n Start the watchdog\n \"\"\"\n self._timer = Timer(self._timeout, self._event)\n self._timer.daemon = True\n self._timer.start()\n\n def update(self):\n \"\"\"\n Reset watchdog.\n \"\"\"\n self.stop()\n self.start()\n\n def _event(self):\n \"\"\"\n This method is called when the timer triggers. A KayboardInterrupt\n is generated on the main thread and the watchdog is stopped.\n \"\"\"\n print('Watchdog exception - Timeout of {} seconds occured'.format(self._timeout))\n self._failed = True\n self.stop()\n thread.interrupt_main()\n\n def stop(self):\n \"\"\"\n Stops the watchdog.\n \"\"\"\n self._timer.cancel()\n\n def get_status(self):\n \"\"\"\n returns:\n bool: False if watchdog exception occured, True otherwise\n \"\"\"\n return not self._failed\n" }, { "path": "scenario_runner/srunner/scenariomanager/weather_sim.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a weather class and py_trees behavior\nto simulate weather in CARLA according to the astronomic\nbehavior of the sun.\n\"\"\"\n\nimport datetime\nimport math\nimport operator\n\nimport ephem\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import GameTime\n\n\nclass Weather(object):\n\n \"\"\"\n Class to simulate weather in CARLA according to the astronomic behavior of the sun\n\n The sun position (azimuth and altitude angles) is obtained by calculating its\n astronomic position for the CARLA reference position (x=0, y=0, z=0) using the ephem\n library.\n\n Args:\n carla_weather (carla.WeatherParameters): Initial weather settings.\n dtime (datetime): Initial date and time in UTC (required for animation only).\n Defaults to None.\n animation (bool): Flag to allow animating the sun position over time.\n Defaults to False.\n\n Attributes:\n carla_weather (carla.WeatherParameters): Weather parameters for CARLA.\n animation (bool): Flag to allow animating the sun position over time.\n _sun (ephem.Sun): The sun as astronomic entity.\n _observer_location (ephem.Observer): Holds the geographical position (lat/lon/altitude)\n for which the sun position is obtained.\n datetime (datetime): Date and time in UTC (required for animation only).\n \"\"\"\n\n def __init__(self, carla_weather, dtime=None, animation=False):\n \"\"\"\n Class constructor\n \"\"\"\n self.carla_weather = carla_weather\n self.animation = animation\n\n self._sun = ephem.Sun() # pylint: disable=no-member\n self._observer_location = ephem.Observer()\n geo_location = CarlaDataProvider.get_map().transform_to_geolocation(carla.Location(0, 0, 0))\n self._observer_location.lon = str(geo_location.longitude)\n self._observer_location.lat = str(geo_location.latitude)\n \n self.datetime = dtime\n if self.datetime:\n self._observer_location.date = self.datetime\n\n self.update()\n\n def update(self, delta_time=0):\n \"\"\"\n If the weather animation is true, the new sun position is calculated w.r.t delta_time\n\n Nothing happens if animation or datetime are None.\n\n Args:\n delta_time (float): Time passed since self.datetime [seconds].\n \"\"\"\n if not self.animation or not self.datetime:\n return\n\n self.datetime = self.datetime + datetime.timedelta(seconds=delta_time)\n self._observer_location.date = self.datetime\n\n self._sun.compute(self._observer_location)\n self.carla_weather.sun_altitude_angle = math.degrees(self._sun.alt)\n self.carla_weather.sun_azimuth_angle = math.degrees(self._sun.az)\n\n\nclass WeatherBehavior(py_trees.behaviour.Behaviour):\n\n \"\"\"\n Atomic to read weather settings from the blackboard and apply these in CARLA.\n Used in combination with UpdateWeather() to have a continuous weather simulation.\n\n This behavior is always in a running state and must never terminate.\n The user must not add this behavior. It is automatically added by the ScenarioManager.\n\n This atomic also sets the datetime to blackboard variable, used by TimeOfDayComparison atomic\n\n Args:\n name (string): Name of the behavior.\n Defaults to 'WeatherBehavior'.\n\n Attributes:\n _weather (srunner.scenariomanager.weather_sim.Weather): Weather settings.\n _current_time (float): Current CARLA time [seconds].\n \"\"\"\n\n def __init__(self, name=\"WeatherBehavior\"):\n \"\"\"\n Setup parameters\n \"\"\"\n super(WeatherBehavior, self).__init__(name)\n self._weather = None\n self._current_time = None\n\n def initialise(self):\n \"\"\"\n Set current time to current CARLA time\n \"\"\"\n self._current_time = GameTime.get_time()\n\n def update(self):\n \"\"\"\n Check if new weather settings are available on the blackboard, and if yes fetch these\n into the _weather attribute.\n\n Apply the weather settings from _weather to CARLA.\n\n Note:\n To minimize CARLA server interactions, the weather is only updated, when the blackboard\n is updated, or if the weather animation flag is true. In the latter case, the update\n frequency is 1 Hz.\n\n returns:\n py_trees.common.Status.RUNNING\n \"\"\"\n\n weather = None\n\n try:\n check_weather = operator.attrgetter(\"CarlaWeather\")\n weather = check_weather(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n\n if weather:\n self._weather = weather\n delattr(py_trees.blackboard.Blackboard(), \"CarlaWeather\")\n CarlaDataProvider.get_world().set_weather(self._weather.carla_weather)\n py_trees.blackboard.Blackboard().set(\"Datetime\", self._weather.datetime, overwrite=True)\n\n if self._weather and self._weather.animation:\n new_time = GameTime.get_time()\n delta_time = new_time - self._current_time\n\n if delta_time > 1:\n self._weather.update(delta_time)\n self._current_time = new_time\n CarlaDataProvider.get_world().set_weather(self._weather.carla_weather)\n\n py_trees.blackboard.Blackboard().set(\"Datetime\", self._weather.datetime, overwrite=True)\n\n return py_trees.common.Status.RUNNING\n" }, { "path": "scenario_runner/srunner/scenarios/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/scenarios/background_activity.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nScenario spawning elements to make the town dynamic and interesting\n\"\"\"\n\nimport carla\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenarios.basic_scenario import BasicScenario\nimport os\n\nclass BackgroundActivity(BasicScenario):\n\n \"\"\"\n Implementation of a scenario to spawn a set of background actors,\n and to remove traffic jams in background traffic\n\n This is a single ego vehicle scenario\n \"\"\"\n\n town_amount = {\n 'Town01': 120,\n 'Town02': 100,\n 'Town03': 120,\n 'Town04': 200,\n 'Town05': 120,\n 'Town06': 150,\n 'Town07': 110,\n 'Town08': 180,\n 'Town09': 300,\n 'Town10': 120,\n }\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, timeout=35 * 60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self.config = config\n self.debug = debug_mode\n\n self.timeout = timeout # Timeout of scenario in seconds\n\n super(BackgroundActivity, self).__init__(\"BackgroundActivity\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n terminate_on_failure=True,\n criteria_enable=True)\n\n def _initialize_actors(self, config):\n\n town_name = config.town\n if town_name in self.town_amount:\n amount = self.town_amount[town_name]\n else:\n amount = 0\n if os.environ[\"BENCHMARK\"] == \"town05long\":\n amount = 120\n print(\"----------------Eval with Town05 Long, amount=120\", flush=True)\n if os.environ[\"BENCHMARK\"] == \"longest6\":\n amount = 500\n print(\"----------------Eval with Longest6, amount=500\", flush=True)\n new_actors = CarlaDataProvider.request_new_batch_actors('vehicle.*',\n amount,\n carla.Transform(),\n autopilot=True,\n random_location=True,\n rolename='background')\n\n if new_actors is None:\n raise Exception(\"Error: Unable to add the background activity, all spawn points were occupied\")\n\n for _actor in new_actors:\n self.other_actors.append(_actor)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n pass\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n pass\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/basic_scenario.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provide BasicScenario, the basic class of all the scenarios.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport operator\nimport py_trees\n\nimport carla\n\nimport srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions as conditions\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.timer import TimeOut\nfrom srunner.scenariomanager.weather_sim import WeatherBehavior\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import UpdateAllActorControls\n\n\nclass BasicScenario(object):\n\n \"\"\"\n Base class for user-defined scenario\n \"\"\"\n\n def __init__(self, name, ego_vehicles, config, world,\n debug_mode=False, terminate_on_failure=False, criteria_enable=False):\n \"\"\"\n Setup all relevant parameters and create scenario\n and instantiate scenario manager\n \"\"\"\n self.other_actors = []\n if not self.timeout: # pylint: disable=access-member-before-definition\n self.timeout = 60 # If no timeout was provided, set it to 60 seconds\n\n self.criteria_list = [] # List of evaluation criteria\n self.scenario = None\n\n self.ego_vehicles = ego_vehicles\n self.name = name\n self.config = config\n self.terminate_on_failure = terminate_on_failure\n\n self._initialize_environment(world)\n\n # Initializing adversarial actors\n self._initialize_actors(config)\n if CarlaDataProvider.is_sync_mode():\n world.tick()\n else:\n world.wait_for_tick()\n\n # Setup scenario\n if debug_mode:\n py_trees.logging.level = py_trees.logging.Level.DEBUG\n\n behavior = self._create_behavior()\n\n criteria = None\n if criteria_enable:\n criteria = self._create_test_criteria()\n\n # Add a trigger condition for the behavior to ensure the behavior is only activated, when it is relevant\n behavior_seq = py_trees.composites.Sequence()\n trigger_behavior = self._setup_scenario_trigger(config)\n if trigger_behavior:\n behavior_seq.add_child(trigger_behavior)\n\n if behavior is not None:\n behavior_seq.add_child(behavior)\n behavior_seq.name = behavior.name\n\n end_behavior = self._setup_scenario_end(config)\n if end_behavior:\n behavior_seq.add_child(end_behavior)\n\n self.scenario = Scenario(behavior_seq, criteria, self.name, self.timeout, self.terminate_on_failure)\n\n def _initialize_environment(self, world):\n \"\"\"\n Default initialization of weather and road friction.\n Override this method in child class to provide custom initialization.\n \"\"\"\n\n # Set the appropriate weather conditions\n world.set_weather(self.config.weather)\n\n # Set the appropriate road friction\n if self.config.friction is not None:\n friction_bp = world.get_blueprint_library().find('static.trigger.friction')\n extent = carla.Location(1000000.0, 1000000.0, 1000000.0)\n friction_bp.set_attribute('friction', str(self.config.friction))\n friction_bp.set_attribute('extent_x', str(extent.x))\n friction_bp.set_attribute('extent_y', str(extent.y))\n friction_bp.set_attribute('extent_z', str(extent.z))\n\n # Spawn Trigger Friction\n transform = carla.Transform()\n transform.location = carla.Location(-10000.0, -10000.0, 0.0)\n world.spawn_actor(friction_bp, transform)\n\n def _initialize_actors(self, config):\n \"\"\"\n Default initialization of other actors.\n Override this method in child class to provide custom initialization.\n \"\"\"\n if config.other_actors:\n new_actors = CarlaDataProvider.request_new_actors(config.other_actors)\n if not new_actors:\n raise Exception(\"Error: Unable to add actors\")\n\n for new_actor in new_actors:\n self.other_actors.append(new_actor)\n\n def _setup_scenario_trigger(self, config):\n \"\"\"\n This function creates a trigger maneuver, that has to be finished before the real scenario starts.\n This implementation focuses on the first available ego vehicle.\n\n The function can be overloaded by a user implementation inside the user-defined scenario class.\n \"\"\"\n start_location = None\n if config.trigger_points and config.trigger_points[0]:\n start_location = config.trigger_points[0].location # start location of the scenario\n\n ego_vehicle_route = CarlaDataProvider.get_ego_vehicle_route()\n\n if start_location:\n if ego_vehicle_route:\n if config.route_var_name is None: # pylint: disable=no-else-return\n return conditions.InTriggerDistanceToLocationAlongRoute(self.ego_vehicles[0],\n ego_vehicle_route,\n start_location,\n 5)\n else:\n check_name = \"WaitForBlackboardVariable: {}\".format(config.route_var_name)\n return conditions.WaitForBlackboardVariable(name=check_name,\n variable_name=config.route_var_name,\n variable_value=True,\n var_init_value=False)\n\n return conditions.InTimeToArrivalToLocation(self.ego_vehicles[0],\n 2.0,\n start_location)\n\n return None\n\n def _setup_scenario_end(self, config):\n \"\"\"\n This function adds and additional behavior to the scenario, which is triggered\n after it has ended.\n\n The function can be overloaded by a user implementation inside the user-defined scenario class.\n \"\"\"\n ego_vehicle_route = CarlaDataProvider.get_ego_vehicle_route()\n\n if ego_vehicle_route:\n if config.route_var_name is not None:\n set_name = \"Reset Blackboard Variable: {} \".format(config.route_var_name)\n return py_trees.blackboard.SetBlackboardVariable(name=set_name,\n variable_name=config.route_var_name,\n variable_value=False)\n return None\n\n def _create_behavior(self):\n \"\"\"\n Pure virtual function to setup user-defined scenario behavior\n \"\"\"\n raise NotImplementedError(\n \"This function is re-implemented by all scenarios\"\n \"If this error becomes visible the class hierarchy is somehow broken\")\n\n def _create_test_criteria(self):\n \"\"\"\n Pure virtual function to setup user-defined evaluation criteria for the\n scenario\n \"\"\"\n raise NotImplementedError(\n \"This function is re-implemented by all scenarios\"\n \"If this error becomes visible the class hierarchy is somehow broken\")\n\n def change_control(self, control): # pylint: disable=no-self-use\n \"\"\"\n This is a function that changes the control based on the scenario determination\n :param control: a carla vehicle control\n :return: a control to be changed by the scenario.\n\n Note: This method should be overriden by the user-defined scenario behavior\n \"\"\"\n return control\n\n def remove_all_actors(self):\n \"\"\"\n Remove all actors\n \"\"\"\n for i, _ in enumerate(self.other_actors):\n if self.other_actors[i] is not None:\n if CarlaDataProvider.actor_id_exists(self.other_actors[i].id):\n CarlaDataProvider.remove_actor_by_id(self.other_actors[i].id)\n self.other_actors[i] = None\n self.other_actors = []\n\n\nclass Scenario(object):\n\n \"\"\"\n Basic scenario class. This class holds the behavior_tree describing the\n scenario and the test criteria.\n\n The user must not modify this class.\n\n Important parameters:\n - behavior: User defined scenario with py_tree\n - criteria_list: List of user defined test criteria with py_tree\n - timeout (default = 60s): Timeout of the scenario in seconds\n - terminate_on_failure: Terminate scenario on first failure\n \"\"\"\n\n def __init__(self, behavior, criteria, name, timeout=60, terminate_on_failure=False):\n self.behavior = behavior\n self.test_criteria = criteria\n self.timeout = timeout\n self.name = name\n\n if self.test_criteria is not None and not isinstance(self.test_criteria, py_trees.composites.Parallel):\n # list of nodes\n for criterion in self.test_criteria:\n criterion.terminate_on_failure = terminate_on_failure\n\n # Create py_tree for test criteria\n self.criteria_tree = py_trees.composites.Parallel(\n name=\"Test Criteria\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE\n )\n self.criteria_tree.add_children(self.test_criteria)\n self.criteria_tree.setup(timeout=1)\n else:\n self.criteria_tree = criteria\n\n # Create node for timeout\n self.timeout_node = TimeOut(self.timeout, name=\"TimeOut\")\n\n # Create overall py_tree\n self.scenario_tree = py_trees.composites.Parallel(name, policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n if behavior is not None:\n self.scenario_tree.add_child(self.behavior)\n self.scenario_tree.add_child(self.timeout_node)\n self.scenario_tree.add_child(WeatherBehavior())\n self.scenario_tree.add_child(UpdateAllActorControls())\n\n if criteria is not None:\n self.scenario_tree.add_child(self.criteria_tree)\n self.scenario_tree.setup(timeout=1)\n\n def _extract_nodes_from_tree(self, tree): # pylint: disable=no-self-use\n \"\"\"\n Returns the list of all nodes from the given tree\n \"\"\"\n node_list = [tree]\n more_nodes_exist = True\n while more_nodes_exist:\n more_nodes_exist = False\n for node in node_list:\n if node.children:\n node_list.remove(node)\n more_nodes_exist = True\n for child in node.children:\n node_list.append(child)\n\n if len(node_list) == 1 and isinstance(node_list[0], py_trees.composites.Parallel):\n return []\n\n return node_list\n\n def get_criteria(self):\n \"\"\"\n Return the list of test criteria (all leave nodes)\n \"\"\"\n criteria_list = self._extract_nodes_from_tree(self.criteria_tree)\n return criteria_list\n\n def terminate(self):\n \"\"\"\n This function sets the status of all leaves in the scenario tree to INVALID\n \"\"\"\n # Get list of all nodes in the tree\n node_list = self._extract_nodes_from_tree(self.scenario_tree)\n\n # Set status to INVALID\n for node in node_list:\n node.terminate(py_trees.common.Status.INVALID)\n\n # Cleanup all instantiated controllers\n actor_dict = {}\n try:\n check_actors = operator.attrgetter(\"ActorsWithController\")\n actor_dict = check_actors(py_trees.blackboard.Blackboard())\n except AttributeError:\n pass\n for actor_id in actor_dict:\n actor_dict[actor_id].reset()\n py_trees.blackboard.Blackboard().set(\"ActorsWithController\", {}, overwrite=True)\n" }, { "path": "scenario_runner/srunner/scenarios/change_lane.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nChange lane scenario:\n\nThe scenario realizes a driving behavior, in which the user-controlled ego vehicle\nfollows a fast driving car on the highway. There's a slow car driving in great distance to the fast vehicle.\nAt one point the fast vehicle is changing the lane to overtake a slow car, which is driving on the same lane.\n\nThe ego vehicle doesn't \"see\" the slow car before the lane change of the fast car, therefore it hast to react\nfast to avoid an collision. There are two options to avoid an accident:\nThe ego vehicle adjusts its velocity or changes the lane as well.\n\"\"\"\n\nimport random\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n StopVehicle,\n LaneChange,\n WaypointFollower,\n Idle)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import InTriggerDistanceToVehicle, StandStill\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_waypoint_in_distance\n\n\nclass ChangeLane(BasicScenario):\n\n \"\"\"\n This class holds everything required for a \"change lane\" scenario involving three vehicles.\n There are two vehicles driving in the same direction on the highway: A fast car and a slow car in front.\n The fast car will change the lane, when it is close to the slow car.\n\n The ego vehicle is driving right behind the fast car.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n timeout = 1200\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=600):\n \"\"\"\n Setup all relevant parameters and create scenario\n\n If randomize is True, the scenario parameters are randomized\n \"\"\"\n self.timeout = timeout\n self._map = CarlaDataProvider.get_map()\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n\n self._fast_vehicle_velocity = 70\n self._slow_vehicle_velocity = 0\n self._change_lane_velocity = 15\n\n self._slow_vehicle_distance = 100\n self._fast_vehicle_distance = 20\n self._trigger_distance = 30\n self._max_brake = 1\n\n self.direction = 'left' # direction of lane change\n self.lane_check = 'true' # check whether a lane change is possible\n\n super(ChangeLane, self).__init__(\"ChangeLane\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n if randomize:\n self._fast_vehicle_distance = random.randint(10, 51)\n self._fast_vehicle_velocity = random.randint(100, 201)\n self._slow_vehicle_velocity = random.randint(1, 6)\n\n def _initialize_actors(self, config):\n\n # add actors from xml file\n for actor in config.other_actors:\n vehicle = CarlaDataProvider.request_new_actor(actor.model, actor.transform)\n self.other_actors.append(vehicle)\n vehicle.set_simulate_physics(enabled=False)\n\n # fast vehicle, tesla\n # transform visible\n fast_car_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._fast_vehicle_distance)\n self.fast_car_visible = carla.Transform(\n carla.Location(fast_car_waypoint.transform.location.x,\n fast_car_waypoint.transform.location.y,\n fast_car_waypoint.transform.location.z + 1),\n fast_car_waypoint.transform.rotation)\n\n # slow vehicle, vw\n # transform visible\n slow_car_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._slow_vehicle_distance)\n self.slow_car_visible = carla.Transform(\n carla.Location(slow_car_waypoint.transform.location.x,\n slow_car_waypoint.transform.location.y,\n slow_car_waypoint.transform.location.z),\n slow_car_waypoint.transform.rotation)\n\n def _create_behavior(self):\n\n # sequence vw\n # make visible\n sequence_vw = py_trees.composites.Sequence(\"VW T2\")\n vw_visible = ActorTransformSetter(self.other_actors[1], self.slow_car_visible)\n sequence_vw.add_child(vw_visible)\n\n # brake, avoid rolling backwarts\n brake = StopVehicle(self.other_actors[1], self._max_brake)\n sequence_vw.add_child(brake)\n sequence_vw.add_child(Idle())\n\n # sequence tesla\n # make visible\n sequence_tesla = py_trees.composites.Sequence(\"Tesla\")\n tesla_visible = ActorTransformSetter(self.other_actors[0], self.fast_car_visible)\n sequence_tesla.add_child(tesla_visible)\n\n # drive fast towards slow vehicle\n just_drive = py_trees.composites.Parallel(\"DrivingTowardsSlowVehicle\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n tesla_driving_fast = WaypointFollower(self.other_actors[0], self._fast_vehicle_velocity)\n just_drive.add_child(tesla_driving_fast)\n distance_to_vehicle = InTriggerDistanceToVehicle(\n self.other_actors[1], self.other_actors[0], self._trigger_distance)\n just_drive.add_child(distance_to_vehicle)\n sequence_tesla.add_child(just_drive)\n\n # change lane\n lane_change_atomic = LaneChange(self.other_actors[0], distance_other_lane=200)\n sequence_tesla.add_child(lane_change_atomic)\n sequence_tesla.add_child(Idle())\n\n # ego vehicle\n # end condition\n endcondition = py_trees.composites.Parallel(\"Waiting for end position of ego vehicle\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n endcondition_part1 = InTriggerDistanceToVehicle(self.other_actors[1],\n self.ego_vehicles[0],\n distance=20,\n name=\"FinalDistance\")\n endcondition_part2 = StandStill(self.ego_vehicles[0], name=\"FinalSpeed\")\n endcondition.add_child(endcondition_part1)\n endcondition.add_child(endcondition_part2)\n\n # build tree\n root = py_trees.composites.Parallel(\"Parallel Behavior\", policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n root.add_child(sequence_vw)\n root.add_child(sequence_tesla)\n root.add_child(endcondition)\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/control_loss.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nControl Loss Vehicle scenario:\n\nThe scenario realizes that the vehicle looses control due to\nbad road conditions, etc. and checks to see if the vehicle\nregains control and corrects it's course.\n\"\"\"\n\nimport numpy.random as random\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import ChangeNoiseParameters, ActorTransformSetter\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToLocation,\n InTriggerDistanceToNextIntersection,\n DriveDistance)\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_location_in_distance_from_wp\n\n\nclass ControlLoss(BasicScenario):\n\n \"\"\"\n Implementation of \"Control Loss Vehicle\" (Traffic Scenario 01)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n # ego vehicle parameters\n self._no_of_jitter = 10\n self._noise_mean = 0 # Mean value of steering noise\n self._noise_std = 0.01 # Std. deviation of steering noise\n self._dynamic_mean_for_steer = 0.001\n self._dynamic_mean_for_throttle = 0.045\n self._abort_distance_to_intersection = 10\n self._current_steer_noise = [0] # This is a list, since lists are mutable\n self._current_throttle_noise = [0]\n self._start_distance = 20\n self._trigger_dist = 2\n self._end_distance = 30\n self._ego_vehicle_max_steer = 0.0\n self._ego_vehicle_max_throttle = 1.0\n self._ego_vehicle_target_velocity = 15\n self._map = CarlaDataProvider.get_map()\n # Timeout of scenario in seconds\n self.timeout = timeout\n # The reference trigger for the control loss\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n self.loc_list = []\n self.obj = []\n self._randomize = randomize\n super(ControlLoss, self).__init__(\"ControlLoss\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n if self._randomize:\n self._distance = random.randint(low=10, high=80, size=3)\n self._distance = sorted(self._distance)\n else:\n self._distance = [14, 48, 74]\n first_loc, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._distance[0])\n second_loc, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._distance[1])\n third_loc, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._distance[2])\n\n self.loc_list.extend([first_loc, second_loc, third_loc])\n self._dist_prop = [x - 2 for x in self._distance]\n\n self.first_loc_prev, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._dist_prop[0])\n self.sec_loc_prev, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._dist_prop[1])\n self.third_loc_prev, _ = get_location_in_distance_from_wp(self._reference_waypoint, self._dist_prop[2])\n\n self.first_transform = carla.Transform(self.first_loc_prev)\n self.sec_transform = carla.Transform(self.sec_loc_prev)\n self.third_transform = carla.Transform(self.third_loc_prev)\n self.first_transform = carla.Transform(carla.Location(self.first_loc_prev.x,\n self.first_loc_prev.y,\n self.first_loc_prev.z))\n self.sec_transform = carla.Transform(carla.Location(self.sec_loc_prev.x,\n self.sec_loc_prev.y,\n self.sec_loc_prev.z))\n self.third_transform = carla.Transform(carla.Location(self.third_loc_prev.x,\n self.third_loc_prev.y,\n self.third_loc_prev.z))\n\n first_debris = CarlaDataProvider.request_new_actor('static.prop.dirtdebris01', self.first_transform, 'prop')\n second_debris = CarlaDataProvider.request_new_actor('static.prop.dirtdebris01', self.sec_transform, 'prop')\n third_debris = CarlaDataProvider.request_new_actor('static.prop.dirtdebris01', self.third_transform, 'prop')\n\n first_debris.set_transform(self.first_transform)\n second_debris.set_transform(self.sec_transform)\n third_debris.set_transform(self.third_transform)\n\n self.obj.extend([first_debris, second_debris, third_debris])\n for debris in self.obj:\n debris.set_simulate_physics(False)\n\n self.other_actors.append(first_debris)\n self.other_actors.append(second_debris)\n self.other_actors.append(third_debris)\n\n def _create_behavior(self):\n \"\"\"\n The scenario defined after is a \"control loss vehicle\" scenario. After\n invoking this scenario, it will wait until the vehicle drove a few meters\n (_start_distance), and then perform a jitter action. Finally, the vehicle\n has to reach a target point (_end_distance). If this does not happen within\n 60 seconds, a timeout stops the scenario\n \"\"\"\n # start condition\n start_end_parallel = py_trees.composites.Parallel(\"Jitter\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n start_condition = InTriggerDistanceToLocation(self.ego_vehicles[0], self.first_loc_prev, self._trigger_dist)\n for _ in range(self._no_of_jitter):\n\n # change the current noise to be applied\n turn = ChangeNoiseParameters(self._current_steer_noise, self._current_throttle_noise,\n self._noise_mean, self._noise_std, self._dynamic_mean_for_steer,\n self._dynamic_mean_for_throttle) # Mean value of steering noise\n # Noise end! put again the added noise to zero.\n noise_end = ChangeNoiseParameters(self._current_steer_noise, self._current_throttle_noise,\n 0, 0, 0, 0)\n\n jitter_action = py_trees.composites.Parallel(\"Jitter\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n # Abort jitter_sequence, if the vehicle is approaching an intersection\n jitter_abort = InTriggerDistanceToNextIntersection(self.ego_vehicles[0], self._abort_distance_to_intersection)\n # endcondition: Check if vehicle reached waypoint _end_distance from here:\n end_condition = DriveDistance(self.ego_vehicles[0], self._end_distance)\n start_end_parallel.add_child(start_condition)\n start_end_parallel.add_child(end_condition)\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"ControlLoss\")\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self.first_transform, physics=False))\n sequence.add_child(ActorTransformSetter(self.other_actors[1], self.sec_transform, physics=False))\n sequence.add_child(ActorTransformSetter(self.other_actors[2], self.third_transform, physics=False))\n jitter = py_trees.composites.Sequence(\"Jitter Behavior\")\n jitter.add_child(turn)\n jitter.add_child(InTriggerDistanceToLocation(self.ego_vehicles[0], self.sec_loc_prev, self._trigger_dist))\n jitter.add_child(turn)\n jitter.add_child(InTriggerDistanceToLocation(self.ego_vehicles[0], self.third_loc_prev, self._trigger_dist))\n jitter.add_child(turn)\n jitter_action.add_child(jitter)\n jitter_action.add_child(jitter_abort)\n sequence.add_child(start_end_parallel)\n sequence.add_child(jitter_action)\n sequence.add_child(end_condition)\n sequence.add_child(noise_end)\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n criteria.append(collision_criterion)\n\n return criteria\n\n def change_control(self, control):\n \"\"\"\n This is a function that changes the control based on the scenario determination\n :param control: a carla vehicle control\n :return: a control to be changed by the scenario.\n \"\"\"\n control.steer += self._current_steer_noise[0]\n control.throttle += self._current_throttle_noise[0]\n\n return control\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/cut_in.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nCut in scenario:\n\nThe scenario realizes a driving behavior on the highway.\nThe user-controlled ego vehicle is driving straight and keeping its velocity at a constant level.\nAnother car is cutting just in front, coming from left or right lane.\n\nThe ego vehicle may need to brake to avoid a collision.\n\"\"\"\n\nimport random\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n LaneChange,\n WaypointFollower,\n AccelerateToCatchUp)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import InTriggerDistanceToVehicle, DriveDistance\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\n\nclass CutIn(BasicScenario):\n\n \"\"\"\n The ego vehicle is driving on a highway and another car is cutting in just in front.\n This is a single ego vehicle scenario\n \"\"\"\n\n timeout = 1200\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=600):\n\n self.timeout = timeout\n self._map = CarlaDataProvider.get_map()\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n\n self._velocity = 40\n self._delta_velocity = 10\n self._trigger_distance = 30\n\n # get direction from config name\n self._config = config\n self._direction = None\n self._transform_visible = None\n\n super(CutIn, self).__init__(\"CutIn\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n if randomize:\n self._velocity = random.randint(20, 60)\n self._trigger_distance = random.randint(10, 40)\n\n def _initialize_actors(self, config):\n\n # direction of lane, on which other_actor is driving before lane change\n if 'LEFT' in self._config.name.upper():\n self._direction = 'left'\n\n if 'RIGHT' in self._config.name.upper():\n self._direction = 'right'\n\n # add actors from xml file\n for actor in config.other_actors:\n vehicle = CarlaDataProvider.request_new_actor(actor.model, actor.transform)\n self.other_actors.append(vehicle)\n vehicle.set_simulate_physics(enabled=False)\n\n # transform visible\n other_actor_transform = self.other_actors[0].get_transform()\n self._transform_visible = carla.Transform(\n carla.Location(other_actor_transform.location.x,\n other_actor_transform.location.y,\n other_actor_transform.location.z + 105),\n other_actor_transform.rotation)\n\n def _create_behavior(self):\n \"\"\"\n Order of sequence:\n - car_visible: spawn car at a visible transform\n - just_drive: drive until in trigger distance to ego_vehicle\n - accelerate: accelerate to catch up distance to ego_vehicle\n - lane_change: change the lane\n - endcondition: drive for a defined distance\n \"\"\"\n\n # car_visible\n behaviour = py_trees.composites.Sequence(\"CarOn_{}_Lane\" .format(self._direction))\n car_visible = ActorTransformSetter(self.other_actors[0], self._transform_visible)\n behaviour.add_child(car_visible)\n\n # just_drive\n just_drive = py_trees.composites.Parallel(\n \"DrivingStraight\", policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n car_driving = WaypointFollower(self.other_actors[0], self._velocity)\n just_drive.add_child(car_driving)\n\n trigger_distance = InTriggerDistanceToVehicle(\n self.other_actors[0], self.ego_vehicles[0], self._trigger_distance)\n just_drive.add_child(trigger_distance)\n behaviour.add_child(just_drive)\n\n # accelerate\n accelerate = AccelerateToCatchUp(self.other_actors[0], self.ego_vehicles[0], throttle_value=1,\n delta_velocity=self._delta_velocity, trigger_distance=5, max_distance=500)\n behaviour.add_child(accelerate)\n\n # lane_change\n if self._direction == 'left':\n lane_change = LaneChange(\n self.other_actors[0], speed=None, direction='right', distance_same_lane=5, distance_other_lane=300)\n behaviour.add_child(lane_change)\n else:\n lane_change = LaneChange(\n self.other_actors[0], speed=None, direction='left', distance_same_lane=5, distance_other_lane=300)\n behaviour.add_child(lane_change)\n\n # endcondition\n endcondition = DriveDistance(self.other_actors[0], 200)\n\n # build tree\n root = py_trees.composites.Sequence(\"Behavior\", policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n root.add_child(behaviour)\n root.add_child(endcondition)\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria is created, which is later used in the parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors after deletion.\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/follow_leading_vehicle.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nFollow leading vehicle scenario:\n\nThe scenario realizes a common driving behavior, in which the\nuser-controlled ego vehicle follows a leading car driving down\na given road. At some point the leading car has to slow down and\nfinally stop. The ego vehicle has to react accordingly to avoid\na collision. The scenario ends either via a timeout, or if the ego\nvehicle stopped close enough to the leading vehicle\n\"\"\"\n\nimport random\n\nimport py_trees\n\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n KeepVelocity,\n StopVehicle,\n WaypointFollower)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToVehicle,\n InTriggerDistanceToNextIntersection,\n DriveDistance,\n StandStill)\nfrom srunner.scenariomanager.timer import TimeOut\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_waypoint_in_distance\n\n\nclass FollowLeadingVehicle(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple \"Follow a leading vehicle\"\n scenario involving two vehicles. (Traffic Scenario 2)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n timeout = 120 # Timeout of scenario in seconds\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n\n If randomize is True, the scenario parameters are randomized\n \"\"\"\n\n self._map = CarlaDataProvider.get_map()\n self._first_vehicle_location = 25\n self._first_vehicle_speed = 10\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n self._other_actor_max_brake = 1.0\n self._other_actor_stop_in_front_intersection = 20\n self._other_actor_transform = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(FollowLeadingVehicle, self).__init__(\"FollowVehicle\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n if randomize:\n self._ego_other_distance_start = random.randint(4, 8)\n\n # Example code how to randomize start location\n # distance = random.randint(20, 80)\n # new_location, _ = get_location_in_distance(self.ego_vehicles[0], distance)\n # waypoint = CarlaDataProvider.get_map().get_waypoint(new_location)\n # waypoint.transform.location.z += 39\n # self.other_actors[0].set_transform(waypoint.transform)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n first_vehicle_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._first_vehicle_location)\n self._other_actor_transform = carla.Transform(\n carla.Location(first_vehicle_waypoint.transform.location.x,\n first_vehicle_waypoint.transform.location.y,\n first_vehicle_waypoint.transform.location.z + 1),\n first_vehicle_waypoint.transform.rotation)\n first_vehicle_transform = carla.Transform(\n carla.Location(self._other_actor_transform.location.x,\n self._other_actor_transform.location.y,\n self._other_actor_transform.location.z - 500),\n self._other_actor_transform.rotation)\n first_vehicle = CarlaDataProvider.request_new_actor('vehicle.nissan.patrol', first_vehicle_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n The scenario defined after is a \"follow leading vehicle\" scenario. After\n invoking this scenario, it will wait for the user controlled vehicle to\n enter the start region, then make the other actor to drive until reaching\n the next intersection. Finally, the user-controlled vehicle has to be close\n enough to the other actor to end the scenario.\n If this does not happen within 60 seconds, a timeout stops the scenario\n \"\"\"\n\n # to avoid the other actor blocking traffic, it was spawed elsewhere\n # reset its pose to the required one\n start_transform = ActorTransformSetter(self.other_actors[0], self._other_actor_transform)\n\n # let the other actor drive until next intersection\n # We should add some feedback mechanism to respond to ego_vehicle behavior\n driving_to_next_intersection = py_trees.composites.Parallel(\n \"DrivingTowardsIntersection\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n driving_to_next_intersection.add_child(WaypointFollower(self.other_actors[0], self._first_vehicle_speed))\n driving_to_next_intersection.add_child(InTriggerDistanceToNextIntersection(\n self.other_actors[0], self._other_actor_stop_in_front_intersection))\n\n # stop vehicle\n stop = StopVehicle(self.other_actors[0], self._other_actor_max_brake)\n\n # end condition\n endcondition = py_trees.composites.Parallel(\"Waiting for end position\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n endcondition_part1 = InTriggerDistanceToVehicle(self.other_actors[0],\n self.ego_vehicles[0],\n distance=20,\n name=\"FinalDistance\")\n endcondition_part2 = StandStill(self.ego_vehicles[0], name=\"StandStill\", duration=1)\n endcondition.add_child(endcondition_part1)\n endcondition.add_child(endcondition_part2)\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"Sequence Behavior\")\n sequence.add_child(start_transform)\n sequence.add_child(driving_to_next_intersection)\n sequence.add_child(stop)\n sequence.add_child(endcondition)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n\n\nclass FollowLeadingVehicleWithObstacle(BasicScenario):\n\n \"\"\"\n This class holds a scenario similar to FollowLeadingVehicle\n but there is an obstacle in front of the leading vehicle\n\n This is a single ego vehicle scenario\n \"\"\"\n\n timeout = 120 # Timeout of scenario in seconds\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._map = CarlaDataProvider.get_map()\n self._first_actor_location = 25\n self._second_actor_location = self._first_actor_location + 41\n self._first_actor_speed = 10\n self._second_actor_speed = 1.5\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n self._other_actor_max_brake = 1.0\n self._first_actor_transform = None\n self._second_actor_transform = None\n\n super(FollowLeadingVehicleWithObstacle, self).__init__(\"FollowLeadingVehicleWithObstacle\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n if randomize:\n self._ego_other_distance_start = random.randint(4, 8)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n first_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._first_actor_location)\n second_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._second_actor_location)\n first_actor_transform = carla.Transform(\n carla.Location(first_actor_waypoint.transform.location.x,\n first_actor_waypoint.transform.location.y,\n first_actor_waypoint.transform.location.z - 500),\n first_actor_waypoint.transform.rotation)\n self._first_actor_transform = carla.Transform(\n carla.Location(first_actor_waypoint.transform.location.x,\n first_actor_waypoint.transform.location.y,\n first_actor_waypoint.transform.location.z + 1),\n first_actor_waypoint.transform.rotation)\n yaw_1 = second_actor_waypoint.transform.rotation.yaw + 90\n second_actor_transform = carla.Transform(\n carla.Location(second_actor_waypoint.transform.location.x,\n second_actor_waypoint.transform.location.y,\n second_actor_waypoint.transform.location.z - 500),\n carla.Rotation(second_actor_waypoint.transform.rotation.pitch, yaw_1,\n second_actor_waypoint.transform.rotation.roll))\n self._second_actor_transform = carla.Transform(\n carla.Location(second_actor_waypoint.transform.location.x,\n second_actor_waypoint.transform.location.y,\n second_actor_waypoint.transform.location.z + 1),\n carla.Rotation(second_actor_waypoint.transform.rotation.pitch, yaw_1,\n second_actor_waypoint.transform.rotation.roll))\n\n first_actor = CarlaDataProvider.request_new_actor(\n 'vehicle.nissan.patrol', first_actor_transform)\n second_actor = CarlaDataProvider.request_new_actor(\n 'vehicle.diamondback.century', second_actor_transform)\n\n first_actor.set_simulate_physics(enabled=False)\n second_actor.set_simulate_physics(enabled=False)\n self.other_actors.append(first_actor)\n self.other_actors.append(second_actor)\n\n def _create_behavior(self):\n \"\"\"\n The scenario defined after is a \"follow leading vehicle\" scenario. After\n invoking this scenario, it will wait for the user controlled vehicle to\n enter the start region, then make the other actor to drive towards obstacle.\n Once obstacle clears the road, make the other actor to drive towards the\n next intersection. Finally, the user-controlled vehicle has to be close\n enough to the other actor to end the scenario.\n If this does not happen within 60 seconds, a timeout stops the scenario\n \"\"\"\n\n # let the other actor drive until next intersection\n driving_to_next_intersection = py_trees.composites.Parallel(\n \"Driving towards Intersection\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n obstacle_clear_road = py_trees.composites.Parallel(\"Obstalce clearing road\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n obstacle_clear_road.add_child(DriveDistance(self.other_actors[1], 4))\n obstacle_clear_road.add_child(KeepVelocity(self.other_actors[1], self._second_actor_speed))\n\n stop_near_intersection = py_trees.composites.Parallel(\n \"Waiting for end position near Intersection\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n stop_near_intersection.add_child(WaypointFollower(self.other_actors[0], 10))\n stop_near_intersection.add_child(InTriggerDistanceToNextIntersection(self.other_actors[0], 20))\n\n driving_to_next_intersection.add_child(WaypointFollower(self.other_actors[0], self._first_actor_speed))\n driving_to_next_intersection.add_child(InTriggerDistanceToVehicle(self.other_actors[1],\n self.other_actors[0], 15))\n\n # end condition\n endcondition = py_trees.composites.Parallel(\"Waiting for end position\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n endcondition_part1 = InTriggerDistanceToVehicle(self.other_actors[0],\n self.ego_vehicles[0],\n distance=20,\n name=\"FinalDistance\")\n endcondition_part2 = StandStill(self.ego_vehicles[0], name=\"FinalSpeed\", duration=1)\n endcondition.add_child(endcondition_part1)\n endcondition.add_child(endcondition_part2)\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"Sequence Behavior\")\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self._first_actor_transform))\n sequence.add_child(ActorTransformSetter(self.other_actors[1], self._second_actor_transform))\n sequence.add_child(driving_to_next_intersection)\n sequence.add_child(StopVehicle(self.other_actors[0], self._other_actor_max_brake))\n sequence.add_child(TimeOut(3))\n sequence.add_child(obstacle_clear_road)\n sequence.add_child(stop_near_intersection)\n sequence.add_child(StopVehicle(self.other_actors[0], self._other_actor_max_brake))\n sequence.add_child(endcondition)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n sequence.add_child(ActorDestroy(self.other_actors[1]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/freeride.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nSimple freeride scenario. No action, no triggers. Ego vehicle can simply cruise around.\n\"\"\"\n\nimport py_trees\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import Idle\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\n\nclass FreeRide(BasicScenario):\n\n \"\"\"\n Implementation of a simple free ride scenario that consits only of the ego vehicle\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=10000000):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n # Timeout of scenario in seconds\n self.timeout = timeout\n super(FreeRide, self).__init__(\"FreeRide\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _setup_scenario_trigger(self, config):\n \"\"\"\n \"\"\"\n return None\n\n def _create_behavior(self):\n \"\"\"\n \"\"\"\n sequence = py_trees.composites.Sequence(\"Sequence Behavior\")\n sequence.add_child(Idle())\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n for ego_vehicle in self.ego_vehicles:\n collision_criterion = CollisionTest(ego_vehicle)\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/junction_crossing_route.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nAll intersection related scenarios that are part of a route.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport py_trees\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import TrafficLightManipulator\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest, DrivenDistanceTest, MaxVelocityTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import DriveDistance, WaitEndIntersection\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\n\nclass SignalJunctionCrossingRoute(BasicScenario):\n\n \"\"\"\n At routes, these scenarios are simplified, as they can be triggered making\n use of the background activity. To ensure interactions with this background\n activity, the traffic lights are modified, setting two of them to green\n \"\"\"\n\n # ego vehicle parameters\n _ego_max_velocity_allowed = 20 # Maximum allowed velocity [m/s]\n _ego_expected_driven_distance = 50 # Expected driven distance [m]\n _ego_distance_to_drive = 20 # Allowed distance to drive\n\n _traffic_light = None\n\n # Depending on the route, decide which traffic lights can be modified\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=180):\n \"\"\"\n Setup all relevant parameters and create scenario\n and instantiate scenario manager\n \"\"\"\n # Timeout of scenario in seconds\n self.timeout = timeout\n self.subtype = config.subtype\n\n super(SignalJunctionCrossingRoute, self).__init__(\"SignalJunctionCrossingRoute\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n def _create_behavior(self):\n \"\"\"\n Scenario behavior:\n When close to an intersection, the traffic lights will turn green for\n both the ego_vehicle and another lane, allowing the background activity\n to \"run\" their red light, creating scenarios 7, 8 and 9.\n\n If this does not happen within 120 seconds, a timeout stops the scenario\n \"\"\"\n\n # Changes traffic lights\n traffic_hack = TrafficLightManipulator(self.ego_vehicles[0], self.subtype)\n\n # finally wait that ego vehicle drove a specific distance\n wait = DriveDistance(\n self.ego_vehicles[0],\n self._ego_distance_to_drive,\n name=\"DriveDistance\")\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"SignalJunctionCrossingRoute\")\n sequence.add_child(traffic_hack)\n sequence.add_child(wait)\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n max_velocity_criterion = MaxVelocityTest(\n self.ego_vehicles[0],\n self._ego_max_velocity_allowed,\n optional=True)\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n driven_distance_criterion = DrivenDistanceTest(\n self.ego_vehicles[0],\n self._ego_expected_driven_distance)\n\n criteria.append(max_velocity_criterion)\n criteria.append(collision_criterion)\n criteria.append(driven_distance_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors and traffic lights upon deletion\n \"\"\"\n self._traffic_light = None\n self.remove_all_actors()\n\n\nclass NoSignalJunctionCrossingRoute(BasicScenario):\n\n \"\"\"\n At routes, these scenarios are simplified, as they can be triggered making\n use of the background activity. For unsignalized intersections, just wait\n until the ego_vehicle has left the intersection.\n \"\"\"\n\n # ego vehicle parameters\n _ego_max_velocity_allowed = 20 # Maximum allowed velocity [m/s]\n _ego_expected_driven_distance = 50 # Expected driven distance [m]\n _ego_distance_to_drive = 20 # Allowed distance to drive\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=180):\n \"\"\"\n Setup all relevant parameters and create scenario\n and instantiate scenario manager\n \"\"\"\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(NoSignalJunctionCrossingRoute, self).__init__(\"NoSignalJunctionCrossingRoute\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n def _create_behavior(self):\n \"\"\"\n Scenario behavior:\n When close to an intersection, the traffic lights will turn green for\n both the ego_vehicle and another lane, allowing the background activity\n to \"run\" their red light.\n\n If this does not happen within 120 seconds, a timeout stops the scenario\n \"\"\"\n # finally wait that ego vehicle drove a specific distance\n wait = WaitEndIntersection(\n self.ego_vehicles[0],\n name=\"WaitEndIntersection\")\n end_condition = DriveDistance(\n self.ego_vehicles[0],\n self._ego_distance_to_drive,\n name=\"DriveDistance\")\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"NoSignalJunctionCrossingRoute\")\n sequence.add_child(wait)\n sequence.add_child(end_condition)\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n max_velocity_criterion = MaxVelocityTest(\n self.ego_vehicles[0],\n self._ego_max_velocity_allowed,\n optional=True)\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n driven_distance_criterion = DrivenDistanceTest(\n self.ego_vehicles[0],\n self._ego_expected_driven_distance)\n\n criteria.append(max_velocity_criterion)\n criteria.append(collision_criterion)\n criteria.append(driven_distance_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors and traffic lights upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/maneuver_opposite_direction.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nVehicle Maneuvering In Opposite Direction:\n\nVehicle is passing another vehicle in a rural area, in daylight, under clear\nweather conditions, at a non-junction and encroaches into another\nvehicle traveling in the opposite direction.\n\"\"\"\n\nfrom six.moves.queue import Queue # pylint: disable=relative-import\n\nimport math\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n ActorSource,\n ActorSink,\n WaypointFollower)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import DriveDistance\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_waypoint_in_distance\n\n\nclass ManeuverOppositeDirection(BasicScenario):\n\n \"\"\"\n \"Vehicle Maneuvering In Opposite Direction\" (Traffic Scenario 06)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n obstacle_type='barrier', timeout=120):\n \"\"\"\n Setup all relevant parameters and create scenario\n obstacle_type -> flag to select type of leading obstacle. Values: vehicle, barrier\n \"\"\"\n self._world = world\n self._map = CarlaDataProvider.get_map()\n self._first_vehicle_location = 50\n self._second_vehicle_location = self._first_vehicle_location + 60\n self._ego_vehicle_drive_distance = self._second_vehicle_location * 2\n self._start_distance = self._first_vehicle_location * 0.9\n self._opposite_speed = 5.56 # m/s\n self._source_gap = 40 # m\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n self._source_transform = None\n self._sink_location = None\n self._blackboard_queue_name = 'ManeuverOppositeDirection/actor_flow_queue'\n self._queue = py_trees.blackboard.Blackboard().set(self._blackboard_queue_name, Queue())\n self._obstacle_type = obstacle_type\n self._first_actor_transform = None\n self._second_actor_transform = None\n self._third_actor_transform = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(ManeuverOppositeDirection, self).__init__(\n \"ManeuverOppositeDirection\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n first_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._first_vehicle_location)\n second_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._second_vehicle_location)\n second_actor_waypoint = second_actor_waypoint.get_left_lane()\n\n first_actor_transform = carla.Transform(\n first_actor_waypoint.transform.location,\n first_actor_waypoint.transform.rotation)\n if self._obstacle_type == 'vehicle':\n first_actor_model = 'vehicle.nissan.micra'\n else:\n first_actor_transform.rotation.yaw += 90\n first_actor_model = 'static.prop.streetbarrier'\n second_prop_waypoint = first_actor_waypoint.next(2.0)[0]\n position_yaw = second_prop_waypoint.transform.rotation.yaw + 90\n offset_location = carla.Location(\n 0.50 * second_prop_waypoint.lane_width * math.cos(math.radians(position_yaw)),\n 0.50 * second_prop_waypoint.lane_width * math.sin(math.radians(position_yaw)))\n second_prop_transform = carla.Transform(\n second_prop_waypoint.transform.location + offset_location, first_actor_transform.rotation)\n second_prop_actor = CarlaDataProvider.request_new_actor(first_actor_model, second_prop_transform)\n second_prop_actor.set_simulate_physics(True)\n first_actor = CarlaDataProvider.request_new_actor(first_actor_model, first_actor_transform)\n first_actor.set_simulate_physics(True)\n second_actor = CarlaDataProvider.request_new_actor('vehicle.audi.tt', second_actor_waypoint.transform)\n\n self.other_actors.append(first_actor)\n self.other_actors.append(second_actor)\n if self._obstacle_type != 'vehicle':\n self.other_actors.append(second_prop_actor)\n\n self._source_transform = second_actor_waypoint.transform\n sink_waypoint = second_actor_waypoint.next(1)[0]\n while not sink_waypoint.is_intersection:\n sink_waypoint = sink_waypoint.next(1)[0]\n self._sink_location = sink_waypoint.transform.location\n\n self._first_actor_transform = first_actor_transform\n self._second_actor_transform = second_actor_waypoint.transform\n self._third_actor_transform = second_prop_transform\n\n def _create_behavior(self):\n \"\"\"\n The behavior tree returned by this method is as follows:\n The ego vehicle is trying to pass a leading vehicle in the same lane\n by moving onto the oncoming lane while another vehicle is moving in the\n opposite direction in the oncoming lane.\n \"\"\"\n\n # Leaf nodes\n actor_source = ActorSource(\n ['vehicle.audi.tt', 'vehicle.tesla.model3', 'vehicle.nissan.micra'],\n self._source_transform, self._source_gap, self._blackboard_queue_name)\n actor_sink = ActorSink(self._sink_location, 10)\n ego_drive_distance = DriveDistance(self.ego_vehicles[0], self._ego_vehicle_drive_distance)\n waypoint_follower = WaypointFollower(\n self.other_actors[1], self._opposite_speed,\n blackboard_queue_name=self._blackboard_queue_name, avoid_collision=True)\n\n # Non-leaf nodes\n parallel_root = py_trees.composites.Parallel(policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n # Building tree\n parallel_root.add_child(ego_drive_distance)\n parallel_root.add_child(actor_source)\n parallel_root.add_child(actor_sink)\n parallel_root.add_child(waypoint_follower)\n\n scenario_sequence = py_trees.composites.Sequence()\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._first_actor_transform))\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[1], self._second_actor_transform))\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[2], self._third_actor_transform))\n scenario_sequence.add_child(parallel_root)\n scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))\n scenario_sequence.add_child(ActorDestroy(self.other_actors[1]))\n scenario_sequence.add_child(ActorDestroy(self.other_actors[2]))\n\n return scenario_sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/master_scenario.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nBasic CARLA Autonomous Driving training scenario\n\"\"\"\n\nimport py_trees\n\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteConfiguration\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import Idle\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import (CollisionTest,\n InRouteTest,\n RouteCompletionTest,\n OutsideRouteLanesTest,\n RunningRedLightTest,\n RunningStopTest,\n ActorSpeedAboveThresholdTest)\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\n\nclass MasterScenario(BasicScenario):\n\n \"\"\"\n Implementation of a Master scenario that controls the route.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n radius = 10.0 # meters\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=300):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self.config = config\n self.route = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n if hasattr(self.config, 'route'):\n self.route = self.config.route\n else:\n raise ValueError(\"Master scenario must have a route\")\n\n super(MasterScenario, self).__init__(\"MasterScenario\", ego_vehicles=ego_vehicles, config=config,\n world=world, debug_mode=debug_mode,\n terminate_on_failure=True, criteria_enable=criteria_enable)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"MasterScenario\")\n idle_behavior = Idle()\n sequence.add_child(idle_behavior)\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n\n if isinstance(self.route, RouteConfiguration):\n route = self.route.data\n else:\n route = self.route\n\n collision_criterion = CollisionTest(self.ego_vehicles[0], terminate_on_failure=False)\n\n route_criterion = InRouteTest(self.ego_vehicles[0],\n route=route,\n offroad_max=30,\n terminate_on_failure=True)\n\n completion_criterion = RouteCompletionTest(self.ego_vehicles[0], route=route)\n\n outsidelane_criterion = OutsideRouteLanesTest(self.ego_vehicles[0], route=route)\n\n red_light_criterion = RunningRedLightTest(self.ego_vehicles[0])\n\n stop_criterion = RunningStopTest(self.ego_vehicles[0])\n\n blocked_criterion = ActorSpeedAboveThresholdTest(self.ego_vehicles[0],\n speed_threshold=0.1,\n below_threshold_max_time=90.0,\n terminate_on_failure=True)\n\n parallel_criteria = py_trees.composites.Parallel(\"group_criteria\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n parallel_criteria.add_child(completion_criterion)\n parallel_criteria.add_child(collision_criterion)\n parallel_criteria.add_child(route_criterion)\n parallel_criteria.add_child(outsidelane_criterion)\n parallel_criteria.add_child(red_light_criterion)\n parallel_criteria.add_child(stop_criterion)\n parallel_criteria.add_child(blocked_criterion)\n\n return parallel_criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/no_signal_junction_crossing.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nNon-signalized junctions: crossing negotiation:\n\nThe hero vehicle is passing through a junction without traffic lights\nAnd encounters another vehicle passing across the junction.\n\"\"\"\n\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n SyncArrival,\n KeepVelocity,\n StopVehicle)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import InTriggerRegion\nfrom srunner.scenarios.basic_scenario import BasicScenario\n\n\nclass NoSignalJunctionCrossing(BasicScenario):\n\n \"\"\"\n Implementation class for\n 'Non-signalized junctions: crossing negotiation' scenario,\n (Traffic Scenario 10).\n\n This is a single ego vehicle scenario\n \"\"\"\n\n # ego vehicle parameters\n _ego_vehicle_max_velocity = 20\n _ego_vehicle_driven_distance = 105\n\n # other vehicle\n _other_actor_max_brake = 1.0\n _other_actor_target_velocity = 15\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n\n self._other_actor_transform = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(NoSignalJunctionCrossing, self).__init__(\"NoSignalJunctionCrossing\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n self._other_actor_transform = config.other_actors[0].transform\n first_vehicle_transform = carla.Transform(\n carla.Location(config.other_actors[0].transform.location.x,\n config.other_actors[0].transform.location.y,\n config.other_actors[0].transform.location.z - 500),\n config.other_actors[0].transform.rotation)\n first_vehicle = CarlaDataProvider.request_new_actor(config.other_actors[0].model, first_vehicle_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n After invoking this scenario, it will wait for the user\n controlled vehicle to enter the start region,\n then make a traffic participant to accelerate\n until it is going fast enough to reach an intersection point.\n at the same time as the user controlled vehicle at the junction.\n Once the user controlled vehicle comes close to the junction,\n the traffic participant accelerates and passes through the junction.\n After 60 seconds, a timeout stops the scenario.\n \"\"\"\n\n # Creating leaf nodes\n start_other_trigger = InTriggerRegion(\n self.ego_vehicles[0],\n -80, -70,\n -75, -60)\n\n sync_arrival = SyncArrival(\n self.other_actors[0], self.ego_vehicles[0],\n carla.Location(x=-74.63, y=-136.34))\n\n pass_through_trigger = InTriggerRegion(\n self.ego_vehicles[0],\n -90, -70,\n -124, -119)\n\n keep_velocity_other = KeepVelocity(\n self.other_actors[0],\n self._other_actor_target_velocity)\n\n stop_other_trigger = InTriggerRegion(\n self.other_actors[0],\n -45, -35,\n -140, -130)\n\n stop_other = StopVehicle(\n self.other_actors[0],\n self._other_actor_max_brake)\n\n end_condition = InTriggerRegion(\n self.ego_vehicles[0],\n -90, -70,\n -170, -156\n )\n\n # Creating non-leaf nodes\n root = py_trees.composites.Sequence()\n scenario_sequence = py_trees.composites.Sequence()\n sync_arrival_parallel = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n keep_velocity_other_parallel = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n # Building tree\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform))\n scenario_sequence.add_child(start_other_trigger)\n scenario_sequence.add_child(sync_arrival_parallel)\n scenario_sequence.add_child(keep_velocity_other_parallel)\n scenario_sequence.add_child(stop_other)\n scenario_sequence.add_child(end_condition)\n scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n sync_arrival_parallel.add_child(sync_arrival)\n sync_arrival_parallel.add_child(pass_through_trigger)\n keep_velocity_other_parallel.add_child(keep_velocity_other)\n keep_velocity_other_parallel.add_child(stop_other_trigger)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collison_criteria = CollisionTest(self.ego_vehicles[0])\n criteria.append(collison_criteria)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/object_crash_intersection.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\"\"\"\nObject crash with prior vehicle action scenario:\nThe scenario realizes the user controlled ego vehicle\nmoving along the road and encounters a cyclist ahead after taking a right or left turn.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport math\nimport py_trees\n\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n KeepVelocity,\n HandBrakeVehicle)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToLocationAlongRoute,\n InTriggerDistanceToVehicle,\n DriveDistance)\nfrom srunner.scenariomanager.timer import TimeOut\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import generate_target_waypoint, generate_target_waypoint_in_route\n\n\ndef get_opponent_transform(added_dist, waypoint, trigger_location):\n \"\"\"\n Calculate the transform of the adversary\n \"\"\"\n lane_width = waypoint.lane_width\n\n offset = {\"orientation\": 270, \"position\": 90, \"k\": 1.0}\n _wp = waypoint.next(added_dist)\n if _wp:\n _wp = _wp[-1]\n else:\n raise RuntimeError(\"Cannot get next waypoint !\")\n\n location = _wp.transform.location\n orientation_yaw = _wp.transform.rotation.yaw + offset[\"orientation\"]\n position_yaw = _wp.transform.rotation.yaw + offset[\"position\"]\n\n offset_location = carla.Location(\n offset['k'] * lane_width * math.cos(math.radians(position_yaw)),\n offset['k'] * lane_width * math.sin(math.radians(position_yaw)))\n location += offset_location\n location.z = trigger_location.z\n transform = carla.Transform(location, carla.Rotation(yaw=orientation_yaw))\n\n return transform\n\n\ndef get_right_driving_lane(waypoint):\n \"\"\"\n Gets the driving / parking lane that is most to the right of the waypoint\n as well as the number of lane changes done\n \"\"\"\n lane_changes = 0\n\n while True:\n wp_next = waypoint.get_right_lane()\n lane_changes += 1\n\n if wp_next is None or wp_next.lane_type == carla.LaneType.Sidewalk:\n break\n elif wp_next.lane_type == carla.LaneType.Shoulder:\n # Filter Parkings considered as Shoulders\n if is_lane_a_parking(wp_next):\n lane_changes += 1\n waypoint = wp_next\n break\n else:\n waypoint = wp_next\n\n return waypoint, lane_changes\n\n\ndef is_lane_a_parking(waypoint):\n \"\"\"\n This function filters false negative Shoulder which are in reality Parking lanes.\n These are differentiated from the others because, similar to the driving lanes,\n they have, on the right, a small Shoulder followed by a Sidewalk.\n \"\"\"\n\n # Parking are wide lanes\n if waypoint.lane_width > 2:\n wp_next = waypoint.get_right_lane()\n\n # That are next to a mini-Shoulder\n if wp_next is not None and wp_next.lane_type == carla.LaneType.Shoulder:\n wp_next_next = wp_next.get_right_lane()\n\n # Followed by a Sidewalk\n if wp_next_next is not None and wp_next_next.lane_type == carla.LaneType.Sidewalk:\n return True\n\n return False\n\n\nclass VehicleTurningRight(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple object crash\n with prior vehicle action involving a vehicle and a cyclist.\n The ego vehicle is passing through a road and encounters\n a cyclist after taking a right turn. (Traffic Scenario 4)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n\n self._other_actor_target_velocity = 10\n self._wmap = CarlaDataProvider.get_map()\n self._reference_waypoint = self._wmap.get_waypoint(config.trigger_points[0].location)\n self._trigger_location = config.trigger_points[0].location\n self._other_actor_transform = None\n self._num_lane_changes = 0\n # Timeout of scenario in seconds\n self.timeout = timeout\n # Total Number of attempts to relocate a vehicle before spawning\n self._number_of_attempts = 6\n # Number of attempts made so far\n self._spawn_attempted = 0\n\n self._ego_route = CarlaDataProvider.get_ego_vehicle_route()\n\n super(VehicleTurningRight, self).__init__(\"VehicleTurningRight\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n # Get the waypoint right after the junction\n waypoint = generate_target_waypoint(self._reference_waypoint, 1)\n\n # Move a certain distance to the front\n start_distance = 8\n waypoint = waypoint.next(start_distance)[0]\n\n # Get the last driving lane to the right\n waypoint, self._num_lane_changes = get_right_driving_lane(waypoint)\n # And for synchrony purposes, move to the front a bit\n added_dist = self._num_lane_changes\n\n while True:\n\n # Try to spawn the actor\n try:\n self._other_actor_transform = get_opponent_transform(added_dist, waypoint, self._trigger_location)\n first_vehicle = CarlaDataProvider.request_new_actor(\n 'vehicle.diamondback.century', self._other_actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n break\n\n # Move the spawning point a bit and try again\n except RuntimeError as r:\n # In the case there is an object just move a little bit and retry\n print(\" Base transform is blocking objects \", self._other_actor_transform)\n added_dist += 0.5\n self._spawn_attempted += 1\n if self._spawn_attempted >= self._number_of_attempts:\n raise r\n\n # Set the transform to -500 z after we are able to spawn it\n actor_transform = carla.Transform(\n carla.Location(self._other_actor_transform.location.x,\n self._other_actor_transform.location.y,\n self._other_actor_transform.location.z - 500),\n self._other_actor_transform.rotation)\n first_vehicle.set_transform(actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n After invoking this scenario, cyclist will wait for the user\n controlled vehicle to enter the in the trigger distance region,\n the cyclist starts crossing the road once the condition meets,\n ego vehicle has to avoid the crash after a right turn, but\n continue driving after the road is clear.If this does not happen\n within 90 seconds, a timeout stops the scenario.\n \"\"\"\n\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"IntersectionRightTurn\")\n\n lane_width = self._reference_waypoint.lane_width\n dist_to_travel = lane_width + (1.10 * lane_width * self._num_lane_changes)\n\n bycicle_start_dist = 13 + dist_to_travel\n\n if self._ego_route is not None:\n trigger_distance = InTriggerDistanceToLocationAlongRoute(self.ego_vehicles[0],\n self._ego_route,\n self._other_actor_transform.location,\n bycicle_start_dist)\n else:\n trigger_distance = InTriggerDistanceToVehicle(self.other_actors[0],\n self.ego_vehicles[0],\n bycicle_start_dist)\n\n actor_velocity = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n actor_traverse = DriveDistance(self.other_actors[0], 0.30 * dist_to_travel)\n post_timer_velocity_actor = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n post_timer_traverse_actor = DriveDistance(self.other_actors[0], 0.70 * dist_to_travel)\n end_condition = TimeOut(5)\n\n # non leaf nodes\n scenario_sequence = py_trees.composites.Sequence()\n\n actor_ego_sync = py_trees.composites.Parallel(\n \"Synchronization of actor and ego vehicle\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n after_timer_actor = py_trees.composites.Parallel(\n \"After timeout actor will cross the remaining lane_width\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n # building the tree\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform,\n name='TransformSetterTS4'))\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], True))\n scenario_sequence.add_child(trigger_distance)\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], False))\n scenario_sequence.add_child(actor_ego_sync)\n scenario_sequence.add_child(after_timer_actor)\n scenario_sequence.add_child(end_condition)\n scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n actor_ego_sync.add_child(actor_velocity)\n actor_ego_sync.add_child(actor_traverse)\n\n after_timer_actor.add_child(post_timer_velocity_actor)\n after_timer_actor.add_child(post_timer_traverse_actor)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n\n\nclass VehicleTurningLeft(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple object crash\n with prior vehicle action involving a vehicle and a cyclist.\n The ego vehicle is passing through a road and encounters\n a cyclist after taking a left turn. (Traffic Scenario 4)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n\n self._other_actor_target_velocity = 10\n self._wmap = CarlaDataProvider.get_map()\n self._reference_waypoint = self._wmap.get_waypoint(config.trigger_points[0].location)\n self._trigger_location = config.trigger_points[0].location\n self._other_actor_transform = None\n self._num_lane_changes = 0\n # Timeout of scenario in seconds\n self.timeout = timeout\n # Total Number of attempts to relocate a vehicle before spawning\n self._number_of_attempts = 6\n # Number of attempts made so far\n self._spawn_attempted = 0\n\n self._ego_route = CarlaDataProvider.get_ego_vehicle_route()\n\n super(VehicleTurningLeft, self).__init__(\"VehicleTurningLeft\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n # Get the waypoint right after the junction\n waypoint = generate_target_waypoint(self._reference_waypoint, -1)\n\n # Move a certain distance to the front\n start_distance = 8\n waypoint = waypoint.next(start_distance)[0]\n\n # Get the last driving lane to the right\n waypoint, self._num_lane_changes = get_right_driving_lane(waypoint)\n # And for synchrony purposes, move to the front a bit\n added_dist = self._num_lane_changes\n\n while True:\n\n # Try to spawn the actor\n try:\n self._other_actor_transform = get_opponent_transform(added_dist, waypoint, self._trigger_location)\n first_vehicle = CarlaDataProvider.request_new_actor(\n 'vehicle.diamondback.century', self._other_actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n break\n\n # Move the spawning point a bit and try again\n except RuntimeError as r:\n # In the case there is an object just move a little bit and retry\n print(\" Base transform is blocking objects \", self._other_actor_transform)\n added_dist += 0.5\n self._spawn_attempted += 1\n if self._spawn_attempted >= self._number_of_attempts:\n raise r\n\n # Set the transform to -500 z after we are able to spawn it\n actor_transform = carla.Transform(\n carla.Location(self._other_actor_transform.location.x,\n self._other_actor_transform.location.y,\n self._other_actor_transform.location.z - 500),\n self._other_actor_transform.rotation)\n first_vehicle.set_transform(actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n After invoking this scenario, cyclist will wait for the user\n controlled vehicle to enter the in the trigger distance region,\n the cyclist starts crossing the road once the condition meets,\n ego vehicle has to avoid the crash after a left turn, but\n continue driving after the road is clear.If this does not happen\n within 90 seconds, a timeout stops the scenario.\n \"\"\"\n\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"IntersectionLeftTurn\")\n\n lane_width = self._reference_waypoint.lane_width\n dist_to_travel = lane_width + (1.10 * lane_width * self._num_lane_changes)\n\n bycicle_start_dist = 13 + dist_to_travel\n\n if self._ego_route is not None:\n trigger_distance = InTriggerDistanceToLocationAlongRoute(self.ego_vehicles[0],\n self._ego_route,\n self._other_actor_transform.location,\n bycicle_start_dist)\n else:\n trigger_distance = InTriggerDistanceToVehicle(self.other_actors[0],\n self.ego_vehicles[0],\n bycicle_start_dist)\n\n actor_velocity = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n actor_traverse = DriveDistance(self.other_actors[0], 0.30 * dist_to_travel)\n post_timer_velocity_actor = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n post_timer_traverse_actor = DriveDistance(self.other_actors[0], 0.70 * dist_to_travel)\n end_condition = TimeOut(5)\n\n # non leaf nodes\n scenario_sequence = py_trees.composites.Sequence()\n\n actor_ego_sync = py_trees.composites.Parallel(\n \"Synchronization of actor and ego vehicle\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n after_timer_actor = py_trees.composites.Parallel(\n \"After timeout actor will cross the remaining lane_width\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n # building the tree\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform,\n name='TransformSetterTS4'))\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], True))\n scenario_sequence.add_child(trigger_distance)\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], False))\n scenario_sequence.add_child(actor_ego_sync)\n scenario_sequence.add_child(after_timer_actor)\n scenario_sequence.add_child(end_condition)\n scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n actor_ego_sync.add_child(actor_velocity)\n actor_ego_sync.add_child(actor_traverse)\n\n after_timer_actor.add_child(post_timer_velocity_actor)\n after_timer_actor.add_child(post_timer_traverse_actor)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n\n\nclass VehicleTurningRoute(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple object crash\n with prior vehicle action involving a vehicle and a cyclist.\n The ego vehicle is passing through a road and encounters\n a cyclist after taking a turn. This is the version used when the ego vehicle\n is following a given route. (Traffic Scenario 4)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n\n self._other_actor_target_velocity = 10\n self._wmap = CarlaDataProvider.get_map()\n self._reference_waypoint = self._wmap.get_waypoint(config.trigger_points[0].location)\n self._trigger_location = config.trigger_points[0].location\n self._other_actor_transform = None\n self._num_lane_changes = 0\n # Timeout of scenario in seconds\n self.timeout = timeout\n # Total Number of attempts to relocate a vehicle before spawning\n self._number_of_attempts = 6\n # Number of attempts made so far\n self._spawn_attempted = 0\n\n self._ego_route = CarlaDataProvider.get_ego_vehicle_route()\n\n super(VehicleTurningRoute, self).__init__(\"VehicleTurningRoute\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n\n # Get the waypoint right after the junction\n waypoint = generate_target_waypoint_in_route(self._reference_waypoint, self._ego_route)\n\n # Move a certain distance to the front\n start_distance = 8\n waypoint = waypoint.next(start_distance)[0]\n\n # Get the last driving lane to the right\n waypoint, self._num_lane_changes = get_right_driving_lane(waypoint)\n # And for synchrony purposes, move to the front a bit\n added_dist = self._num_lane_changes\n\n while True:\n\n # Try to spawn the actor\n try:\n self._other_actor_transform = get_opponent_transform(added_dist, waypoint, self._trigger_location)\n first_vehicle = CarlaDataProvider.request_new_actor(\n 'vehicle.diamondback.century', self._other_actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n break\n\n # Move the spawning point a bit and try again\n except RuntimeError as r:\n # In the case there is an object just move a little bit and retry\n print(\" Base transform is blocking objects \", self._other_actor_transform)\n added_dist += 0.5\n self._spawn_attempted += 1\n if self._spawn_attempted >= self._number_of_attempts:\n raise r\n\n # Set the transform to -500 z after we are able to spawn it\n actor_transform = carla.Transform(\n carla.Location(self._other_actor_transform.location.x,\n self._other_actor_transform.location.y,\n self._other_actor_transform.location.z - 500),\n self._other_actor_transform.rotation)\n first_vehicle.set_transform(actor_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n After invoking this scenario, cyclist will wait for the user\n controlled vehicle to enter the in the trigger distance region,\n the cyclist starts crossing the road once the condition meets,\n ego vehicle has to avoid the crash after a turn, but\n continue driving after the road is clear.If this does not happen\n within 90 seconds, a timeout stops the scenario.\n \"\"\"\n\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"IntersectionRouteTurn\")\n\n lane_width = self._reference_waypoint.lane_width\n dist_to_travel = lane_width + (1.10 * lane_width * self._num_lane_changes)\n\n bycicle_start_dist = 13 + dist_to_travel\n\n if self._ego_route is not None:\n trigger_distance = InTriggerDistanceToLocationAlongRoute(self.ego_vehicles[0],\n self._ego_route,\n self._other_actor_transform.location,\n bycicle_start_dist)\n else:\n trigger_distance = InTriggerDistanceToVehicle(self.other_actors[0],\n self.ego_vehicles[0],\n bycicle_start_dist)\n\n actor_velocity = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n actor_traverse = DriveDistance(self.other_actors[0], 0.30 * dist_to_travel)\n post_timer_velocity_actor = KeepVelocity(self.other_actors[0], self._other_actor_target_velocity)\n post_timer_traverse_actor = DriveDistance(self.other_actors[0], 0.70 * dist_to_travel)\n end_condition = TimeOut(5)\n\n # non leaf nodes\n scenario_sequence = py_trees.composites.Sequence()\n\n actor_ego_sync = py_trees.composites.Parallel(\n \"Synchronization of actor and ego vehicle\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n after_timer_actor = py_trees.composites.Parallel(\n \"After timeout actor will cross the remaining lane_width\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n # building the tree\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform,\n name='TransformSetterTS4'))\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], True))\n scenario_sequence.add_child(trigger_distance)\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], False))\n scenario_sequence.add_child(actor_ego_sync)\n scenario_sequence.add_child(after_timer_actor)\n scenario_sequence.add_child(end_condition)\n scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n actor_ego_sync.add_child(actor_velocity)\n actor_ego_sync.add_child(actor_traverse)\n\n after_timer_actor.add_child(post_timer_velocity_actor)\n after_timer_actor.add_child(post_timer_traverse_actor)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n\n criteria.append(collision_criterion)\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/object_crash_vehicle.py", "content": "#!/usr/bin/env python\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nObject crash without prior vehicle action scenario:\nThe scenario realizes the user controlled ego vehicle\nmoving along the road and encountering a cyclist ahead.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport math\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n AccelerateToVelocity,\n HandBrakeVehicle,\n KeepVelocity,\n StopVehicle)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToLocationAlongRoute,\n InTimeToArrivalToVehicle,\n DriveDistance)\nfrom srunner.scenariomanager.timer import TimeOut\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_location_in_distance_from_wp\n\n\nclass StationaryObjectCrossing(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple object crash\n without prior vehicle action involving a vehicle and a cyclist.\n The ego vehicle is passing through a road and encounters\n a stationary cyclist.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._wmap = CarlaDataProvider.get_map()\n self._reference_waypoint = self._wmap.get_waypoint(config.trigger_points[0].location)\n # ego vehicle parameters\n self._ego_vehicle_distance_driven = 40\n\n # other vehicle parameters\n self._other_actor_target_velocity = 10\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(StationaryObjectCrossing, self).__init__(\"Stationaryobjectcrossing\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n _start_distance = 40\n lane_width = self._reference_waypoint.lane_width\n location, _ = get_location_in_distance_from_wp(self._reference_waypoint, _start_distance)\n waypoint = self._wmap.get_waypoint(location)\n offset = {\"orientation\": 270, \"position\": 90, \"z\": 0.4, \"k\": 0.2}\n position_yaw = waypoint.transform.rotation.yaw + offset['position']\n orientation_yaw = waypoint.transform.rotation.yaw + offset['orientation']\n offset_location = carla.Location(\n offset['k'] * lane_width * math.cos(math.radians(position_yaw)),\n offset['k'] * lane_width * math.sin(math.radians(position_yaw)))\n location += offset_location\n location.z += offset['z']\n self.transform = carla.Transform(location, carla.Rotation(yaw=orientation_yaw))\n static = CarlaDataProvider.request_new_actor('static.prop.container', self.transform)\n static.set_simulate_physics(True)\n self.other_actors.append(static)\n\n def _create_behavior(self):\n \"\"\"\n Only behavior here is to wait\n \"\"\"\n lane_width = self.ego_vehicles[0].get_world().get_map().get_waypoint(\n self.ego_vehicles[0].get_location()).lane_width\n lane_width = lane_width + (1.25 * lane_width)\n\n # leaf nodes\n actor_stand = TimeOut(15)\n actor_removed = ActorDestroy(self.other_actors[0])\n end_condition = DriveDistance(self.ego_vehicles[0], self._ego_vehicle_distance_driven)\n\n # non leaf nodes\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n scenario_sequence = py_trees.composites.Sequence()\n\n # building tree\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self.transform))\n scenario_sequence.add_child(actor_stand)\n scenario_sequence.add_child(actor_removed)\n scenario_sequence.add_child(end_condition)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n\n\nclass DynamicObjectCrossing(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple object crash\n without prior vehicle action involving a vehicle and a cyclist/pedestrian,\n The ego vehicle is passing through a road,\n And encounters a cyclist/pedestrian crossing the road.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False,\n debug_mode=False, criteria_enable=True, adversary_type=False, timeout=60):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._wmap = CarlaDataProvider.get_map()\n\n self._reference_waypoint = self._wmap.get_waypoint(config.trigger_points[0].location)\n # ego vehicle parameters\n self._ego_vehicle_distance_driven = 40\n # other vehicle parameters\n self._other_actor_target_velocity = 5\n self._other_actor_max_brake = 1.0\n self._time_to_reach = 10\n self._adversary_type = adversary_type # flag to select either pedestrian (False) or cyclist (True)\n self._walker_yaw = 0\n self._num_lane_changes = 1\n self.transform = None\n self.transform2 = None\n self.timeout = timeout\n self._trigger_location = config.trigger_points[0].location\n # Total Number of attempts to relocate a vehicle before spawning\n self._number_of_attempts = 20\n # Number of attempts made so far\n self._spawn_attempted = 0\n\n self._ego_route = CarlaDataProvider.get_ego_vehicle_route()\n\n super(DynamicObjectCrossing, self).__init__(\"DynamicObjectCrossing\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _calculate_base_transform(self, _start_distance, waypoint):\n\n lane_width = waypoint.lane_width\n\n # Patches false junctions\n if self._reference_waypoint.is_junction:\n stop_at_junction = False\n else:\n stop_at_junction = True\n\n location, _ = get_location_in_distance_from_wp(waypoint, _start_distance, stop_at_junction)\n waypoint = self._wmap.get_waypoint(location)\n offset = {\"orientation\": 270, \"position\": 90, \"z\": 0.6, \"k\": 1.0}\n position_yaw = waypoint.transform.rotation.yaw + offset['position']\n orientation_yaw = waypoint.transform.rotation.yaw + offset['orientation']\n offset_location = carla.Location(\n offset['k'] * lane_width * math.cos(math.radians(position_yaw)),\n offset['k'] * lane_width * math.sin(math.radians(position_yaw)))\n location += offset_location\n location.z = self._trigger_location.z + offset['z']\n return carla.Transform(location, carla.Rotation(yaw=orientation_yaw)), orientation_yaw\n\n def _spawn_adversary(self, transform, orientation_yaw):\n\n self._time_to_reach *= self._num_lane_changes\n\n if self._adversary_type is False:\n self._walker_yaw = orientation_yaw\n self._other_actor_target_velocity = 3 + (0.4 * self._num_lane_changes)\n walker = CarlaDataProvider.request_new_actor('walker.*', transform)\n adversary = walker\n else:\n self._other_actor_target_velocity = self._other_actor_target_velocity * self._num_lane_changes\n first_vehicle = CarlaDataProvider.request_new_actor('vehicle.diamondback.century', transform)\n first_vehicle.set_simulate_physics(enabled=False)\n adversary = first_vehicle\n\n return adversary\n\n def _spawn_blocker(self, transform, orientation_yaw):\n \"\"\"\n Spawn the blocker prop that blocks the vision from the egovehicle of the jaywalker\n :return:\n \"\"\"\n # static object transform\n shift = 0.9\n x_ego = self._reference_waypoint.transform.location.x\n y_ego = self._reference_waypoint.transform.location.y\n x_cycle = transform.location.x\n y_cycle = transform.location.y\n x_static = x_ego + shift * (x_cycle - x_ego)\n y_static = y_ego + shift * (y_cycle - y_ego)\n\n spawn_point_wp = self.ego_vehicles[0].get_world().get_map().get_waypoint(transform.location)\n\n self.transform2 = carla.Transform(carla.Location(x_static, y_static,\n spawn_point_wp.transform.location.z + 0.3),\n carla.Rotation(yaw=orientation_yaw + 180))\n\n static = CarlaDataProvider.request_new_actor('static.prop.vendingmachine', self.transform2)\n static.set_simulate_physics(enabled=False)\n\n return static\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n # cyclist transform\n _start_distance = 12\n # We start by getting and waypoint in the closest sidewalk.\n waypoint = self._reference_waypoint\n while True:\n wp_next = waypoint.get_right_lane()\n self._num_lane_changes += 1\n if wp_next is None or wp_next.lane_type == carla.LaneType.Sidewalk:\n break\n elif wp_next.lane_type == carla.LaneType.Shoulder:\n # Filter Parkings considered as Shoulders\n if wp_next.lane_width > 2:\n _start_distance += 1.5\n waypoint = wp_next\n break\n else:\n _start_distance += 1.5\n waypoint = wp_next\n\n while True: # We keep trying to spawn avoiding props\n\n try:\n self.transform, orientation_yaw = self._calculate_base_transform(_start_distance, waypoint)\n first_vehicle = self._spawn_adversary(self.transform, orientation_yaw)\n\n blocker = self._spawn_blocker(self.transform, orientation_yaw)\n\n break\n except RuntimeError as r:\n # We keep retrying until we spawn\n print(\"Base transform is blocking objects \", self.transform)\n _start_distance += 0.4\n self._spawn_attempted += 1\n if self._spawn_attempted >= self._number_of_attempts:\n raise r\n\n # Now that we found a possible position we just put the vehicle to the underground\n disp_transform = carla.Transform(\n carla.Location(self.transform.location.x,\n self.transform.location.y,\n self.transform.location.z - 500),\n self.transform.rotation)\n\n prop_disp_transform = carla.Transform(\n carla.Location(self.transform2.location.x,\n self.transform2.location.y,\n self.transform2.location.z - 500),\n self.transform2.rotation)\n\n first_vehicle.set_transform(disp_transform)\n blocker.set_transform(prop_disp_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n blocker.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n self.other_actors.append(blocker)\n\n def _create_behavior(self):\n \"\"\"\n After invoking this scenario, cyclist will wait for the user\n controlled vehicle to enter trigger distance region,\n the cyclist starts crossing the road once the condition meets,\n then after 60 seconds, a timeout stops the scenario\n \"\"\"\n\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"OccludedObjectCrossing\")\n lane_width = self._reference_waypoint.lane_width\n lane_width = lane_width + (1.25 * lane_width * self._num_lane_changes)\n\n dist_to_trigger = 12 + self._num_lane_changes\n # leaf nodes\n if self._ego_route is not None:\n start_condition = InTriggerDistanceToLocationAlongRoute(self.ego_vehicles[0],\n self._ego_route,\n self.transform.location,\n dist_to_trigger)\n else:\n start_condition = InTimeToArrivalToVehicle(self.ego_vehicles[0],\n self.other_actors[0],\n self._time_to_reach)\n\n actor_velocity = KeepVelocity(self.other_actors[0],\n self._other_actor_target_velocity,\n name=\"walker velocity\")\n actor_drive = DriveDistance(self.other_actors[0],\n 0.5 * lane_width,\n name=\"walker drive distance\")\n actor_start_cross_lane = AccelerateToVelocity(self.other_actors[0],\n 1.0,\n self._other_actor_target_velocity,\n name=\"walker crossing lane accelerate velocity\")\n actor_cross_lane = DriveDistance(self.other_actors[0],\n lane_width,\n name=\"walker drive distance for lane crossing \")\n actor_stop_crossed_lane = StopVehicle(self.other_actors[0],\n self._other_actor_max_brake,\n name=\"walker stop\")\n ego_pass_machine = DriveDistance(self.ego_vehicles[0],\n 5,\n name=\"ego vehicle passed prop\")\n actor_remove = ActorDestroy(self.other_actors[0],\n name=\"Destroying walker\")\n static_remove = ActorDestroy(self.other_actors[1],\n name=\"Destroying Prop\")\n end_condition = DriveDistance(self.ego_vehicles[0],\n self._ego_vehicle_distance_driven,\n name=\"End condition ego drive distance\")\n\n # non leaf nodes\n\n scenario_sequence = py_trees.composites.Sequence()\n keep_velocity_other = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"keep velocity other\")\n keep_velocity = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"keep velocity\")\n\n # building tree\n\n root.add_child(scenario_sequence)\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self.transform,\n name='TransformSetterTS3walker'))\n scenario_sequence.add_child(ActorTransformSetter(self.other_actors[1], self.transform2,\n name='TransformSetterTS3coca', physics=False))\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], True))\n scenario_sequence.add_child(start_condition)\n scenario_sequence.add_child(HandBrakeVehicle(self.other_actors[0], False))\n scenario_sequence.add_child(keep_velocity)\n scenario_sequence.add_child(keep_velocity_other)\n scenario_sequence.add_child(actor_stop_crossed_lane)\n scenario_sequence.add_child(actor_remove)\n scenario_sequence.add_child(static_remove)\n scenario_sequence.add_child(end_condition)\n\n keep_velocity.add_child(actor_velocity)\n keep_velocity.add_child(actor_drive)\n keep_velocity_other.add_child(actor_start_cross_lane)\n keep_velocity_other.add_child(actor_cross_lane)\n keep_velocity_other.add_child(ego_pass_machine)\n\n return root\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/open_scenario.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nBasic scenario class using the OpenSCENARIO definition\n\"\"\"\n\nfrom __future__ import print_function\n\nimport itertools\nimport py_trees\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import ChangeWeather, ChangeRoadFriction\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import ChangeActorControl, ChangeActorTargetSpeed\nfrom srunner.scenariomanager.timer import GameTime\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.openscenario_parser import OpenScenarioParser\nfrom srunner.tools.py_trees_port import Decorator, oneshot_behavior\n\n\ndef repeatable_behavior(behaviour, name=None):\n \"\"\"\n This behaviour allows a composite with oneshot ancestors to run multiple\n times, resetting the oneshot variables after each execution\n \"\"\"\n if not name:\n name = behaviour.name\n clear_descendant_variables = ClearBlackboardVariablesStartingWith(\n name=\"Clear Descendant Variables of {}\".format(name),\n variable_name_beginning=name + \">\"\n )\n # If it's a sequence, don't double-nest it in a redundant manner\n if isinstance(behaviour, py_trees.composites.Sequence):\n behaviour.add_child(clear_descendant_variables)\n sequence = behaviour\n else:\n sequence = py_trees.composites.Sequence(name=\"RepeatableBehaviour of {}\".format(name))\n sequence.add_children([behaviour, clear_descendant_variables])\n return sequence\n\n\nclass ClearBlackboardVariablesStartingWith(py_trees.behaviours.Success):\n\n \"\"\"\n Clear the values starting with the specified string from the blackboard.\n\n Args:\n name (:obj:`str`): name of the behaviour\n variable_name_beginning (:obj:`str`): beginning of the names of variable to clear\n \"\"\"\n\n def __init__(self,\n name=\"Clear Blackboard Variable Starting With\",\n variable_name_beginning=\"dummy\",\n ):\n super(ClearBlackboardVariablesStartingWith, self).__init__(name)\n self.variable_name_beginning = variable_name_beginning\n\n def initialise(self):\n \"\"\"\n Delete the variables from the blackboard.\n \"\"\"\n blackboard_variables = [key for key, _ in py_trees.blackboard.Blackboard().__dict__.items(\n ) if key.startswith(self.variable_name_beginning)]\n for variable in blackboard_variables:\n delattr(py_trees.blackboard.Blackboard(), variable)\n\n\nclass StoryElementStatusToBlackboard(Decorator):\n\n \"\"\"\n Reflect the status of the decorator's child story element to the blackboard.\n\n Args:\n child: the child behaviour or subtree\n story_element_type: the element type [act,scene,maneuver,event,action]\n element_name: the story element's name attribute\n \"\"\"\n\n def __init__(self, child, story_element_type, element_name):\n super(StoryElementStatusToBlackboard, self).__init__(name=child.name, child=child)\n self.story_element_type = story_element_type\n self.element_name = element_name\n self.blackboard = py_trees.blackboard.Blackboard()\n\n def initialise(self):\n \"\"\"\n Record the elements's start time on the blackboard\n \"\"\"\n self.blackboard.set(\n name=\"({}){}-{}\".format(self.story_element_type.upper(),\n self.element_name, \"START\"),\n value=GameTime.get_time(),\n overwrite=True\n )\n\n def update(self):\n \"\"\"\n Reflect the decorated child's status\n Returns: the decorated child's status\n \"\"\"\n return self.decorated.status\n\n def terminate(self, new_status):\n \"\"\"\n Terminate and mark Blackboard entry with END\n \"\"\"\n # Report whether we ended with End or Cancel\n # If we were ended or cancelled, our state will be INVALID and\n # We will have an ancestor (a parallel SUCCESS_ON_ALL) with a successful child/children\n # It's possible we ENDed AND CANCELled if both condition groups were true simultaneously\n # NOTE 'py_trees.common.Status.INVALID' is the status of a behaviur which was terminated by a parent\n rules = []\n if new_status == py_trees.common.Status.INVALID:\n # We were terminated from above unnaturally\n # Figure out if were ended or cancelled\n terminating_ancestor = self.parent\n while terminating_ancestor.status == py_trees.common.Status.INVALID:\n terminating_ancestor = terminating_ancestor.parent\n # We have found an ancestory which was not terminated by a parent\n # Check what caused it to terminate its children\n if terminating_ancestor.status == py_trees.common.Status.SUCCESS:\n successful_children = [\n child.name\n for child\n in terminating_ancestor.children\n if child.status == py_trees.common.Status.SUCCESS]\n if \"StopTrigger\" in successful_children:\n rules.append(\"END\")\n\n # END is the default status unless we have a more detailed one\n rules = rules or [\"END\"]\n\n for rule in rules:\n self.blackboard.set(\n name=\"({}){}-{}\".format(self.story_element_type.upper(),\n self.element_name, rule),\n value=GameTime.get_time(),\n overwrite=True\n )\n\n\ndef get_xml_path(tree, node):\n \"\"\"\n Extract the full path of a node within an XML tree\n\n Note: Catalogs are pulled from a separate file so the XML tree is split.\n This means that in order to get the XML path, it must be done in 2 steps.\n Some places in this python script do that by concatenating the results\n of 2 get_xml_path calls with another \">\".\n Example: \"Behavior>AutopilotSequence\" + \">\" + \"StartAutopilot>StartAutopilot>StartAutopilot\"\n \"\"\"\n\n path = \"\"\n parent_map = {c: p for p in tree.iter() for c in p}\n\n cur_node = node\n while cur_node != tree:\n path = \"{}>{}\".format(cur_node.attrib.get('name'), path)\n cur_node = parent_map[cur_node]\n\n path = path[:-1]\n return path\n\n\nclass OpenScenario(BasicScenario):\n\n \"\"\"\n Implementation of the OpenSCENARIO scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, config_file, debug_mode=False, criteria_enable=True, timeout=300):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self.config = config\n self.route = None\n self.config_file = config_file\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(OpenScenario, self).__init__(\"OpenScenario\", ego_vehicles=ego_vehicles, config=config,\n world=world, debug_mode=debug_mode,\n terminate_on_failure=False, criteria_enable=criteria_enable)\n\n def _initialize_environment(self, world):\n \"\"\"\n Initialization of weather and road friction.\n \"\"\"\n pass\n\n def _create_environment_behavior(self):\n # Set the appropriate weather conditions\n\n env_behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"EnvironmentBehavior\")\n\n weather_update = ChangeWeather(\n OpenScenarioParser.get_weather_from_env_action(self.config.init, self.config.catalogs))\n road_friction = ChangeRoadFriction(\n OpenScenarioParser.get_friction_from_env_action(self.config.init, self.config.catalogs))\n env_behavior.add_child(oneshot_behavior(variable_name=\"InitialWeather\", behaviour=weather_update))\n env_behavior.add_child(oneshot_behavior(variable_name=\"InitRoadFriction\", behaviour=road_friction))\n\n return env_behavior\n\n def _create_init_behavior(self):\n\n init_behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"InitBehaviour\")\n\n for actor in self.config.other_actors + self.config.ego_vehicles:\n for carla_actor in self.other_actors + self.ego_vehicles:\n if 'role_name' in carla_actor.attributes and carla_actor.attributes['role_name'] == actor.rolename:\n actor_init_behavior = py_trees.composites.Sequence(name=\"InitActor{}\".format(actor.rolename))\n\n controller_atomic = None\n\n for private in self.config.init.iter(\"Private\"):\n if private.attrib.get('entityRef', None) == actor.rolename:\n for private_action in private.iter(\"PrivateAction\"):\n for controller_action in private_action.iter('ControllerAction'):\n module, args = OpenScenarioParser.get_controller(\n controller_action, self.config.catalogs)\n controller_atomic = ChangeActorControl(\n carla_actor, control_py_module=module, args=args)\n\n if controller_atomic is None:\n controller_atomic = ChangeActorControl(carla_actor, control_py_module=None, args={})\n\n actor_init_behavior.add_child(controller_atomic)\n\n if actor.speed > 0:\n actor_init_behavior.add_child(ChangeActorTargetSpeed(carla_actor, actor.speed, init_speed=True))\n\n init_behavior.add_child(actor_init_behavior)\n break\n\n return init_behavior\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n\n story_behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"Story\")\n\n joint_actor_list = self.other_actors + self.ego_vehicles + [None]\n\n for act in self.config.story.iter(\"Act\"):\n\n act_sequence = py_trees.composites.Sequence(\n name=\"Act StartConditions and behaviours\")\n\n start_conditions = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"StartConditions Group\")\n\n parallel_behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE, name=\"Maneuver + EndConditions Group\")\n\n parallel_sequences = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"Maneuvers\")\n\n for sequence in act.iter(\"ManeuverGroup\"):\n sequence_behavior = py_trees.composites.Sequence(name=sequence.attrib.get('name'))\n repetitions = sequence.attrib.get('maximumExecutionCount', 1)\n\n for _ in range(int(repetitions)):\n\n actor_ids = []\n for actor in sequence.iter(\"Actors\"):\n for entity in actor.iter(\"EntityRef\"):\n entity_name = entity.attrib.get('entityRef', None)\n for k, _ in enumerate(joint_actor_list):\n if joint_actor_list[k] and entity_name == joint_actor_list[k].attributes['role_name']:\n actor_ids.append(k)\n break\n\n if not actor_ids:\n print(\"Warning: Maneuvergroup {} does not use reference actors!\".format(\n sequence.attrib.get('name')))\n actor_ids.append(len(joint_actor_list) - 1)\n\n # Collect catalog reference maneuvers in order to process them at the same time as normal maneuvers\n catalog_maneuver_list = []\n for catalog_reference in sequence.iter(\"CatalogReference\"):\n catalog_maneuver = OpenScenarioParser.get_catalog_entry(self.config.catalogs, catalog_reference)\n catalog_maneuver_list.append(catalog_maneuver)\n all_maneuvers = itertools.chain(iter(catalog_maneuver_list), sequence.iter(\"Maneuver\"))\n single_sequence_iteration = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=sequence_behavior.name)\n for maneuver in all_maneuvers: # Iterates through both CatalogReferences and Maneuvers\n maneuver_parallel = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL,\n name=\"Maneuver \" + maneuver.attrib.get('name'))\n for event in maneuver.iter(\"Event\"):\n event_sequence = py_trees.composites.Sequence(\n name=\"Event \" + event.attrib.get('name'))\n parallel_actions = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"Actions\")\n for child in event.iter():\n if child.tag == \"Action\":\n for actor_id in actor_ids:\n maneuver_behavior = OpenScenarioParser.convert_maneuver_to_atomic(\n child, joint_actor_list[actor_id], self.config.catalogs)\n maneuver_behavior = StoryElementStatusToBlackboard(\n maneuver_behavior, \"ACTION\", child.attrib.get('name'))\n parallel_actions.add_child(\n oneshot_behavior(variable_name=# See note in get_xml_path\n get_xml_path(self.config.story, sequence) + '>' + \\\n get_xml_path(maneuver, child),\n behaviour=maneuver_behavior))\n\n if child.tag == \"StartTrigger\":\n # There is always one StartConditions block per Event\n parallel_condition_groups = self._create_condition_container(\n child, \"Parallel Condition Groups\", sequence, maneuver)\n event_sequence.add_child(\n parallel_condition_groups)\n\n parallel_actions = StoryElementStatusToBlackboard(\n parallel_actions, \"EVENT\", event.attrib.get('name'))\n event_sequence.add_child(parallel_actions)\n maneuver_parallel.add_child(\n oneshot_behavior(variable_name=get_xml_path(self.config.story, sequence) + '>' +\n get_xml_path(maneuver, event), # See get_xml_path\n behaviour=event_sequence))\n maneuver_parallel = StoryElementStatusToBlackboard(\n maneuver_parallel, \"MANEUVER\", maneuver.attrib.get('name'))\n single_sequence_iteration.add_child(\n oneshot_behavior(variable_name=get_xml_path(self.config.story, sequence) + '>' +\n maneuver.attrib.get('name'), # See get_xml_path\n behaviour=maneuver_parallel))\n\n # OpenSCENARIO refers to Sequences as Scenes in this instance\n single_sequence_iteration = StoryElementStatusToBlackboard(\n single_sequence_iteration, \"SCENE\", sequence.attrib.get('name'))\n single_sequence_iteration = repeatable_behavior(\n single_sequence_iteration, get_xml_path(self.config.story, sequence))\n\n sequence_behavior.add_child(single_sequence_iteration)\n\n if sequence_behavior.children:\n parallel_sequences.add_child(\n oneshot_behavior(variable_name=get_xml_path(self.config.story, sequence),\n behaviour=sequence_behavior))\n\n if parallel_sequences.children:\n parallel_sequences = StoryElementStatusToBlackboard(\n parallel_sequences, \"ACT\", act.attrib.get('name'))\n parallel_behavior.add_child(parallel_sequences)\n\n start_triggers = act.find(\"StartTrigger\")\n if list(start_triggers) is not None:\n for start_condition in start_triggers:\n parallel_start_criteria = self._create_condition_container(start_condition, \"StartConditions\")\n if parallel_start_criteria.children:\n start_conditions.add_child(parallel_start_criteria)\n end_triggers = act.find(\"StopTrigger\")\n if end_triggers is not None and list(end_triggers) is not None:\n for end_condition in end_triggers:\n parallel_end_criteria = self._create_condition_container(\n end_condition, \"EndConditions\", success_on_all=False)\n if parallel_end_criteria.children:\n parallel_behavior.add_child(parallel_end_criteria)\n\n if start_conditions.children:\n act_sequence.add_child(start_conditions)\n if parallel_behavior.children:\n act_sequence.add_child(parallel_behavior)\n\n if act_sequence.children:\n story_behavior.add_child(act_sequence)\n\n # Build behavior tree\n behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=\"behavior\")\n\n env_behavior = self._create_environment_behavior()\n if env_behavior is not None:\n behavior.add_child(oneshot_behavior(variable_name=\"InitialEnvironmentSettings\", behaviour=env_behavior))\n\n init_behavior = self._create_init_behavior()\n if init_behavior is not None:\n behavior.add_child(oneshot_behavior(variable_name=\"InitialActorSettings\", behaviour=init_behavior))\n\n behavior.add_child(story_behavior)\n\n return behavior\n\n def _create_condition_container(self, node, name='Conditions Group', sequence=None,\n maneuver=None, success_on_all=True):\n \"\"\"\n This is a generic function to handle conditions utilising ConditionGroups\n Each ConditionGroup is represented as a Sequence of Conditions\n The ConditionGroups are grouped under a SUCCESS_ON_ONE Parallel\n \"\"\"\n\n parallel_condition_groups = py_trees.composites.Parallel(name,\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n for condition_group in node.iter(\"ConditionGroup\"):\n if success_on_all:\n condition_group_sequence = py_trees.composites.Parallel(\n name=\"Condition Group\", policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n else:\n condition_group_sequence = py_trees.composites.Parallel(\n name=\"Condition Group\", policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n for condition in condition_group.iter(\"Condition\"):\n criterion = OpenScenarioParser.convert_condition_to_atomic(\n condition, self.other_actors + self.ego_vehicles)\n if sequence is not None and maneuver is not None:\n xml_path = get_xml_path(self.config.story, sequence) + '>' + \\\n get_xml_path(maneuver, condition) # See note in get_xml_path\n else:\n xml_path = get_xml_path(self.config.story, condition)\n criterion = oneshot_behavior(variable_name=xml_path, behaviour=criterion)\n condition_group_sequence.add_child(criterion)\n\n if condition_group_sequence.children:\n parallel_condition_groups.add_child(condition_group_sequence)\n\n return parallel_condition_groups\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n parallel_criteria = py_trees.composites.Parallel(\"EndConditions (Criteria Group)\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n criteria = []\n for endcondition in self.config.storyboard.iter(\"StopTrigger\"):\n for condition in endcondition.iter(\"Condition\"):\n if condition.attrib.get('name').startswith('criteria_'):\n condition.set('name', condition.attrib.get('name')[9:])\n criteria.append(condition)\n\n for condition in criteria:\n criterion = OpenScenarioParser.convert_condition_to_atomic(condition, self.ego_vehicles)\n parallel_criteria.add_child(criterion)\n\n return parallel_criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/opposite_vehicle_taking_priority.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2018-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nScenarios in which another (opposite) vehicle 'illegally' takes\npriority, e.g. by running a red traffic light.\n\"\"\"\n\nfrom __future__ import print_function\nimport sys\n\nimport py_trees\nimport carla\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n WaypointFollower,\n SyncArrival)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest, DrivenDistanceTest, MaxVelocityTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToLocation,\n InTriggerDistanceToNextIntersection,\n DriveDistance)\nfrom srunner.scenariomanager.timer import TimeOut\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import (get_crossing_point,\n get_geometric_linear_intersection,\n generate_target_waypoint_list)\n\n\nclass OppositeVehicleRunningRedLight(BasicScenario):\n\n \"\"\"\n This class holds everything required for a scenario,\n in which an other vehicle takes priority from the ego\n vehicle, by running a red traffic light (while the ego\n vehicle has green) (Traffic Scenario 7)\n\n This is a single ego vehicle scenario\n \"\"\"\n\n # ego vehicle parameters\n _ego_max_velocity_allowed = 20 # Maximum allowed velocity [m/s]\n _ego_avg_velocity_expected = 4 # Average expected velocity [m/s]\n _ego_expected_driven_distance = 70 # Expected driven distance [m]\n _ego_distance_to_traffic_light = 32 # Trigger distance to traffic light [m]\n _ego_distance_to_drive = 40 # Allowed distance to drive\n\n # other vehicle\n _other_actor_target_velocity = 10 # Target velocity of other vehicle\n _other_actor_max_brake = 1.0 # Maximum brake of other vehicle\n _other_actor_distance = 50 # Distance the other vehicle should drive\n\n _traffic_light = None\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=180):\n \"\"\"\n Setup all relevant parameters and create scenario\n and instantiate scenario manager\n \"\"\"\n\n self._other_actor_transform = None\n\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(OppositeVehicleRunningRedLight, self).__init__(\"OppositeVehicleRunningRedLight\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n self._traffic_light = CarlaDataProvider.get_next_traffic_light(self.ego_vehicles[0], False)\n\n if self._traffic_light is None:\n print(\"No traffic light for the given location of the ego vehicle found\")\n sys.exit(-1)\n\n self._traffic_light.set_state(carla.TrafficLightState.Green)\n self._traffic_light.set_green_time(self.timeout)\n\n # other vehicle's traffic light\n traffic_light_other = CarlaDataProvider.get_next_traffic_light(self.other_actors[0], False)\n\n if traffic_light_other is None:\n print(\"No traffic light for the given location of the other vehicle found\")\n sys.exit(-1)\n\n traffic_light_other.set_state(carla.TrafficLightState.Red)\n traffic_light_other.set_red_time(self.timeout)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n self._other_actor_transform = config.other_actors[0].transform\n first_vehicle_transform = carla.Transform(\n carla.Location(config.other_actors[0].transform.location.x,\n config.other_actors[0].transform.location.y,\n config.other_actors[0].transform.location.z),\n config.other_actors[0].transform.rotation)\n first_vehicle = CarlaDataProvider.request_new_actor(config.other_actors[0].model, first_vehicle_transform)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n Scenario behavior:\n The other vehicle waits until the ego vehicle is close enough to the\n intersection and that its own traffic light is red. Then, it will start\n driving and 'illegally' cross the intersection. After a short distance\n it should stop again, outside of the intersection. The ego vehicle has\n to avoid the crash, but continue driving after the intersection is clear.\n\n If this does not happen within 120 seconds, a timeout stops the scenario\n \"\"\"\n crossing_point_dynamic = get_crossing_point(self.ego_vehicles[0])\n\n # start condition\n startcondition = InTriggerDistanceToLocation(\n self.ego_vehicles[0],\n crossing_point_dynamic,\n self._ego_distance_to_traffic_light,\n name=\"Waiting for start position\")\n\n sync_arrival_parallel = py_trees.composites.Parallel(\n \"Synchronize arrival times\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n location_of_collision_dynamic = get_geometric_linear_intersection(self.ego_vehicles[0], self.other_actors[0])\n\n sync_arrival = SyncArrival(\n self.other_actors[0], self.ego_vehicles[0], location_of_collision_dynamic)\n sync_arrival_stop = InTriggerDistanceToNextIntersection(self.other_actors[0],\n 5)\n sync_arrival_parallel.add_child(sync_arrival)\n sync_arrival_parallel.add_child(sync_arrival_stop)\n\n # Generate plan for WaypointFollower\n turn = 0 # drive straight ahead\n plan = []\n\n # generating waypoints until intersection (target_waypoint)\n plan, target_waypoint = generate_target_waypoint_list(\n CarlaDataProvider.get_map().get_waypoint(self.other_actors[0].get_location()), turn)\n\n # Generating waypoint list till next intersection\n wp_choice = target_waypoint.next(5.0)\n while len(wp_choice) == 1:\n target_waypoint = wp_choice[0]\n plan.append((target_waypoint, RoadOption.LANEFOLLOW))\n wp_choice = target_waypoint.next(5.0)\n\n continue_driving = py_trees.composites.Parallel(\n \"ContinueDriving\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n continue_driving_waypoints = WaypointFollower(\n self.other_actors[0], self._other_actor_target_velocity, plan=plan, avoid_collision=False)\n\n continue_driving_distance = DriveDistance(\n self.other_actors[0],\n self._other_actor_distance,\n name=\"Distance\")\n\n continue_driving_timeout = TimeOut(10)\n\n continue_driving.add_child(continue_driving_waypoints)\n continue_driving.add_child(continue_driving_distance)\n continue_driving.add_child(continue_driving_timeout)\n\n # finally wait that ego vehicle drove a specific distance\n wait = DriveDistance(\n self.ego_vehicles[0],\n self._ego_distance_to_drive,\n name=\"DriveDistance\")\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"Sequence Behavior\")\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform))\n sequence.add_child(startcondition)\n sequence.add_child(sync_arrival_parallel)\n sequence.add_child(continue_driving)\n sequence.add_child(wait)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n max_velocity_criterion = MaxVelocityTest(\n self.ego_vehicles[0],\n self._ego_max_velocity_allowed,\n optional=True)\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n driven_distance_criterion = DrivenDistanceTest(\n self.ego_vehicles[0],\n self._ego_expected_driven_distance)\n\n criteria.append(max_velocity_criterion)\n criteria.append(collision_criterion)\n criteria.append(driven_distance_criterion)\n\n # Add the collision and lane checks for all vehicles as well\n for vehicle in self.other_actors:\n collision_criterion = CollisionTest(vehicle)\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors and traffic lights upon deletion\n \"\"\"\n self._traffic_light = None\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/other_leading_vehicle.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nOther Leading Vehicle scenario:\n\nThe scenario realizes a common driving behavior, in which the\nuser-controlled ego vehicle follows a leading car driving down\na given road. At some point the leading car has to decelerate.\nThe ego vehicle has to react accordingly by changing lane to avoid a\ncollision and follow the leading car in other lane. The scenario ends\neither via a timeout, or if the ego vehicle drives some distance.\n\"\"\"\n\nimport py_trees\n\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n WaypointFollower,\n ActorDestroy)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToVehicle,\n DriveDistance)\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import get_waypoint_in_distance\n\n\nclass OtherLeadingVehicle(BasicScenario):\n\n \"\"\"\n This class holds everything required for a simple \"Other Leading Vehicle\"\n scenario involving a user controlled vehicle and two other actors.\n Traffic Scenario 05\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=80):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._world = world\n self._map = CarlaDataProvider.get_map()\n self._first_vehicle_location = 35\n self._second_vehicle_location = self._first_vehicle_location + 1\n self._ego_vehicle_drive_distance = self._first_vehicle_location * 4\n self._first_vehicle_speed = 55\n self._second_vehicle_speed = 45\n self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)\n self._other_actor_max_brake = 1.0\n self._first_actor_transform = None\n self._second_actor_transform = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n\n super(OtherLeadingVehicle, self).__init__(\"VehicleDeceleratingInMultiLaneSetUp\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n first_vehicle_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._first_vehicle_location)\n second_vehicle_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._second_vehicle_location)\n second_vehicle_waypoint = second_vehicle_waypoint.get_left_lane()\n\n first_vehicle_transform = carla.Transform(first_vehicle_waypoint.transform.location,\n first_vehicle_waypoint.transform.rotation)\n second_vehicle_transform = carla.Transform(second_vehicle_waypoint.transform.location,\n second_vehicle_waypoint.transform.rotation)\n\n first_vehicle = CarlaDataProvider.request_new_actor('vehicle.nissan.patrol', first_vehicle_transform)\n second_vehicle = CarlaDataProvider.request_new_actor('vehicle.audi.tt', second_vehicle_transform)\n\n self.other_actors.append(first_vehicle)\n self.other_actors.append(second_vehicle)\n\n self._first_actor_transform = first_vehicle_transform\n self._second_actor_transform = second_vehicle_transform\n\n def _create_behavior(self):\n \"\"\"\n The scenario defined after is a \"other leading vehicle\" scenario. After\n invoking this scenario, the user controlled vehicle has to drive towards the\n moving other actors, then make the leading actor to decelerate when user controlled\n vehicle is at some close distance. Finally, the user-controlled vehicle has to change\n lane to avoid collision and follow other leading actor in other lane to end the scenario.\n If this does not happen within 90 seconds, a timeout stops the scenario or the ego vehicle\n drives certain distance and stops the scenario.\n \"\"\"\n # start condition\n driving_in_same_direction = py_trees.composites.Parallel(\"All actors driving in same direction\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n leading_actor_sequence_behavior = py_trees.composites.Sequence(\"Decelerating actor sequence behavior\")\n\n # both actors moving in same direction\n keep_velocity = py_trees.composites.Parallel(\"Trigger condition for deceleration\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n keep_velocity.add_child(WaypointFollower(self.other_actors[0], self._first_vehicle_speed, avoid_collision=True))\n keep_velocity.add_child(InTriggerDistanceToVehicle(self.other_actors[0], self.ego_vehicles[0], 55))\n\n # Decelerating actor sequence behavior\n decelerate = self._first_vehicle_speed / 3.2\n leading_actor_sequence_behavior.add_child(keep_velocity)\n leading_actor_sequence_behavior.add_child(WaypointFollower(self.other_actors[0], decelerate,\n avoid_collision=True))\n # end condition\n ego_drive_distance = DriveDistance(self.ego_vehicles[0], self._ego_vehicle_drive_distance)\n\n # Build behavior tree\n sequence = py_trees.composites.Sequence(\"Scenario behavior\")\n parallel_root = py_trees.composites.Parallel(policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n parallel_root.add_child(ego_drive_distance)\n parallel_root.add_child(driving_in_same_direction)\n driving_in_same_direction.add_child(leading_actor_sequence_behavior)\n driving_in_same_direction.add_child(WaypointFollower(self.other_actors[1], self._second_vehicle_speed,\n avoid_collision=True))\n\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self._first_actor_transform))\n sequence.add_child(ActorTransformSetter(self.other_actors[1], self._second_actor_transform))\n sequence.add_child(parallel_root)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n sequence.add_child(ActorDestroy(self.other_actors[1]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collision_criterion = CollisionTest(self.ego_vehicles[0])\n criteria.append(collision_criterion)\n\n return criteria\n\n def __del__(self):\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/route_scenario.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides Challenge routes as standalone scenarios\n\"\"\"\n\nfrom __future__ import print_function\n\nimport math\nimport traceback\nimport xml.etree.ElementTree as ET\nimport numpy.random as random\n\nimport py_trees\n\nimport carla\n\nfrom agents.navigation.local_planner import RoadOption\n\n# pylint: disable=line-too-long\nfrom srunner.scenarioconfigs.scenario_configuration import ScenarioConfiguration, ActorConfigurationData\n# pylint: enable=line-too-long\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import Idle, ScenarioTriggerer\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.route_parser import RouteParser, TRIGGER_THRESHOLD, TRIGGER_ANGLE_THRESHOLD\nfrom srunner.tools.route_manipulation import interpolate_trajectory\nfrom srunner.tools.py_trees_port import oneshot_behavior\n\nfrom srunner.scenarios.control_loss import ControlLoss\nfrom srunner.scenarios.follow_leading_vehicle import FollowLeadingVehicle\nfrom srunner.scenarios.object_crash_vehicle import DynamicObjectCrossing\nfrom srunner.scenarios.object_crash_intersection import VehicleTurningRoute\nfrom srunner.scenarios.other_leading_vehicle import OtherLeadingVehicle\nfrom srunner.scenarios.maneuver_opposite_direction import ManeuverOppositeDirection\nfrom srunner.scenarios.junction_crossing_route import SignalJunctionCrossingRoute, NoSignalJunctionCrossingRoute\n\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import (CollisionTest,\n InRouteTest,\n RouteCompletionTest,\n OutsideRouteLanesTest,\n RunningRedLightTest,\n RunningStopTest,\n ActorSpeedAboveThresholdTest)\n\nSECONDS_GIVEN_PER_METERS = 0.4\nimport os\nNUMBER_CLASS_TRANSLATION = {\n \"Scenario1\": ControlLoss,\n \"Scenario2\": FollowLeadingVehicle,\n \"Scenario3\": DynamicObjectCrossing,\n \"Scenario4\": VehicleTurningRoute,\n \"Scenario5\": OtherLeadingVehicle,\n \"Scenario6\": ManeuverOppositeDirection,\n \"Scenario7\": SignalJunctionCrossingRoute,\n \"Scenario8\": SignalJunctionCrossingRoute,\n \"Scenario9\": SignalJunctionCrossingRoute,\n \"Scenario10\": NoSignalJunctionCrossingRoute\n}\n\n\ndef convert_json_to_transform(actor_dict):\n \"\"\"\n Convert a JSON string to a CARLA transform\n \"\"\"\n return carla.Transform(location=carla.Location(x=float(actor_dict['x']), y=float(actor_dict['y']),\n z=float(actor_dict['z'])),\n rotation=carla.Rotation(roll=0.0, pitch=0.0, yaw=float(actor_dict['yaw'])))\n\n\ndef convert_json_to_actor(actor_dict):\n \"\"\"\n Convert a JSON string to an ActorConfigurationData dictionary\n \"\"\"\n node = ET.Element('waypoint')\n node.set('x', actor_dict['x'])\n node.set('y', actor_dict['y'])\n node.set('z', actor_dict['z'])\n node.set('yaw', actor_dict['yaw'])\n\n return ActorConfigurationData.parse_from_node(node, 'simulation')\n\n\ndef convert_transform_to_location(transform_vec):\n \"\"\"\n Convert a vector of transforms to a vector of locations\n \"\"\"\n location_vec = []\n for transform_tuple in transform_vec:\n location_vec.append((transform_tuple[0].location, transform_tuple[1]))\n\n return location_vec\n\n\ndef compare_scenarios(scenario_choice, existent_scenario):\n \"\"\"\n Compare function for scenarios based on distance of the scenario start position\n \"\"\"\n def transform_to_pos_vec(scenario):\n \"\"\"\n Convert left/right/front to a meaningful CARLA position\n \"\"\"\n position_vec = [scenario['trigger_position']]\n if scenario['other_actors'] is not None:\n if 'left' in scenario['other_actors']:\n position_vec += scenario['other_actors']['left']\n if 'front' in scenario['other_actors']:\n position_vec += scenario['other_actors']['front']\n if 'right' in scenario['other_actors']:\n position_vec += scenario['other_actors']['right']\n\n return position_vec\n\n # put the positions of the scenario choice into a vec of positions to be able to compare\n\n choice_vec = transform_to_pos_vec(scenario_choice)\n existent_vec = transform_to_pos_vec(existent_scenario)\n for pos_choice in choice_vec:\n for pos_existent in existent_vec:\n\n dx = float(pos_choice['x']) - float(pos_existent['x'])\n dy = float(pos_choice['y']) - float(pos_existent['y'])\n dz = float(pos_choice['z']) - float(pos_existent['z'])\n dist_position = math.sqrt(dx * dx + dy * dy + dz * dz)\n dyaw = float(pos_choice['yaw']) - float(pos_choice['yaw'])\n dist_angle = math.sqrt(dyaw * dyaw)\n if dist_position < TRIGGER_THRESHOLD and dist_angle < TRIGGER_ANGLE_THRESHOLD:\n return True\n\n return False\n\n\nclass RouteScenario(BasicScenario):\n\n \"\"\"\n Implementation of a RouteScenario, i.e. a scenario that consists of driving along a pre-defined route,\n along which several smaller scenarios are triggered\n \"\"\"\n\n def __init__(self, world, config, debug_mode=False, criteria_enable=True, timeout=300):\n \"\"\"\n Setup all relevant parameters and create scenarios along route\n \"\"\"\n\n self.config = config\n self.route = None\n self.sampled_scenarios_definitions = None\n\n self._update_route(world, config, debug_mode)\n\n ego_vehicle = self._update_ego_vehicle()\n\n self.list_scenarios = self._build_scenario_instances(world,\n ego_vehicle,\n self.sampled_scenarios_definitions,\n scenarios_per_tick=5,\n timeout=self.timeout,\n debug_mode=debug_mode)\n self.is_eval = (os.environ['IS_EVAL'] == \"True\")\n print(\"IS_EVAL\", self.is_eval)\n super(RouteScenario, self).__init__(name=config.name,\n ego_vehicles=[ego_vehicle],\n config=config,\n world=world,\n debug_mode=False,\n terminate_on_failure=False,\n criteria_enable=criteria_enable)\n\n def _update_route(self, world, config, debug_mode):\n \"\"\"\n Update the input route, i.e. refine waypoint list, and extract possible scenario locations\n\n Parameters:\n - world: CARLA world\n - config: Scenario configuration (RouteConfiguration)\n \"\"\"\n\n # Transform the scenario file into a dictionary\n world_annotations = RouteParser.parse_annotations_file(config.scenario_file)\n\n # prepare route's trajectory (interpolate and add the GPS route)\n gps_route, route = interpolate_trajectory(world, config.trajectory)\n\n potential_scenarios_definitions, _ = RouteParser.scan_route_for_scenarios(config.town, route, world_annotations)\n\n self.route = route\n CarlaDataProvider.set_ego_vehicle_route(convert_transform_to_location(self.route))\n\n config.agent.set_global_plan(gps_route, self.route)\n\n # Sample the scenarios to be used for this route instance.\n self.sampled_scenarios_definitions = self._scenario_sampling(potential_scenarios_definitions)\n\n # Timeout of scenario in seconds\n self.timeout = self._estimate_route_timeout()\n\n # Print route in debug mode\n if debug_mode:\n self._draw_waypoints(world, self.route, vertical_shift=1.0, persistency=50000.0)\n\n def _update_ego_vehicle(self):\n \"\"\"\n Set/Update the start position of the ego_vehicle\n \"\"\"\n # move ego to correct position\n elevate_transform = self.route[0][0]\n elevate_transform.location.z += 0.5\n\n ego_vehicle = CarlaDataProvider.request_new_actor('vehicle.lincoln.mkz2017',\n elevate_transform,\n rolename='hero')\n\n return ego_vehicle\n\n def _estimate_route_timeout(self):\n \"\"\"\n Estimate the duration of the route\n \"\"\"\n route_length = 0.0 # in meters\n\n prev_point = self.route[0][0]\n for current_point, _ in self.route[1:]:\n dist = current_point.location.distance(prev_point.location)\n route_length += dist\n prev_point = current_point\n\n return int(SECONDS_GIVEN_PER_METERS * route_length)\n\n # pylint: disable=no-self-use\n def _draw_waypoints(self, world, waypoints, vertical_shift, persistency=-1):\n \"\"\"\n Draw a list of waypoints at a certain height given in vertical_shift.\n \"\"\"\n for w in waypoints:\n wp = w[0].location + carla.Location(z=vertical_shift)\n\n size = 0.2\n if w[1] == RoadOption.LEFT: # Yellow\n color = carla.Color(255, 255, 0)\n elif w[1] == RoadOption.RIGHT: # Cyan\n color = carla.Color(0, 255, 255)\n elif w[1] == RoadOption.CHANGELANELEFT: # Orange\n color = carla.Color(255, 64, 0)\n elif w[1] == RoadOption.CHANGELANERIGHT: # Dark Cyan\n color = carla.Color(0, 64, 255)\n elif w[1] == RoadOption.STRAIGHT: # Gray\n color = carla.Color(128, 128, 128)\n else: # LANEFOLLOW\n color = carla.Color(0, 255, 0) # Green\n size = 0.1\n\n world.debug.draw_point(wp, size=size, color=color, life_time=persistency)\n\n world.debug.draw_point(waypoints[0][0].location + carla.Location(z=vertical_shift), size=0.2,\n color=carla.Color(0, 0, 255), life_time=persistency)\n world.debug.draw_point(waypoints[-1][0].location + carla.Location(z=vertical_shift), size=0.2,\n color=carla.Color(255, 0, 0), life_time=persistency)\n\n def _scenario_sampling(self, potential_scenarios_definitions, random_seed=0):\n \"\"\"\n The function used to sample the scenarios that are going to happen for this route.\n \"\"\"\n\n # fix the random seed for reproducibility\n rng = random.RandomState(random_seed)\n\n def position_sampled(scenario_choice, sampled_scenarios):\n \"\"\"\n Check if a position was already sampled, i.e. used for another scenario\n \"\"\"\n for existent_scenario in sampled_scenarios:\n # If the scenarios have equal positions then it is true.\n if compare_scenarios(scenario_choice, existent_scenario):\n return True\n\n return False\n\n # The idea is to randomly sample a scenario per trigger position.\n sampled_scenarios = []\n for trigger in potential_scenarios_definitions.keys():\n possible_scenarios = potential_scenarios_definitions[trigger]\n\n scenario_choice = rng.choice(possible_scenarios)\n del possible_scenarios[possible_scenarios.index(scenario_choice)]\n # We keep sampling and testing if this position is present on any of the scenarios.\n while position_sampled(scenario_choice, sampled_scenarios):\n if possible_scenarios is None or not possible_scenarios:\n scenario_choice = None\n break\n scenario_choice = rng.choice(possible_scenarios)\n del possible_scenarios[possible_scenarios.index(scenario_choice)]\n\n if scenario_choice is not None:\n sampled_scenarios.append(scenario_choice)\n\n return sampled_scenarios\n\n def _build_scenario_instances(self, world, ego_vehicle, scenario_definitions,\n scenarios_per_tick=5, timeout=300, debug_mode=False):\n \"\"\"\n Based on the parsed route and possible scenarios, build all the scenario classes.\n \"\"\"\n scenario_instance_vec = []\n\n if debug_mode:\n for scenario in scenario_definitions:\n loc = carla.Location(scenario['trigger_position']['x'],\n scenario['trigger_position']['y'],\n scenario['trigger_position']['z']) + carla.Location(z=2.0)\n world.debug.draw_point(loc, size=0.3, color=carla.Color(255, 0, 0), life_time=100000)\n world.debug.draw_string(loc, str(scenario['name']), draw_shadow=False,\n color=carla.Color(0, 0, 255), life_time=100000, persistent_lines=True)\n\n for scenario_number, definition in enumerate(scenario_definitions):\n # Get the class possibilities for this scenario number\n scenario_class = NUMBER_CLASS_TRANSLATION[definition['name']]\n\n # Create the other actors that are going to appear\n if definition['other_actors'] is not None:\n list_of_actor_conf_instances = self._get_actors_instances(definition['other_actors'])\n else:\n list_of_actor_conf_instances = []\n # Create an actor configuration for the ego-vehicle trigger position\n\n egoactor_trigger_position = convert_json_to_transform(definition['trigger_position'])\n scenario_configuration = ScenarioConfiguration()\n scenario_configuration.other_actors = list_of_actor_conf_instances\n scenario_configuration.trigger_points = [egoactor_trigger_position]\n scenario_configuration.subtype = definition['scenario_type']\n scenario_configuration.ego_vehicles = [ActorConfigurationData('vehicle.lincoln.mkz2017',\n ego_vehicle.get_transform(),\n 'hero')]\n route_var_name = \"ScenarioRouteNumber{}\".format(scenario_number)\n scenario_configuration.route_var_name = route_var_name\n\n try:\n scenario_instance = scenario_class(world, [ego_vehicle], scenario_configuration,\n criteria_enable=False, timeout=timeout)\n # Do a tick every once in a while to avoid spawning everything at the same time\n if scenario_number % scenarios_per_tick == 0:\n if CarlaDataProvider.is_sync_mode():\n world.tick()\n else:\n world.wait_for_tick()\n\n scenario_number += 1\n except Exception as e: # pylint: disable=broad-except\n if debug_mode:\n traceback.print_exc()\n print(\"Skipping scenario '{}' due to setup error: {}\".format(definition['name'], e))\n continue\n\n scenario_instance_vec.append(scenario_instance)\n\n return scenario_instance_vec\n\n def _get_actors_instances(self, list_of_antagonist_actors):\n \"\"\"\n Get the full list of actor instances.\n \"\"\"\n\n def get_actors_from_list(list_of_actor_def):\n \"\"\"\n Receives a list of actor definitions and creates an actual list of ActorConfigurationObjects\n \"\"\"\n sublist_of_actors = []\n for actor_def in list_of_actor_def:\n sublist_of_actors.append(convert_json_to_actor(actor_def))\n\n return sublist_of_actors\n\n list_of_actors = []\n # Parse vehicles to the left\n if 'front' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['front'])\n\n if 'left' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['left'])\n\n if 'right' in list_of_antagonist_actors:\n list_of_actors += get_actors_from_list(list_of_antagonist_actors['right'])\n\n return list_of_actors\n\n # pylint: enable=no-self-use\n\n def _initialize_actors(self, config):\n \"\"\"\n Set other_actors to the superset of all scenario actors\n \"\"\"\n\n # Create the background activity of the route\n town_amount = {\n 'Town01': 120,\n 'Town02': 100,\n 'Town03': 120,\n 'Town04': 200,\n 'Town05': 120,\n 'Town06': 150,\n 'Town07': 110,\n 'Town08': 180,\n 'Town09': 300,\n 'Town10': 120,\n }\n\n amount = town_amount[config.town] if config.town in town_amount else 0\n if os.environ[\"BENCHMARK\"] == \"town05long\":\n amount = 120\n print(\"----------------Eval with Town05 Long, amount=120\", flush=True)\n if os.environ[\"BENCHMARK\"] == \"longest6\":\n amount = 500\n print(\"----------------Eval with Longest6, amount=500\", flush=True)\n new_actors = CarlaDataProvider.request_new_batch_actors('vehicle.*',\n amount,\n carla.Transform(),\n autopilot=True,\n random_location=True,\n rolename='background')\n\n if new_actors is None:\n raise Exception(\"Error: Unable to add the background activity, all spawn points were occupied\")\n\n for _actor in new_actors:\n self.other_actors.append(_actor)\n\n # Add all the actors of the specific scenarios to self.other_actors\n for scenario in self.list_scenarios:\n self.other_actors.extend(scenario.other_actors)\n\n def _create_behavior(self):\n \"\"\"\n Basic behavior do nothing, i.e. Idle\n \"\"\"\n scenario_trigger_distance = 1.5 # Max trigger distance between route and scenario\n\n behavior = py_trees.composites.Parallel(policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n\n subbehavior = py_trees.composites.Parallel(name=\"Behavior\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL)\n\n scenario_behaviors = []\n blackboard_list = []\n\n for i, scenario in enumerate(self.list_scenarios):\n if scenario.scenario.behavior is not None:\n route_var_name = scenario.config.route_var_name\n if route_var_name is not None:\n scenario_behaviors.append(scenario.scenario.behavior)\n blackboard_list.append([scenario.config.route_var_name,\n scenario.config.trigger_points[0].location])\n else:\n name = \"{} - {}\".format(i, scenario.scenario.behavior.name)\n oneshot_idiom = oneshot_behavior(name,\n behaviour=scenario.scenario.behavior,\n name=name)\n scenario_behaviors.append(oneshot_idiom)\n\n # Add behavior that manages the scenarios trigger conditions\n scenario_triggerer = ScenarioTriggerer(\n self.ego_vehicles[0],\n self.route,\n blackboard_list,\n scenario_trigger_distance,\n repeat_scenarios=False\n )\n\n subbehavior.add_child(scenario_triggerer) # make ScenarioTriggerer the first thing to be checked\n subbehavior.add_children(scenario_behaviors)\n subbehavior.add_child(Idle()) # The behaviours cannot make the route scenario stop\n behavior.add_child(subbehavior)\n\n return behavior\n\n def _create_test_criteria(self):\n \"\"\"\n \"\"\"\n\n criteria = []\n\n route = convert_transform_to_location(self.route)\n\n collision_criterion = CollisionTest(self.ego_vehicles[0], terminate_on_failure=False)\n\n route_criterion = InRouteTest(self.ego_vehicles[0],\n route=route,\n offroad_max=30,\n terminate_on_failure=True)\n\n completion_criterion = RouteCompletionTest(self.ego_vehicles[0], route=route)\n\n outsidelane_criterion = OutsideRouteLanesTest(self.ego_vehicles[0], route=route)\n\n red_light_criterion = RunningRedLightTest(self.ego_vehicles[0])\n\n stop_criterion = RunningStopTest(self.ego_vehicles[0])\n\n blocked_criterion = ActorSpeedAboveThresholdTest(self.ego_vehicles[0],\n speed_threshold=0.1,\n below_threshold_max_time=90.0,\n terminate_on_failure=True)\n\n criteria.append(completion_criterion)\n criteria.append(collision_criterion)\n criteria.append(route_criterion)\n criteria.append(outsidelane_criterion)\n criteria.append(red_light_criterion)\n criteria.append(stop_criterion)\n criteria.append(blocked_criterion)\n\n return criteria\n\n def __del__(self):\n \"\"\"\n Remove all actors upon deletion\n \"\"\"\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/signalized_junction_left_turn.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nCollection of traffic scenarios where the ego vehicle (hero)\nis making a left turn\n\"\"\"\n\nfrom six.moves.queue import Queue # pylint: disable=relative-import\n\nimport py_trees\nimport carla\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n ActorSource,\n ActorSink,\n WaypointFollower)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import DriveDistance\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import generate_target_waypoint\n\n\nclass SignalizedJunctionLeftTurn(BasicScenario):\n\n \"\"\"\n Implementation class for Hero\n Vehicle turning left at signalized junction scenario,\n Traffic Scenario 08.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n timeout = 80 # Timeout of scenario in seconds\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=80):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._world = world\n self._map = CarlaDataProvider.get_map()\n self._target_vel = 6.9\n self._brake_value = 0.5\n self._ego_distance = 110\n self._traffic_light = None\n self._other_actor_transform = None\n self._blackboard_queue_name = 'SignalizedJunctionLeftTurn/actor_flow_queue'\n self._queue = py_trees.blackboard.Blackboard().set(self._blackboard_queue_name, Queue())\n self._initialized = True\n super(SignalizedJunctionLeftTurn, self).__init__(\"TurnLeftAtSignalizedJunction\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n self._traffic_light = CarlaDataProvider.get_next_traffic_light(self.ego_vehicles[0], False)\n traffic_light_other = CarlaDataProvider.get_next_traffic_light(self.other_actors[0], False)\n if self._traffic_light is None or traffic_light_other is None:\n raise RuntimeError(\"No traffic light for the given location found\")\n self._traffic_light.set_state(carla.TrafficLightState.Green)\n self._traffic_light.set_green_time(self.timeout)\n # other vehicle's traffic light\n traffic_light_other.set_state(carla.TrafficLightState.Green)\n traffic_light_other.set_green_time(self.timeout)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n self._other_actor_transform = config.other_actors[0].transform\n first_vehicle_transform = carla.Transform(\n carla.Location(config.other_actors[0].transform.location.x,\n config.other_actors[0].transform.location.y,\n config.other_actors[0].transform.location.z - 500),\n config.other_actors[0].transform.rotation)\n first_vehicle = CarlaDataProvider.request_new_actor(config.other_actors[0].model, self._other_actor_transform)\n first_vehicle.set_transform(first_vehicle_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n Hero vehicle is turning left in an urban area,\n at a signalized intersection, while other actor coming straight\n .The hero actor may turn left either before other actor\n passes intersection or later, without any collision.\n After 80 seconds, a timeout stops the scenario.\n \"\"\"\n\n sequence = py_trees.composites.Sequence(\"Sequence Behavior\")\n\n # Selecting straight path at intersection\n target_waypoint = generate_target_waypoint(\n CarlaDataProvider.get_map().get_waypoint(self.other_actors[0].get_location()), 0)\n # Generating waypoint list till next intersection\n plan = []\n wp_choice = target_waypoint.next(1.0)\n while not wp_choice[0].is_intersection:\n target_waypoint = wp_choice[0]\n plan.append((target_waypoint, RoadOption.LANEFOLLOW))\n wp_choice = target_waypoint.next(1.0)\n # adding flow of actors\n actor_source = ActorSource(\n ['vehicle.tesla.model3', 'vehicle.audi.tt'],\n self._other_actor_transform, 15, self._blackboard_queue_name)\n # destroying flow of actors\n actor_sink = ActorSink(plan[-1][0].transform.location, 10)\n # follow waypoints untill next intersection\n move_actor = WaypointFollower(self.other_actors[0], self._target_vel, plan=plan,\n blackboard_queue_name=self._blackboard_queue_name, avoid_collision=True)\n # wait\n wait = DriveDistance(self.ego_vehicles[0], self._ego_distance)\n\n # Behavior tree\n root = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n root.add_child(wait)\n root.add_child(actor_source)\n root.add_child(actor_sink)\n root.add_child(move_actor)\n\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform))\n sequence.add_child(root)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collison_criteria = CollisionTest(self.ego_vehicles[0])\n criteria.append(collison_criteria)\n\n return criteria\n\n def __del__(self):\n self._traffic_light = None\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/scenarios/signalized_junction_right_turn.py", "content": "#!/usr/bin/env python\n\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nCollection of traffic scenarios where the ego vehicle (hero)\nis making a right turn\n\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\n\nimport py_trees\n\nimport carla\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,\n ActorDestroy,\n StopVehicle,\n SyncArrival,\n WaypointFollower)\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import DriveDistance, InTriggerDistanceToLocation\nfrom srunner.scenarios.basic_scenario import BasicScenario\nfrom srunner.tools.scenario_helper import (get_geometric_linear_intersection,\n get_crossing_point,\n generate_target_waypoint)\n\n\nclass SignalizedJunctionRightTurn(BasicScenario):\n\n \"\"\"\n Implementation class for Hero\n Vehicle turning right at signalized junction scenario,\n Traffic Scenario 09.\n\n This is a single ego vehicle scenario\n \"\"\"\n\n def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,\n timeout=80):\n \"\"\"\n Setup all relevant parameters and create scenario\n \"\"\"\n self._target_vel = 6.9\n self._brake_value = 0.5\n self._ego_distance = 40\n self._traffic_light = None\n self._other_actor_transform = None\n # Timeout of scenario in seconds\n self.timeout = timeout\n super(SignalizedJunctionRightTurn, self).__init__(\"HeroActorTurningRightAtSignalizedJunction\",\n ego_vehicles,\n config,\n world,\n debug_mode,\n criteria_enable=criteria_enable)\n\n self._traffic_light = CarlaDataProvider.get_next_traffic_light(self.ego_vehicles[0], False)\n if self._traffic_light is None:\n print(\"No traffic light for the given location of the ego vehicle found\")\n sys.exit(-1)\n self._traffic_light.set_state(carla.TrafficLightState.Red)\n self._traffic_light.set_red_time(self.timeout)\n # other vehicle's traffic light\n traffic_light_other = CarlaDataProvider.get_next_traffic_light(self.other_actors[0], False)\n if traffic_light_other is None:\n print(\"No traffic light for the given location of the other vehicle found\")\n sys.exit(-1)\n traffic_light_other.set_state(carla.TrafficLightState.Green)\n traffic_light_other.set_green_time(self.timeout)\n\n def _initialize_actors(self, config):\n \"\"\"\n Custom initialization\n \"\"\"\n self._other_actor_transform = config.other_actors[0].transform\n first_vehicle_transform = carla.Transform(\n carla.Location(config.other_actors[0].transform.location.x,\n config.other_actors[0].transform.location.y,\n config.other_actors[0].transform.location.z - 500),\n config.other_actors[0].transform.rotation)\n first_vehicle = CarlaDataProvider.request_new_actor(config.other_actors[0].model, first_vehicle_transform)\n first_vehicle.set_simulate_physics(enabled=False)\n self.other_actors.append(first_vehicle)\n\n def _create_behavior(self):\n \"\"\"\n Hero vehicle is turning right in an urban area,\n at a signalized intersection, while other actor coming straight\n from left.The hero actor may turn right either before other actor\n passes intersection or later, without any collision.\n After 80 seconds, a timeout stops the scenario.\n \"\"\"\n\n location_of_collision_dynamic = get_geometric_linear_intersection(self.ego_vehicles[0], self.other_actors[0])\n crossing_point_dynamic = get_crossing_point(self.other_actors[0])\n sync_arrival = SyncArrival(\n self.other_actors[0], self.ego_vehicles[0], location_of_collision_dynamic)\n sync_arrival_stop = InTriggerDistanceToLocation(self.other_actors[0], crossing_point_dynamic, 5)\n\n sync_arrival_parallel = py_trees.composites.Parallel(\n \"Synchronize arrival times\",\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n sync_arrival_parallel.add_child(sync_arrival)\n sync_arrival_parallel.add_child(sync_arrival_stop)\n\n # Selecting straight path at intersection\n target_waypoint = generate_target_waypoint(\n CarlaDataProvider.get_map().get_waypoint(self.other_actors[0].get_location()), 0)\n # Generating waypoint list till next intersection\n plan = []\n wp_choice = target_waypoint.next(1.0)\n while not wp_choice[0].is_intersection:\n target_waypoint = wp_choice[0]\n plan.append((target_waypoint, RoadOption.LANEFOLLOW))\n wp_choice = target_waypoint.next(1.0)\n\n move_actor = WaypointFollower(self.other_actors[0], self._target_vel, plan=plan)\n waypoint_follower_end = InTriggerDistanceToLocation(\n self.other_actors[0], plan[-1][0].transform.location, 10)\n\n move_actor_parallel = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)\n move_actor_parallel.add_child(move_actor)\n move_actor_parallel.add_child(waypoint_follower_end)\n # stop other actor\n stop = StopVehicle(self.other_actors[0], self._brake_value)\n # end condition\n end_condition = DriveDistance(self.ego_vehicles[0], self._ego_distance)\n\n # Behavior tree\n sequence = py_trees.composites.Sequence()\n sequence.add_child(ActorTransformSetter(self.other_actors[0], self._other_actor_transform))\n sequence.add_child(sync_arrival_parallel)\n sequence.add_child(move_actor_parallel)\n sequence.add_child(stop)\n sequence.add_child(end_condition)\n sequence.add_child(ActorDestroy(self.other_actors[0]))\n\n return sequence\n\n def _create_test_criteria(self):\n \"\"\"\n A list of all test criteria will be created that is later used\n in parallel behavior tree.\n \"\"\"\n criteria = []\n\n collison_criteria = CollisionTest(self.ego_vehicles[0])\n criteria.append(collison_criteria)\n\n return criteria\n\n def __del__(self):\n self._traffic_light = None\n self.remove_all_actors()\n" }, { "path": "scenario_runner/srunner/tools/__init__.py", "content": "" }, { "path": "scenario_runner/srunner/tools/openscenario_parser.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019-2020 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides a parser for scenario configuration files based on OpenSCENARIO\n\"\"\"\n\nfrom distutils.util import strtobool\nimport copy\nimport datetime\nimport math\nimport operator\n\nimport py_trees\nimport carla\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\nfrom srunner.scenariomanager.weather_sim import Weather\nfrom srunner.scenariomanager.scenarioatomics.atomic_behaviors import (TrafficLightStateSetter,\n ActorTransformSetterToOSCPosition,\n RunScript,\n ChangeWeather,\n ChangeAutoPilot,\n ChangeRoadFriction,\n ChangeActorTargetSpeed,\n ChangeActorControl,\n ChangeActorWaypoints,\n ChangeActorWaypointsToReachPosition,\n ChangeActorLateralMotion,\n Idle)\n# pylint: disable=unused-import\n# For the following includes the pylint check is disabled, as these are accessed via globals()\nfrom srunner.scenariomanager.scenarioatomics.atomic_criteria import (CollisionTest,\n MaxVelocityTest,\n DrivenDistanceTest,\n AverageVelocityTest,\n KeepLaneTest,\n ReachedRegionTest,\n OnSidewalkTest,\n WrongLaneTest,\n InRadiusRegionTest,\n InRouteTest,\n RouteCompletionTest,\n RunningRedLightTest,\n RunningStopTest,\n OffRoadTest,\n EndofRoadTest)\n# pylint: enable=unused-import\nfrom srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import (InTriggerDistanceToVehicle,\n InTriggerDistanceToOSCPosition,\n InTimeToArrivalToOSCPosition,\n InTimeToArrivalToVehicle,\n DriveDistance,\n StandStill,\n OSCStartEndCondition,\n TriggerAcceleration,\n RelativeVelocityToOtherActor,\n TimeOfDayComparison,\n TriggerVelocity,\n WaitForTrafficLightState)\nfrom srunner.scenariomanager.timer import TimeOut, SimulationTimeCondition\nfrom srunner.tools.py_trees_port import oneshot_behavior\n\n\nclass OpenScenarioParser(object):\n\n \"\"\"\n Pure static class providing conversions from OpenSCENARIO elements to ScenarioRunner elements\n \"\"\"\n operators = {\n \"greaterThan\": operator.gt,\n \"lessThan\": operator.lt,\n \"equalTo\": operator.eq\n }\n\n actor_types = {\n \"pedestrian\": \"walker\",\n \"vehicle\": \"vehicle\",\n \"miscellaneous\": \"miscellaneous\"\n }\n\n tl_states = {\n \"GREEN\": carla.TrafficLightState.Green,\n \"YELLOW\": carla.TrafficLightState.Yellow,\n \"RED\": carla.TrafficLightState.Red,\n \"OFF\": carla.TrafficLightState.Off,\n }\n\n global_osc_parameters = dict()\n use_carla_coordinate_system = False\n osc_filepath = None\n\n @staticmethod\n def get_traffic_light_from_osc_name(name):\n \"\"\"\n Returns a carla.TrafficLight instance that matches the name given\n \"\"\"\n traffic_light = None\n\n # Given by id\n if name.startswith(\"id=\"):\n tl_id = name[3:]\n for carla_tl in CarlaDataProvider.get_world().get_actors().filter('traffic.traffic_light'):\n if carla_tl.id == tl_id:\n traffic_light = carla_tl\n break\n # Given by position\n elif name.startswith(\"pos=\"):\n tl_pos = name[4:]\n pos = tl_pos.split(\",\")\n for carla_tl in CarlaDataProvider.get_world().get_actors().filter('traffic.traffic_light'):\n carla_tl_location = carla_tl.get_transform().location\n distance = carla_tl_location.distance(carla.Location(float(pos[0]),\n float(pos[1]),\n carla_tl_location.z))\n if distance < 2.0:\n traffic_light = carla_tl\n break\n\n if traffic_light is None:\n raise AttributeError(\"Unknown traffic light {}\".format(name))\n\n return traffic_light\n\n @staticmethod\n def set_osc_filepath(filepath):\n \"\"\"\n Set path of OSC file. This is required if for example custom commands are provided with\n relative paths.\n \"\"\"\n OpenScenarioParser.osc_filepath = filepath\n\n @staticmethod\n def set_use_carla_coordinate_system():\n \"\"\"\n CARLA internally uses a left-hand coordinate system (Unreal), but OpenSCENARIO and OpenDRIVE\n are intended for right-hand coordinate system. Hence, we need to invert the coordinates, if\n the scenario does not use CARLA coordinates, but instead right-hand coordinates.\n \"\"\"\n OpenScenarioParser.use_carla_coordinate_system = True\n\n @staticmethod\n def set_parameters(xml_tree, additional_parameter_dict=None):\n \"\"\"\n Parse the xml_tree, and replace all parameter references\n with the actual values.\n\n Note: Parameter names must not start with \"$\", however when referencing a parameter the\n reference has to start with \"$\".\n https://releases.asam.net/OpenSCENARIO/1.0.0/ASAM_OpenSCENARIO_BS-1-2_User-Guide_V1-0-0.html#_re_use_mechanisms\n\n Args:\n xml_tree: Containing all nodes that should be updated\n additional_parameter_dict (dictionary): Additional parameters as dict (key, value). Optional.\n\n returns:\n updated xml_tree, dictonary containing all parameters and their values\n \"\"\"\n\n parameter_dict = dict()\n if additional_parameter_dict is not None:\n parameter_dict = additional_parameter_dict\n parameters = xml_tree.find('ParameterDeclarations')\n\n if parameters is None and not parameter_dict:\n return xml_tree, parameter_dict\n\n if parameters is None:\n parameters = []\n\n for parameter in parameters:\n name = parameter.attrib.get('name')\n value = parameter.attrib.get('value')\n parameter_dict[name] = value\n\n for node in xml_tree.iter():\n for key in node.attrib:\n for param in sorted(parameter_dict, key=len, reverse=True):\n if \"$\" + param in node.attrib[key]:\n node.attrib[key] = node.attrib[key].replace(\"$\" + param, parameter_dict[param])\n\n return xml_tree, parameter_dict\n\n @staticmethod\n def set_global_parameters(parameter_dict):\n \"\"\"\n Set global_osc_parameter dictionary\n\n Args:\n parameter_dict (Dictionary): Input for global_osc_parameter\n \"\"\"\n OpenScenarioParser.global_osc_parameters = parameter_dict\n\n @staticmethod\n def get_catalog_entry(catalogs, catalog_reference):\n \"\"\"\n Get catalog entry referenced by catalog_reference included correct parameter settings\n\n Args:\n catalogs (Dictionary of dictionaries): List of all catalogs and their entries\n catalog_reference (XML ElementTree): Reference containing the exact catalog to be used\n\n returns:\n Catalog entry (XML ElementTree)\n \"\"\"\n\n entry = catalogs[catalog_reference.attrib.get(\"catalogName\")][catalog_reference.attrib.get(\"entryName\")]\n entry_copy = copy.deepcopy(entry)\n catalog_copy = copy.deepcopy(catalog_reference)\n entry = OpenScenarioParser.assign_catalog_parameters(entry_copy, catalog_copy)\n\n return entry\n\n @staticmethod\n def assign_catalog_parameters(entry_instance, catalog_reference):\n \"\"\"\n Parse catalog_reference, and replace all parameter references\n in entry_instance by the values provided in catalog_reference.\n\n Not to be used from outside this class.\n\n Args:\n entry_instance (XML ElementTree): Entry to be updated\n catalog_reference (XML ElementTree): Reference containing the exact parameter values\n\n returns:\n updated entry_instance with updated parameter values\n \"\"\"\n\n parameter_dict = dict()\n for elem in entry_instance.iter():\n if elem.find('ParameterDeclarations') is not None:\n parameters = elem.find('ParameterDeclarations')\n for parameter in parameters:\n name = parameter.attrib.get('name')\n value = parameter.attrib.get('value')\n parameter_dict[name] = value\n\n for parameter_assignments in catalog_reference.iter(\"ParameterAssignments\"):\n for parameter_assignment in parameter_assignments.iter(\"ParameterAssignment\"):\n parameter = parameter_assignment.attrib.get(\"parameterRef\")\n value = parameter_assignment.attrib.get(\"value\")\n parameter_dict[parameter] = value\n\n for node in entry_instance.iter():\n for key in node.attrib:\n for param in sorted(parameter_dict, key=len, reverse=True):\n if \"$\" + param in node.attrib[key]:\n node.attrib[key] = node.attrib[key].replace(\"$\" + param, parameter_dict[param])\n\n OpenScenarioParser.set_parameters(entry_instance, OpenScenarioParser.global_osc_parameters)\n\n return entry_instance\n\n @staticmethod\n def get_friction_from_env_action(xml_tree, catalogs):\n \"\"\"\n Extract the CARLA road friction coefficient from an OSC EnvironmentAction\n\n Args:\n xml_tree: Containing the EnvironmentAction,\n or the reference to the catalog it is defined in.\n catalogs: XML Catalogs that could contain the EnvironmentAction\n\n returns:\n friction (float)\n \"\"\"\n set_environment = next(xml_tree.iter(\"EnvironmentAction\"))\n\n if sum(1 for _ in set_environment.iter(\"Weather\")) != 0:\n environment = set_environment.find(\"Environment\")\n elif set_environment.find(\"CatalogReference\") is not None:\n catalog_reference = set_environment.find(\"CatalogReference\")\n environment = OpenScenarioParser.get_catalog_entry(catalogs, catalog_reference)\n\n friction = 1.0\n\n road_condition = environment.iter(\"RoadCondition\")\n for condition in road_condition:\n friction = condition.attrib.get('frictionScaleFactor')\n\n return friction\n\n @staticmethod\n def get_weather_from_env_action(xml_tree, catalogs):\n \"\"\"\n Extract the CARLA weather parameters from an OSC EnvironmentAction\n\n Args:\n xml_tree: Containing the EnvironmentAction,\n or the reference to the catalog it is defined in.\n catalogs: XML Catalogs that could contain the EnvironmentAction\n\n returns:\n Weather (srunner.scenariomanager.weather_sim.Weather)\n \"\"\"\n set_environment = next(xml_tree.iter(\"EnvironmentAction\"))\n\n if sum(1 for _ in set_environment.iter(\"Weather\")) != 0:\n environment = set_environment.find(\"Environment\")\n elif set_environment.find(\"CatalogReference\") is not None:\n catalog_reference = set_environment.find(\"CatalogReference\")\n environment = OpenScenarioParser.get_catalog_entry(catalogs, catalog_reference)\n\n weather = environment.find(\"Weather\")\n sun = weather.find(\"Sun\")\n\n carla_weather = carla.WeatherParameters()\n carla_weather.sun_azimuth_angle = math.degrees(float(sun.attrib.get('azimuth', 0)))\n carla_weather.sun_altitude_angle = math.degrees(float(sun.attrib.get('elevation', 0)))\n carla_weather.cloudiness = 100 - float(sun.attrib.get('intensity', 0)) * 100\n fog = weather.find(\"Fog\")\n carla_weather.fog_distance = float(fog.attrib.get('visualRange', 'inf'))\n if carla_weather.fog_distance < 1000:\n carla_weather.fog_density = 100\n carla_weather.precipitation = 0\n carla_weather.precipitation_deposits = 0\n carla_weather.wetness = 0\n carla_weather.wind_intensity = 0\n precepitation = weather.find(\"Precipitation\")\n if precepitation.attrib.get('precipitationType') == \"rain\":\n carla_weather.precipitation = float(precepitation.attrib.get('intensity')) * 100\n carla_weather.precipitation_deposits = 100 # if it rains, make the road wet\n carla_weather.wetness = carla_weather.precipitation\n elif precepitation.attrib.get('type') == \"snow\":\n raise AttributeError(\"CARLA does not support snow precipitation\")\n\n time_of_day = environment.find(\"TimeOfDay\")\n weather_animation = strtobool(time_of_day.attrib.get(\"animation\"))\n time = time_of_day.attrib.get(\"dateTime\")\n dtime = datetime.datetime.strptime(time, \"%Y-%m-%dT%H:%M:%S\")\n\n return Weather(carla_weather, dtime, weather_animation)\n\n @staticmethod\n def get_controller(xml_tree, catalogs):\n \"\"\"\n Extract the object controller from the OSC XML or a catalog\n\n Args:\n xml_tree: Containing the controller information,\n or the reference to the catalog it is defined in.\n catalogs: XML Catalogs that could contain the EnvironmentAction\n\n returns:\n module: Python module containing the controller implementation\n args: Dictonary with (key, value) parameters for the controller\n \"\"\"\n\n assign_action = next(xml_tree.iter(\"AssignControllerAction\"))\n\n properties = None\n if assign_action.find('Controller') is not None:\n properties = assign_action.find('Controller').find('Properties')\n elif assign_action.find(\"CatalogReference\") is not None:\n catalog_reference = assign_action.find(\"CatalogReference\")\n properties = OpenScenarioParser.get_catalog_entry(catalogs, catalog_reference).find('Properties')\n\n module = None\n args = {}\n for prop in properties:\n if prop.attrib.get('name') == \"module\":\n module = prop.attrib.get('value')\n else:\n args[prop.attrib.get('name')] = prop.attrib.get('value')\n\n override_action = xml_tree.find('OverrideControllerValueAction')\n for child in override_action:\n if strtobool(child.attrib.get('active')):\n raise NotImplementedError(\"Controller override actions are not yet supported\")\n\n return module, args\n\n @staticmethod\n def get_route(xml_tree, catalogs):\n \"\"\"\n Extract the route from the OSC XML or a catalog\n\n Args:\n xml_tree: Containing the route information,\n or the reference to the catalog it is defined in.\n catalogs: XML Catalogs that could contain the Route\n\n returns:\n waypoints: List of route waypoints\n \"\"\"\n route = None\n\n if xml_tree.find('Route') is not None:\n route = xml_tree.find('Route')\n elif xml_tree.find('CatalogReference') is not None:\n catalog_reference = xml_tree.find(\"CatalogReference\")\n route = OpenScenarioParser.get_catalog_entry(catalogs, catalog_reference)\n else:\n raise AttributeError(\"Unknown private FollowRoute action\")\n\n waypoints = []\n if route is not None:\n for waypoint in route.iter('Waypoint'):\n position = waypoint.find('Position')\n transform = OpenScenarioParser.convert_position_to_transform(position)\n waypoints.append(transform)\n\n return waypoints\n\n @staticmethod\n def convert_position_to_transform(position, actor_list=None):\n \"\"\"\n Convert an OpenScenario position into a CARLA transform\n\n Not supported: Road, RelativeRoad, Lane, RelativeLane as the PythonAPI currently\n does not provide sufficient access to OpenDrive information\n Also not supported is Route. This can be added by checking additional\n route information\n \"\"\"\n\n if position.find('WorldPosition') is not None:\n world_pos = position.find('WorldPosition')\n x = float(world_pos.attrib.get('x', 0))\n y = float(world_pos.attrib.get('y', 0))\n z = float(world_pos.attrib.get('z', 0))\n yaw = math.degrees(float(world_pos.attrib.get('h', 0)))\n pitch = math.degrees(float(world_pos.attrib.get('p', 0)))\n roll = math.degrees(float(world_pos.attrib.get('r', 0)))\n if not OpenScenarioParser.use_carla_coordinate_system:\n y = y * (-1.0)\n yaw = yaw * (-1.0)\n return carla.Transform(carla.Location(x=x, y=y, z=z), carla.Rotation(yaw=yaw, pitch=pitch, roll=roll))\n\n elif ((position.find('RelativeWorldPosition') is not None) or\n (position.find('RelativeObjectPosition') is not None) or\n (position.find('RelativeLanePosition') is not None)):\n\n if position.find('RelativeWorldPosition') is not None:\n rel_pos = position.find('RelativeWorldPosition')\n if position.find('RelativeObjectPosition') is not None:\n rel_pos = position.find('RelativeObjectPosition')\n if position.find('RelativeLanePosition') is not None:\n rel_pos = position.find('RelativeLanePosition')\n\n # get relative object and relative position\n obj = rel_pos.attrib.get('entityRef')\n obj_actor = None\n actor_transform = None\n\n if actor_list is not None:\n for actor in actor_list:\n if actor.rolename == obj:\n obj_actor = actor\n actor_transform = actor.transform\n else:\n for actor in CarlaDataProvider.get_world().get_actors():\n if 'role_name' in actor.attributes and actor.attributes['role_name'] == obj:\n obj_actor = actor\n actor_transform = obj_actor.get_transform()\n break\n\n if obj_actor is None:\n raise AttributeError(\"Object '{}' provided as position reference is not known\".format(obj))\n\n # calculate orientation h, p, r\n is_absolute = False\n dyaw = 0\n dpitch = 0\n droll = 0\n if rel_pos.find('Orientation') is not None:\n orientation = rel_pos.find('Orientation')\n is_absolute = (orientation.attrib.get('type') == \"absolute\")\n dyaw = math.degrees(float(orientation.attrib.get('h', 0)))\n dpitch = math.degrees(float(orientation.attrib.get('p', 0)))\n droll = math.degrees(float(orientation.attrib.get('r', 0)))\n\n if not OpenScenarioParser.use_carla_coordinate_system:\n dyaw = dyaw * (-1.0)\n\n yaw = actor_transform.rotation.yaw\n pitch = actor_transform.rotation.pitch\n roll = actor_transform.rotation.roll\n\n if not is_absolute:\n yaw = yaw + dyaw\n pitch = pitch + dpitch\n roll = roll + droll\n else:\n yaw = dyaw\n pitch = dpitch\n roll = droll\n\n # calculate location x, y, z\n # dx, dy, dz\n if ((position.find('RelativeWorldPosition') is not None) or\n (position.find('RelativeObjectPosition') is not None)):\n dx = float(rel_pos.attrib.get('dx', 0))\n dy = float(rel_pos.attrib.get('dy', 0))\n dz = float(rel_pos.attrib.get('dz', 0))\n\n if not OpenScenarioParser.use_carla_coordinate_system:\n dy = dy * (-1.0)\n\n x = actor_transform.location.x + dx\n y = actor_transform.location.y + dy\n z = actor_transform.location.z + dz\n\n # dLane, ds, offset\n elif position.find('RelativeLanePosition') is not None:\n dlane = float(rel_pos.attrib.get('dLane'))\n ds = float(rel_pos.attrib.get('ds'))\n offset = float(rel_pos.attrib.get('offset', 0.0))\n\n carla_map = CarlaDataProvider.get_map()\n relative_waypoint = carla_map.get_waypoint(actor_transform.location)\n\n if dlane == 0:\n wp = relative_waypoint\n elif dlane == -1:\n wp = relative_waypoint.get_left_lane()\n elif dlane == 1:\n wp = relative_waypoint.get_right_lane()\n if wp is None:\n raise AttributeError(\"Object '{}' position with dLane={} is not valid\".format(obj, dlane))\n\n if ds < 0:\n ds = (-1.0) * ds\n wp = wp.previous(ds)[-1]\n else:\n wp = wp.next(ds)[-1]\n\n # Adapt transform according to offset\n h = math.radians(wp.transform.rotation.yaw)\n x_offset = math.sin(h) * offset\n y_offset = math.cos(h) * offset\n\n if OpenScenarioParser.use_carla_coordinate_system:\n x_offset = x_offset * (-1.0)\n y_offset = y_offset * (-1.0)\n\n x = wp.transform.location.x + x_offset\n y = wp.transform.location.y + y_offset\n z = wp.transform.location.z\n\n return carla.Transform(carla.Location(x=x, y=y, z=z), carla.Rotation(yaw=yaw, pitch=pitch, roll=roll))\n\n # Not implemented\n elif position.find('RoadPosition') is not None:\n raise NotImplementedError(\"Road positions are not yet supported\")\n elif position.find('RelativeRoadPosition') is not None:\n raise NotImplementedError(\"RelativeRoad positions are not yet supported\")\n elif position.find('LanePosition') is not None:\n lane_pos = position.find('LanePosition')\n road_id = int(lane_pos.attrib.get('roadId', 0))\n lane_id = int(lane_pos.attrib.get('laneId', 0))\n offset = float(lane_pos.attrib.get('offset', 0))\n s = float(lane_pos.attrib.get('s', 0))\n is_absolute = True\n waypoint = CarlaDataProvider.get_map().get_waypoint_xodr(road_id, lane_id, s)\n if waypoint is None:\n raise AttributeError(\"Lane position cannot be found\")\n\n transform = waypoint.transform\n if lane_pos.find('Orientation') is not None:\n orientation = lane_pos.find('Orientation')\n dyaw = math.degrees(float(orientation.attrib.get('h', 0)))\n dpitch = math.degrees(float(orientation.attrib.get('p', 0)))\n droll = math.degrees(float(orientation.attrib.get('r', 0)))\n\n if not OpenScenarioParser.use_carla_coordinate_system:\n dyaw = dyaw * (-1.0)\n\n transform.rotation.yaw = transform.rotation.yaw + dyaw\n transform.rotation.pitch = transform.rotation.pitch + dpitch\n transform.rotation.roll = transform.rotation.roll + droll\n\n if offset != 0:\n forward_vector = transform.rotation.get_forward_vector()\n orthogonal_vector = carla.Vector3D(x=-forward_vector.y, y=forward_vector.x, z=forward_vector.z)\n transform.location.x = transform.location.x + offset * orthogonal_vector.x\n transform.location.y = transform.location.y + offset * orthogonal_vector.y\n\n return transform\n elif position.find('RoutePosition') is not None:\n raise NotImplementedError(\"Route positions are not yet supported\")\n else:\n raise AttributeError(\"Unknown position\")\n\n @staticmethod\n def convert_condition_to_atomic(condition, actor_list):\n \"\"\"\n Convert an OpenSCENARIO condition into a Behavior/Criterion atomic\n\n If there is a delay defined in the condition, then the condition is checked after the delay time\n passed by, e.g. .\n\n Note: Not all conditions are currently supported.\n \"\"\"\n\n atomic = None\n delay_atomic = None\n condition_name = condition.attrib.get('name')\n\n if condition.attrib.get('delay') is not None and str(condition.attrib.get('delay')) != '0':\n delay = float(condition.attrib.get('delay'))\n delay_atomic = TimeOut(delay)\n\n if condition.find('ByEntityCondition') is not None:\n\n trigger_actor = None # A-priori validation ensures that this will be not None\n triggered_actor = None\n\n for triggering_entities in condition.find('ByEntityCondition').iter('TriggeringEntities'):\n for entity in triggering_entities.iter('EntityRef'):\n for actor in actor_list:\n if entity.attrib.get('entityRef', None) == actor.attributes['role_name']:\n trigger_actor = actor\n break\n\n for entity_condition in condition.find('ByEntityCondition').iter('EntityCondition'):\n if entity_condition.find('EndOfRoadCondition') is not None:\n end_road_condition = entity_condition.find('EndOfRoadCondition')\n condition_duration = float(end_road_condition.attrib.get('duration'))\n atomic_cls = py_trees.meta.inverter(EndofRoadTest)\n atomic = atomic_cls(\n trigger_actor, condition_duration, terminate_on_failure=True, name=condition_name)\n elif entity_condition.find('CollisionCondition') is not None:\n\n collision_condition = entity_condition.find('CollisionCondition')\n\n if collision_condition.find('EntityRef') is not None:\n collision_entity = collision_condition.find('EntityRef')\n\n for actor in actor_list:\n if collision_entity.attrib.get('entityRef', None) == actor.attributes['role_name']:\n triggered_actor = actor\n break\n\n if triggered_actor is None:\n raise AttributeError(\"Cannot find actor '{}' for condition\".format(\n collision_condition.attrib.get('entityRef', None)))\n\n atomic_cls = py_trees.meta.inverter(CollisionTest)\n atomic = atomic_cls(trigger_actor, other_actor=triggered_actor,\n terminate_on_failure=True, name=condition_name)\n\n elif collision_condition.find('ByType') is not None:\n collision_type = collision_condition.find('ByType').attrib.get('type', None)\n\n triggered_type = OpenScenarioParser.actor_types[collision_type]\n\n atomic_cls = py_trees.meta.inverter(CollisionTest)\n atomic = atomic_cls(trigger_actor, other_actor_type=triggered_type,\n terminate_on_failure=True, name=condition_name)\n\n else:\n atomic_cls = py_trees.meta.inverter(CollisionTest)\n atomic = atomic_cls(trigger_actor, terminate_on_failure=True, name=condition_name)\n\n elif entity_condition.find('OffroadCondition') is not None:\n off_condition = entity_condition.find('OffroadCondition')\n condition_duration = float(off_condition.attrib.get('duration'))\n atomic_cls = py_trees.meta.inverter(OffRoadTest)\n atomic = atomic_cls(\n trigger_actor, condition_duration, terminate_on_failure=True, name=condition_name)\n elif entity_condition.find('TimeHeadwayCondition') is not None:\n headtime_condition = entity_condition.find('TimeHeadwayCondition')\n\n condition_value = float(headtime_condition.attrib.get('value'))\n\n condition_rule = headtime_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n\n condition_freespace = strtobool(headtime_condition.attrib.get('freespace', False))\n if condition_freespace:\n raise NotImplementedError(\n \"TimeHeadwayCondition: freespace attribute is currently not implemented\")\n condition_along_route = strtobool(headtime_condition.attrib.get('alongRoute', False))\n\n for actor in actor_list:\n if headtime_condition.attrib.get('entityRef', None) == actor.attributes['role_name']:\n triggered_actor = actor\n break\n if triggered_actor is None:\n raise AttributeError(\"Cannot find actor '{}' for condition\".format(\n headtime_condition.attrib.get('entityRef', None)))\n\n atomic = InTimeToArrivalToVehicle(\n trigger_actor, triggered_actor, condition_value,\n condition_along_route, condition_operator, condition_name\n )\n\n elif entity_condition.find('TimeToCollisionCondition') is not None:\n ttc_condition = entity_condition.find('TimeToCollisionCondition')\n\n condition_rule = ttc_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n\n condition_value = ttc_condition.attrib.get('value')\n condition_target = ttc_condition.find('TimeToCollisionConditionTarget')\n\n condition_freespace = strtobool(ttc_condition.attrib.get('freespace', False))\n if condition_freespace:\n raise NotImplementedError(\n \"TimeToCollisionCondition: freespace attribute is currently not implemented\")\n condition_along_route = strtobool(ttc_condition.attrib.get('alongRoute', False))\n\n if condition_target.find('Position') is not None:\n position = condition_target.find('Position')\n atomic = InTimeToArrivalToOSCPosition(\n trigger_actor, position, condition_value, condition_along_route, condition_operator)\n else:\n for actor in actor_list:\n if ttc_condition.attrib.get('EntityRef', None) == actor.attributes['role_name']:\n triggered_actor = actor\n break\n if triggered_actor is None:\n raise AttributeError(\"Cannot find actor '{}' for condition\".format(\n ttc_condition.attrib.get('EntityRef', None)))\n\n atomic = InTimeToArrivalToVehicle(\n trigger_actor, triggered_actor, condition_value,\n condition_along_route, condition_operator, condition_name)\n elif entity_condition.find('AccelerationCondition') is not None:\n accel_condition = entity_condition.find('AccelerationCondition')\n condition_value = float(accel_condition.attrib.get('value'))\n condition_rule = accel_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n atomic = TriggerAcceleration(\n trigger_actor, condition_value, condition_operator, condition_name)\n elif entity_condition.find('StandStillCondition') is not None:\n ss_condition = entity_condition.find('StandStillCondition')\n duration = float(ss_condition.attrib.get('duration'))\n atomic = StandStill(trigger_actor, condition_name, duration)\n elif entity_condition.find('SpeedCondition') is not None:\n spd_condition = entity_condition.find('SpeedCondition')\n condition_value = float(spd_condition.attrib.get('value'))\n condition_rule = spd_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n\n atomic = TriggerVelocity(\n trigger_actor, condition_value, condition_operator, condition_name)\n elif entity_condition.find('RelativeSpeedCondition') is not None:\n relspd_condition = entity_condition.find('RelativeSpeedCondition')\n condition_value = float(relspd_condition.attrib.get('value'))\n condition_rule = relspd_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n\n for actor in actor_list:\n if relspd_condition.attrib.get('entityRef', None) == actor.attributes['role_name']:\n triggered_actor = actor\n break\n\n if triggered_actor is None:\n raise AttributeError(\"Cannot find actor '{}' for condition\".format(\n relspd_condition.attrib.get('entityRef', None)))\n\n atomic = RelativeVelocityToOtherActor(\n trigger_actor, triggered_actor, condition_value, condition_operator, condition_name)\n elif entity_condition.find('TraveledDistanceCondition') is not None:\n distance_condition = entity_condition.find('TraveledDistanceCondition')\n distance_value = float(distance_condition.attrib.get('value'))\n atomic = DriveDistance(trigger_actor, distance_value, name=condition_name)\n elif entity_condition.find('ReachPositionCondition') is not None:\n rp_condition = entity_condition.find('ReachPositionCondition')\n distance_value = float(rp_condition.attrib.get('tolerance'))\n position = rp_condition.find('Position')\n atomic = InTriggerDistanceToOSCPosition(\n trigger_actor, position, distance_value, name=condition_name)\n elif entity_condition.find('DistanceCondition') is not None:\n distance_condition = entity_condition.find('DistanceCondition')\n\n distance_value = float(distance_condition.attrib.get('value'))\n\n distance_rule = distance_condition.attrib.get('rule')\n distance_operator = OpenScenarioParser.operators[distance_rule]\n\n distance_freespace = strtobool(distance_condition.attrib.get('freespace', False))\n if distance_freespace:\n raise NotImplementedError(\n \"DistanceCondition: freespace attribute is currently not implemented\")\n distance_along_route = strtobool(distance_condition.attrib.get('alongRoute', False))\n\n if distance_condition.find('Position') is not None:\n position = distance_condition.find('Position')\n atomic = InTriggerDistanceToOSCPosition(\n trigger_actor, position, distance_value, distance_along_route,\n distance_operator, name=condition_name)\n\n elif entity_condition.find('RelativeDistanceCondition') is not None:\n distance_condition = entity_condition.find('RelativeDistanceCondition')\n distance_value = float(distance_condition.attrib.get('value'))\n\n distance_freespace = strtobool(distance_condition.attrib.get('freespace', False))\n if distance_freespace:\n raise NotImplementedError(\n \"RelativeDistanceCondition: freespace attribute is currently not implemented\")\n if distance_condition.attrib.get('relativeDistanceType') == \"cartesianDistance\":\n for actor in actor_list:\n if distance_condition.attrib.get('entityRef', None) == actor.attributes['role_name']:\n triggered_actor = actor\n break\n\n if triggered_actor is None:\n raise AttributeError(\"Cannot find actor '{}' for condition\".format(\n distance_condition.attrib.get('entityRef', None)))\n\n condition_rule = distance_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n atomic = InTriggerDistanceToVehicle(\n triggered_actor, trigger_actor, distance_value, condition_operator, name=condition_name)\n else:\n raise NotImplementedError(\n \"RelativeDistance condition with the given specification is not yet supported\")\n elif condition.find('ByValueCondition') is not None:\n value_condition = condition.find('ByValueCondition')\n if value_condition.find('ParameterCondition') is not None:\n parameter_condition = value_condition.find('ParameterCondition')\n arg_name = parameter_condition.attrib.get('parameterRef')\n value = parameter_condition.attrib.get('value')\n if value != '':\n arg_value = float(value)\n else:\n arg_value = 0\n parameter_condition.attrib.get('rule')\n\n if condition_name in globals():\n criterion_instance = globals()[condition_name]\n else:\n raise AttributeError(\n \"The condition {} cannot be mapped to a criterion atomic\".format(condition_name))\n\n atomic = py_trees.composites.Parallel(\"Evaluation Criteria for multiple ego vehicles\")\n for triggered_actor in actor_list:\n if arg_name != '':\n atomic.add_child(criterion_instance(triggered_actor, arg_value))\n else:\n atomic.add_child(criterion_instance(triggered_actor))\n elif value_condition.find('SimulationTimeCondition') is not None:\n simtime_condition = value_condition.find('SimulationTimeCondition')\n value = float(simtime_condition.attrib.get('value'))\n rule = simtime_condition.attrib.get('rule')\n atomic = SimulationTimeCondition(value, success_rule=rule)\n elif value_condition.find('TimeOfDayCondition') is not None:\n tod_condition = value_condition.find('TimeOfDayCondition')\n condition_date = tod_condition.attrib.get('dateTime')\n condition_rule = tod_condition.attrib.get('rule')\n condition_operator = OpenScenarioParser.operators[condition_rule]\n atomic = TimeOfDayComparison(condition_date, condition_operator, condition_name)\n elif value_condition.find('StoryboardElementStateCondition') is not None:\n state_condition = value_condition.find('StoryboardElementStateCondition')\n element_name = state_condition.attrib.get('storyboardElementRef')\n element_type = state_condition.attrib.get('storyboardElementType')\n state = state_condition.attrib.get('state')\n if state == \"startTransition\":\n atomic = OSCStartEndCondition(element_type, element_name, rule=\"START\", name=state + \"Condition\")\n elif state == \"stopTransition\" or state == \"endTransition\" or state == \"completeState\":\n atomic = OSCStartEndCondition(element_type, element_name, rule=\"END\", name=state + \"Condition\")\n else:\n raise NotImplementedError(\n \"Only start, stop, endTransitions and completeState are currently supported\")\n elif value_condition.find('UserDefinedValueCondition') is not None:\n raise NotImplementedError(\"ByValue UserDefinedValue conditions are not yet supported\")\n elif value_condition.find('TrafficSignalCondition') is not None:\n tl_condition = value_condition.find('TrafficSignalCondition')\n\n name_condition = tl_condition.attrib.get('name')\n traffic_light = OpenScenarioParser.get_traffic_light_from_osc_name(name_condition)\n\n tl_state = tl_condition.attrib.get('state').upper()\n if tl_state not in OpenScenarioParser.tl_states:\n raise KeyError(\"CARLA only supports Green, Red, Yellow or Off\")\n state_condition = OpenScenarioParser.tl_states[tl_state]\n\n atomic = WaitForTrafficLightState(\n traffic_light, state_condition, name=condition_name)\n elif value_condition.find('TrafficSignalControllerCondition') is not None:\n raise NotImplementedError(\"ByValue TrafficSignalController conditions are not yet supported\")\n else:\n raise AttributeError(\"Unknown ByValue condition\")\n\n else:\n raise AttributeError(\"Unknown condition\")\n\n if delay_atomic is not None and atomic is not None:\n new_atomic = py_trees.composites.Sequence(\"delayed sequence\")\n new_atomic.add_child(delay_atomic)\n new_atomic.add_child(atomic)\n else:\n new_atomic = atomic\n\n return new_atomic\n\n @staticmethod\n def convert_maneuver_to_atomic(action, actor, catalogs):\n \"\"\"\n Convert an OpenSCENARIO maneuver action into a Behavior atomic\n\n Note not all OpenSCENARIO actions are currently supported\n \"\"\"\n maneuver_name = action.attrib.get('name', 'unknown')\n\n if action.find('GlobalAction') is not None:\n global_action = action.find('GlobalAction')\n if global_action.find('InfrastructureAction') is not None:\n infrastructure_action = global_action.find('InfrastructureAction').find('TrafficSignalAction')\n if infrastructure_action.find('TrafficSignalStateAction') is not None:\n traffic_light_action = infrastructure_action.find('TrafficSignalStateAction')\n\n name_condition = traffic_light_action.attrib.get('name')\n traffic_light = OpenScenarioParser.get_traffic_light_from_osc_name(name_condition)\n\n tl_state = traffic_light_action.attrib.get('state').upper()\n if tl_state not in OpenScenarioParser.tl_states:\n raise KeyError(\"CARLA only supports Green, Red, Yellow or Off\")\n traffic_light_state = OpenScenarioParser.tl_states[tl_state]\n\n atomic = TrafficLightStateSetter(\n traffic_light, traffic_light_state, name=maneuver_name + \"_\" + str(traffic_light.id))\n else:\n raise NotImplementedError(\"TrafficLights can only be influenced via TrafficSignalStateAction\")\n elif global_action.find('EnvironmentAction') is not None:\n weather_behavior = ChangeWeather(\n OpenScenarioParser.get_weather_from_env_action(global_action, catalogs))\n friction_behavior = ChangeRoadFriction(\n OpenScenarioParser.get_friction_from_env_action(global_action, catalogs))\n\n env_behavior = py_trees.composites.Parallel(\n policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ALL, name=maneuver_name)\n\n env_behavior.add_child(\n oneshot_behavior(variable_name=maneuver_name + \">WeatherUpdate\", behaviour=weather_behavior))\n env_behavior.add_child(\n oneshot_behavior(variable_name=maneuver_name + \">FrictionUpdate\", behaviour=friction_behavior))\n\n return env_behavior\n\n else:\n raise NotImplementedError(\"Global actions are not yet supported\")\n elif action.find('UserDefinedAction') is not None:\n user_defined_action = action.find('UserDefinedAction')\n if user_defined_action.find('CustomCommandAction') is not None:\n command = user_defined_action.find('CustomCommandAction').attrib.get('type')\n atomic = RunScript(command, base_path=OpenScenarioParser.osc_filepath, name=maneuver_name)\n elif action.find('PrivateAction') is not None:\n private_action = action.find('PrivateAction')\n\n if private_action.find('LongitudinalAction') is not None:\n private_action = private_action.find('LongitudinalAction')\n\n if private_action.find('SpeedAction') is not None:\n long_maneuver = private_action.find('SpeedAction')\n\n # duration and distance\n distance = float('inf')\n duration = float('inf')\n dimension = long_maneuver.find(\"SpeedActionDynamics\").attrib.get('dynamicsDimension')\n if dimension == \"distance\":\n distance = float(long_maneuver.find(\"SpeedActionDynamics\").attrib.get('value', float(\"inf\")))\n else:\n duration = float(long_maneuver.find(\"SpeedActionDynamics\").attrib.get('value', float(\"inf\")))\n\n # absolute velocity with given target speed\n if long_maneuver.find(\"SpeedActionTarget\").find(\"AbsoluteTargetSpeed\") is not None:\n target_speed = float(long_maneuver.find(\"SpeedActionTarget\").find(\n \"AbsoluteTargetSpeed\").attrib.get('value', 0))\n atomic = ChangeActorTargetSpeed(\n actor, target_speed, distance=distance, duration=duration, name=maneuver_name)\n\n # relative velocity to given actor\n if long_maneuver.find(\"SpeedActionTarget\").find(\"RelativeTargetSpeed\") is not None:\n relative_speed = long_maneuver.find(\"SpeedActionTarget\").find(\"RelativeTargetSpeed\")\n obj = relative_speed.attrib.get('entityRef')\n value = float(relative_speed.attrib.get('value', 0))\n value_type = relative_speed.attrib.get('speedTargetValueType')\n continuous = relative_speed.attrib.get('continuous')\n\n for traffic_actor in CarlaDataProvider.get_world().get_actors():\n if 'role_name' in traffic_actor.attributes and traffic_actor.attributes['role_name'] == obj:\n obj_actor = traffic_actor\n\n atomic = ChangeActorTargetSpeed(actor,\n target_speed=0.0,\n relative_actor=obj_actor,\n value=value,\n value_type=value_type,\n continuous=continuous,\n distance=distance,\n duration=duration,\n name=maneuver_name)\n\n elif private_action.find('LongitudinalDistanceAction') is not None:\n raise NotImplementedError(\"Longitudinal distance actions are not yet supported\")\n else:\n raise AttributeError(\"Unknown longitudinal action\")\n elif private_action.find('LateralAction') is not None:\n private_action = private_action.find('LateralAction')\n if private_action.find('LaneChangeAction') is not None:\n # Note: LaneChangeActions are currently only supported for RelativeTargetLane\n # with +1 or -1 referring to the action actor\n lat_maneuver = private_action.find('LaneChangeAction')\n target_lane_rel = float(lat_maneuver.find(\"LaneChangeTarget\").find(\n \"RelativeTargetLane\").attrib.get('value', 0))\n # duration and distance\n distance = float('inf')\n duration = float('inf')\n dimension = lat_maneuver.find(\"LaneChangeActionDynamics\").attrib.get('dynamicsDimension')\n if dimension == \"distance\":\n distance = float(\n lat_maneuver.find(\"LaneChangeActionDynamics\").attrib.get('value', float(\"inf\")))\n else:\n duration = float(\n lat_maneuver.find(\"LaneChangeActionDynamics\").attrib.get('value', float(\"inf\")))\n atomic = ChangeActorLateralMotion(actor,\n direction=\"left\" if target_lane_rel < 0 else \"right\",\n distance_lane_change=distance,\n distance_other_lane=1000,\n name=maneuver_name)\n else:\n raise AttributeError(\"Unknown lateral action\")\n elif private_action.find('VisibilityAction') is not None:\n raise NotImplementedError(\"Visibility actions are not yet supported\")\n elif private_action.find('SynchronizeAction') is not None:\n raise NotImplementedError(\"Synchronization actions are not yet supported\")\n elif private_action.find('ActivateControllerAction') is not None:\n private_action = private_action.find('ActivateControllerAction')\n activate = strtobool(private_action.attrib.get('longitudinal'))\n atomic = ChangeAutoPilot(actor, activate, name=maneuver_name)\n elif private_action.find('ControllerAction') is not None:\n controller_action = private_action.find('ControllerAction')\n module, args = OpenScenarioParser.get_controller(controller_action, catalogs)\n atomic = ChangeActorControl(actor, control_py_module=module, args=args)\n elif private_action.find('TeleportAction') is not None:\n position = private_action.find('TeleportAction')\n atomic = ActorTransformSetterToOSCPosition(actor, position, name=maneuver_name)\n elif private_action.find('RoutingAction') is not None:\n private_action = private_action.find('RoutingAction')\n if private_action.find('AssignRouteAction') is not None:\n # How to handle relative positions here? This might chance at runtime?!\n route_action = private_action.find('AssignRouteAction')\n waypoints = OpenScenarioParser.get_route(route_action, catalogs)\n atomic = ChangeActorWaypoints(actor, waypoints=waypoints, name=maneuver_name)\n elif private_action.find('FollowTrajectoryAction') is not None:\n raise NotImplementedError(\"Private FollowTrajectory actions are not yet supported\")\n elif private_action.find('AcquirePositionAction') is not None:\n route_action = private_action.find('AcquirePositionAction')\n osc_position = route_action.find('Position')\n position = OpenScenarioParser.convert_position_to_transform(osc_position)\n atomic = ChangeActorWaypointsToReachPosition(actor, position=position, name=maneuver_name)\n else:\n raise AttributeError(\"Unknown private routing action\")\n else:\n raise AttributeError(\"Unknown private action\")\n\n else:\n if list(action):\n raise AttributeError(\"Unknown action: {}\".format(maneuver_name))\n else:\n return Idle(duration=0, name=maneuver_name)\n\n return atomic\n" }, { "path": "scenario_runner/srunner/tools/py_trees_port.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2015 Daniel Stonier\n# Copyright (c) 2020 Intel Corporation\n#\n# License: BSD\n# https://raw.githubusercontent.com/splintered-reality/py_trees/devel/LICENSE\n\n\"\"\"\nThis module provides a backport from newer py_trees releases (> 1.0)\nTo use certain features also within ScenarioRunner, which uses py_trees\nversion 0.8.x\n\"\"\"\n\nimport py_trees\n\n\nclass Decorator(py_trees.behaviour.Behaviour):\n\n \"\"\"\n A decorator is responsible for handling the lifecycle of a single\n child beneath\n\n This is taken from py_trees 1.2 to work with our current implementation\n that uses py_trees 0.8.2\n \"\"\"\n\n def __init__(self, child, name):\n \"\"\"\n Common initialisation steps for a decorator - type checks and\n name construction (if None is given).\n Args:\n name (:obj:`str`): the decorator name\n child (:class:`~py_trees.behaviour.Behaviour`): the child to be decorated\n Raises:\n TypeError: if the child is not an instance of :class:`~py_trees.behaviour.Behaviour`\n \"\"\"\n # Checks\n if not isinstance(child, py_trees.behaviour.Behaviour):\n raise TypeError(\"A decorator's child must be an instance of py_trees.behaviours.Behaviour\")\n # Initialise\n super(Decorator, self).__init__(name=name)\n self.children.append(child)\n # Give a convenient alias\n self.decorated = self.children[0]\n self.decorated.parent = self\n\n def tick(self):\n \"\"\"\n A decorator's tick is exactly the same as a normal proceedings for\n a Behaviour's tick except that it also ticks the decorated child node.\n Yields:\n :class:`~py_trees.behaviour.Behaviour`: a reference to itself or one of its children\n \"\"\"\n self.logger.debug(\"%s.tick()\" % self.__class__.__name__)\n # initialise just like other behaviours/composites\n if self.status != py_trees.common.Status.RUNNING:\n self.initialise()\n # interrupt proceedings and process the child node\n # (including any children it may have as well)\n for node in self.decorated.tick():\n yield node\n # resume normal proceedings for a Behaviour's tick\n new_status = self.update()\n if new_status not in list(py_trees.common.Status):\n self.logger.error(\n \"A behaviour returned an invalid status, setting to INVALID [%s][%s]\" % (new_status, self.name))\n new_status = py_trees.common.Status.INVALID\n if new_status != py_trees.common.Status.RUNNING:\n self.stop(new_status)\n self.status = new_status\n yield self\n\n def stop(self, new_status=py_trees.common.Status.INVALID):\n \"\"\"\n As with other composites, it checks if the child is running\n and stops it if that is the case.\n Args:\n new_status (:class:`~py_trees.common.Status`): the behaviour is transitioning to this new status\n \"\"\"\n self.logger.debug(\"%s.stop(%s)\" % (self.__class__.__name__, new_status))\n self.terminate(new_status)\n # priority interrupt handling\n if new_status == py_trees.common.Status.INVALID:\n self.decorated.stop(new_status)\n # if the decorator returns SUCCESS/FAILURE and should stop the child\n if self.decorated.status == py_trees.common.Status.RUNNING:\n self.decorated.stop(py_trees.common.Status.INVALID)\n self.status = new_status\n\n def tip(self):\n \"\"\"\n Get the *tip* of this behaviour's subtree (if it has one) after it's last\n tick. This corresponds to the the deepest node that was running before the\n subtree traversal reversed direction and headed back to this node.\n \"\"\"\n if self.decorated.status != py_trees.common.Status.INVALID:\n return self.decorated.tip()\n\n return super(Decorator, self).tip()\n\n\ndef oneshot_behavior(variable_name, behaviour, name=None):\n \"\"\"\n This is taken from py_trees.idiom.oneshot.\n \"\"\"\n if not name:\n name = behaviour.name\n\n subtree_root = py_trees.composites.Selector(name=name)\n\n # Initialize the variables\n blackboard = py_trees.blackboard.Blackboard()\n _ = blackboard.set(variable_name, False)\n\n # Wait until the scenario has ended\n check_flag = py_trees.blackboard.CheckBlackboardVariable(\n name=variable_name + \" Done?\",\n variable_name=variable_name,\n expected_value=True,\n clearing_policy=py_trees.common.ClearingPolicy.ON_INITIALISE\n )\n set_flag = py_trees.blackboard.SetBlackboardVariable(\n name=\"Mark Done\",\n variable_name=variable_name,\n variable_value=True\n )\n # If it's a sequence, don't double-nest it in a redundant manner\n if isinstance(behaviour, py_trees.composites.Sequence):\n behaviour.add_child(set_flag)\n sequence = behaviour\n else:\n sequence = py_trees.composites.Sequence(name=\"OneShot\")\n sequence.add_children([behaviour, set_flag])\n\n subtree_root.add_children([check_flag, sequence])\n return subtree_root\n" }, { "path": "scenario_runner/srunner/tools/route_manipulation.py", "content": "#!/usr/bin/env python\n# Copyright (c) 2018-2019 Intel Labs.\n# authors: German Ros (german.ros@intel.com), Felipe Codevilla (felipe.alcm@gmail.com)\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nModule to manipulate the routes, by making then more or less dense (Up to a certain parameter).\nIt also contains functions to convert the CARLA world location do GPS coordinates.\n\"\"\"\n\nimport math\nimport xml.etree.ElementTree as ET\n\nfrom agents.navigation.global_route_planner import GlobalRoutePlanner\nfrom agents.navigation.global_route_planner_dao import GlobalRoutePlannerDAO\n\nfrom agents.navigation.local_planner import RoadOption\n\n\ndef _location_to_gps(lat_ref, lon_ref, location):\n \"\"\"\n Convert from world coordinates to GPS coordinates\n :param lat_ref: latitude reference for the current map\n :param lon_ref: longitude reference for the current map\n :param location: location to translate\n :return: dictionary with lat, lon and height\n \"\"\"\n\n EARTH_RADIUS_EQUA = 6378137.0 # pylint: disable=invalid-name\n scale = math.cos(lat_ref * math.pi / 180.0)\n mx = scale * lon_ref * math.pi * EARTH_RADIUS_EQUA / 180.0\n my = scale * EARTH_RADIUS_EQUA * math.log(math.tan((90.0 + lat_ref) * math.pi / 360.0))\n mx += location.x\n my -= location.y\n\n lon = mx * 180.0 / (math.pi * EARTH_RADIUS_EQUA * scale)\n lat = 360.0 * math.atan(math.exp(my / (EARTH_RADIUS_EQUA * scale))) / math.pi - 90.0\n z = location.z\n\n return {'lat': lat, 'lon': lon, 'z': z}\n\n\ndef location_route_to_gps(route, lat_ref, lon_ref):\n \"\"\"\n Locate each waypoint of the route into gps, (lat long ) representations.\n :param route:\n :param lat_ref:\n :param lon_ref:\n :return:\n \"\"\"\n gps_route = []\n\n for transform, connection in route:\n gps_point = _location_to_gps(lat_ref, lon_ref, transform.location)\n gps_route.append((gps_point, connection))\n\n return gps_route\n\n\ndef _get_latlon_ref(world):\n \"\"\"\n Convert from waypoints world coordinates to CARLA GPS coordinates\n :return: tuple with lat and lon coordinates\n \"\"\"\n xodr = world.get_map().to_opendrive()\n tree = ET.ElementTree(ET.fromstring(xodr))\n\n # default reference\n lat_ref = 42.0\n lon_ref = 2.0\n\n for opendrive in tree.iter(\"OpenDRIVE\"):\n for header in opendrive.iter(\"header\"):\n for georef in header.iter(\"geoReference\"):\n if georef.text:\n str_list = georef.text.split(' ')\n for item in str_list:\n if '+lat_0' in item:\n lat_ref = float(item.split('=')[1])\n if '+lon_0' in item:\n lon_ref = float(item.split('=')[1])\n return lat_ref, lon_ref\n\n\ndef downsample_route(route, sample_factor):\n \"\"\"\n Downsample the route by some factor.\n :param route: the trajectory , has to contain the waypoints and the road options\n :param sample_factor: Maximum distance between samples\n :return: returns the ids of the final route that can\n \"\"\"\n\n ids_to_sample = []\n prev_option = None\n dist = 0\n\n for i, point in enumerate(route):\n curr_option = point[1]\n\n # Lane changing\n if curr_option in (RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT):\n ids_to_sample.append(i)\n dist = 0\n\n # When road option changes\n elif prev_option != curr_option and prev_option not in (RoadOption.CHANGELANELEFT, RoadOption.CHANGELANERIGHT):\n ids_to_sample.append(i)\n dist = 0\n\n # After a certain max distance\n elif dist > sample_factor:\n ids_to_sample.append(i)\n dist = 0\n\n # At the end\n elif i == len(route) - 1:\n ids_to_sample.append(i)\n dist = 0\n\n # Compute the distance traveled\n else:\n curr_location = point[0].location\n prev_location = route[i - 1][0].location\n dist += curr_location.distance(prev_location)\n\n prev_option = curr_option\n\n return ids_to_sample\n\n\ndef interpolate_trajectory(world, waypoints_trajectory, hop_resolution=1.0):\n \"\"\"\n Given some raw keypoints interpolate a full dense trajectory to be used by the user.\n :param world: an reference to the CARLA world so we can use the planner\n :param waypoints_trajectory: the current coarse trajectory\n :param hop_resolution: is the resolution, how dense is the provided trajectory going to be made\n :return: the full interpolated route both in GPS coordinates and also in its original form.\n \"\"\"\n\n dao = GlobalRoutePlannerDAO(world.get_map(), hop_resolution)\n grp = GlobalRoutePlanner(dao)\n grp.setup()\n # Obtain route plan\n route = []\n for i in range(len(waypoints_trajectory) - 1): # Goes until the one before the last.\n\n waypoint = waypoints_trajectory[i]\n waypoint_next = waypoints_trajectory[i + 1]\n interpolated_trace = grp.trace_route(waypoint, waypoint_next)\n for wp_tuple in interpolated_trace:\n route.append((wp_tuple[0].transform, wp_tuple[1]))\n\n # Increase the route position to avoid fails\n\n lat_ref, lon_ref = _get_latlon_ref(world)\n\n return location_route_to_gps(route, lat_ref, lon_ref), route\n" }, { "path": "scenario_runner/srunner/tools/route_parser.py", "content": "#!/usr/bin/env python\n\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nModule used to parse all the route and scenario configuration parameters.\n\"\"\"\n\nimport json\nimport math\nimport xml.etree.ElementTree as ET\n\nimport carla\nfrom agents.navigation.local_planner import RoadOption\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteScenarioConfiguration\n\n#check this threshold, it could be a bit larger but not so large that we cluster scenarios.\nTRIGGER_THRESHOLD = 2.0 # Threshold to say if a trigger position is new or repeated, works for matching positions\nTRIGGER_ANGLE_THRESHOLD = 10 # Threshold to say if two angles can be considering matching when matching transforms.\n\n\nclass RouteParser(object):\n\n \"\"\"\n Pure static class used to parse all the route and scenario configuration parameters.\n \"\"\"\n\n @staticmethod\n def parse_annotations_file(annotation_filename):\n \"\"\"\n Return the annotations of which positions where the scenarios are going to happen.\n :param annotation_filename: the filename for the anotations file\n :return:\n \"\"\"\n\n with open(annotation_filename, 'r') as f:\n annotation_dict = json.loads(f.read())\n\n final_dict = {}\n\n for town_dict in annotation_dict['available_scenarios']:\n final_dict.update(town_dict)\n\n return final_dict # the file has a current maps name that is an one element vec\n\n @staticmethod\n def parse_routes_file(route_filename, scenario_file, single_route=None):\n \"\"\"\n Returns a list of route elements.\n :param route_filename: the path to a set of routes.\n :param single_route: If set, only this route shall be returned\n :return: List of dicts containing the waypoints, id and town of the routes\n \"\"\"\n\n list_route_descriptions = []\n tree = ET.parse(route_filename)\n for route in tree.iter(\"route\"):\n\n route_id = route.attrib['id']\n if single_route and route_id != single_route:\n continue\n\n new_config = RouteScenarioConfiguration()\n new_config.town = route.attrib['town']\n new_config.name = \"RouteScenario_{}\".format(route_id)\n new_config.weather = RouteParser.parse_weather(route)\n new_config.scenario_file = scenario_file\n\n waypoint_list = [] # the list of waypoints that can be found on this route\n for waypoint in route.iter('waypoint'):\n waypoint_list.append(carla.Location(x=float(waypoint.attrib['x']),\n y=float(waypoint.attrib['y']),\n z=float(waypoint.attrib['z'])))\n\n new_config.trajectory = waypoint_list\n\n list_route_descriptions.append(new_config)\n\n return list_route_descriptions\n\n @staticmethod\n def parse_weather(route):\n \"\"\"\n Returns a carla.WeatherParameters with the corresponding weather for that route. If the route\n has no weather attribute, the default one is triggered.\n \"\"\"\n\n route_weather = route.find(\"weather\")\n if route_weather is None:\n\n weather = carla.WeatherParameters(sun_altitude_angle=70)\n\n else:\n weather = carla.WeatherParameters()\n for weather_attrib in route.iter(\"weather\"):\n\n if 'cloudiness' in weather_attrib.attrib:\n weather.cloudiness = float(weather_attrib.attrib['cloudiness'])\n if 'precipitation' in weather_attrib.attrib:\n weather.precipitation = float(weather_attrib.attrib['precipitation'])\n if 'precipitation_deposits' in weather_attrib.attrib:\n weather.precipitation_deposits = float(weather_attrib.attrib['precipitation_deposits'])\n if 'wind_intensity' in weather_attrib.attrib:\n weather.wind_intensity = float(weather_attrib.attrib['wind_intensity'])\n if 'sun_azimuth_angle' in weather_attrib.attrib:\n weather.sun_azimuth_angle = float(weather_attrib.attrib['sun_azimuth_angle'])\n if 'sun_altitude_angle' in weather_attrib.attrib:\n weather.sun_altitude_angle = float(weather_attrib.attrib['sun_altitude_angle'])\n if 'wetness' in weather_attrib.attrib:\n weather.wetness = float(weather_attrib.attrib['wetness'])\n if 'fog_distance' in weather_attrib.attrib:\n weather.fog_distance = float(weather_attrib.attrib['fog_distance'])\n if 'fog_density' in weather_attrib.attrib:\n weather.fog_density = float(weather_attrib.attrib['fog_density'])\n\n return weather\n\n @staticmethod\n def check_trigger_position(new_trigger, existing_triggers):\n \"\"\"\n Check if this trigger position already exists or if it is a new one.\n :param new_trigger:\n :param existing_triggers:\n :return:\n \"\"\"\n\n for trigger_id in existing_triggers.keys():\n trigger = existing_triggers[trigger_id]\n dx = trigger['x'] - new_trigger['x']\n dy = trigger['y'] - new_trigger['y']\n distance = math.sqrt(dx * dx + dy * dy)\n\n dyaw = (trigger['yaw'] - new_trigger['yaw']) % 360\n if distance < TRIGGER_THRESHOLD \\\n and (dyaw < TRIGGER_ANGLE_THRESHOLD or dyaw > (360 - TRIGGER_ANGLE_THRESHOLD)):\n return trigger_id\n\n return None\n\n @staticmethod\n def convert_waypoint_float(waypoint):\n \"\"\"\n Convert waypoint values to float\n \"\"\"\n waypoint['x'] = float(waypoint['x'])\n waypoint['y'] = float(waypoint['y'])\n waypoint['z'] = float(waypoint['z'])\n waypoint['yaw'] = float(waypoint['yaw'])\n\n @staticmethod\n def match_world_location_to_route(world_location, route_description):\n \"\"\"\n We match this location to a given route.\n world_location:\n route_description:\n \"\"\"\n def match_waypoints(waypoint1, wtransform):\n \"\"\"\n Check if waypoint1 and wtransform are similar\n \"\"\"\n dx = float(waypoint1['x']) - wtransform.location.x\n dy = float(waypoint1['y']) - wtransform.location.y\n dz = float(waypoint1['z']) - wtransform.location.z\n dpos = math.sqrt(dx * dx + dy * dy + dz * dz)\n\n dyaw = (float(waypoint1['yaw']) - wtransform.rotation.yaw) % 360\n\n return dpos < TRIGGER_THRESHOLD \\\n and (dyaw < TRIGGER_ANGLE_THRESHOLD or dyaw > (360 - TRIGGER_ANGLE_THRESHOLD))\n\n match_position = 0\n # T this function can be optimized to run on Log(N) time\n for route_waypoint in route_description:\n if match_waypoints(world_location, route_waypoint[0]):\n return match_position\n match_position += 1\n\n return None\n\n @staticmethod\n def get_scenario_type(scenario, match_position, trajectory):\n \"\"\"\n Some scenarios have different types depending on the route.\n :param scenario: the scenario name\n :param match_position: the matching position for the scenarion\n :param trajectory: the route trajectory the ego is following\n :return: tag representing this subtype\n\n Also used to check which are not viable (Such as an scenario\n that triggers when turning but the route doesnt')\n WARNING: These tags are used at:\n - VehicleTurningRoute\n - SignalJunctionCrossingRoute\n and changes to these tags will affect them\n \"\"\"\n\n def check_this_waypoint(tuple_wp_turn):\n \"\"\"\n Decides whether or not the waypoint will define the scenario behavior\n \"\"\"\n if RoadOption.LANEFOLLOW == tuple_wp_turn[1]:\n return False\n elif RoadOption.CHANGELANELEFT == tuple_wp_turn[1]:\n return False\n elif RoadOption.CHANGELANERIGHT == tuple_wp_turn[1]:\n return False\n return True\n\n # Unused tag for the rest of scenarios,\n # can't be None as they are still valid scenarios\n subtype = 'valid'\n\n if scenario == 'Scenario4':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S4left'\n elif RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S4right'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario7':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S7left'\n elif RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S7right'\n elif RoadOption.STRAIGHT == tuple_wp_turn[1]:\n subtype = 'S7opposite'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario8':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.LEFT == tuple_wp_turn[1]:\n subtype = 'S8left'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n if scenario == 'Scenario9':\n for tuple_wp_turn in trajectory[match_position:]:\n if check_this_waypoint(tuple_wp_turn):\n if RoadOption.RIGHT == tuple_wp_turn[1]:\n subtype = 'S9right'\n else:\n subtype = None\n break # Avoid checking all of them\n subtype = None\n\n return subtype\n\n @staticmethod\n def scan_route_for_scenarios(route_name, trajectory, world_annotations):\n \"\"\"\n Just returns a plain list of possible scenarios that can happen in this route by matching\n the locations from the scenario into the route description\n\n :return: A list of scenario definitions with their correspondent parameters\n \"\"\"\n\n # the triggers dictionaries:\n existent_triggers = {}\n # We have a table of IDs and trigger positions associated\n possible_scenarios = {}\n\n # Keep track of the trigger ids being added\n latest_trigger_id = 0\n\n for town_name in world_annotations.keys():\n if town_name != route_name:\n continue\n\n scenarios = world_annotations[town_name]\n for scenario in scenarios: # For each existent scenario\n if \"scenario_type\" not in scenario:\n break\n scenario_name = scenario[\"scenario_type\"]\n for event in scenario[\"available_event_configurations\"]:\n waypoint = event['transform'] # trigger point of this scenario\n RouteParser.convert_waypoint_float(waypoint)\n # We match trigger point to the route, now we need to check if the route affects\n match_position = RouteParser.match_world_location_to_route(\n waypoint, trajectory)\n if match_position is not None:\n # We match a location for this scenario, create a scenario object so this scenario\n # can be instantiated later\n\n if 'other_actors' in event:\n other_vehicles = event['other_actors']\n else:\n other_vehicles = None\n scenario_subtype = RouteParser.get_scenario_type(scenario_name, match_position,\n trajectory)\n if scenario_subtype is None:\n continue\n scenario_description = {\n 'name': scenario_name,\n 'other_actors': other_vehicles,\n 'trigger_position': waypoint,\n 'scenario_type': scenario_subtype, # some scenarios have route dependent configs\n }\n\n trigger_id = RouteParser.check_trigger_position(waypoint, existent_triggers)\n if trigger_id is None:\n # This trigger does not exist create a new reference on existent triggers\n existent_triggers.update({latest_trigger_id: waypoint})\n # Update a reference for this trigger on the possible scenarios\n possible_scenarios.update({latest_trigger_id: []})\n trigger_id = latest_trigger_id\n # Increment the latest trigger\n latest_trigger_id += 1\n\n possible_scenarios[trigger_id].append(scenario_description)\n\n return possible_scenarios, existent_triggers\n" }, { "path": "scenario_runner/srunner/tools/scenario_helper.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nSummary of useful helper functions for scenarios\n\"\"\"\n\nimport math\nimport shapely.geometry\nimport shapely.affinity\n\nimport numpy as np\n\nimport carla\nfrom agents.tools.misc import vector\nfrom agents.navigation.local_planner import RoadOption\n\nfrom srunner.scenariomanager.carla_data_provider import CarlaDataProvider\n\n\ndef get_distance_along_route(route, target_location):\n \"\"\"\n Calculate the distance of the given location along the route\n\n Note: If the location is not along the route, the route length will be returned\n \"\"\"\n\n wmap = CarlaDataProvider.get_map()\n covered_distance = 0\n prev_position = None\n found = False\n\n # Don't use the input location, use the corresponding wp as location\n target_location_from_wp = wmap.get_waypoint(target_location).transform.location\n\n for position, _ in route:\n\n location = target_location_from_wp\n\n # Don't perform any calculations for the first route point\n if not prev_position:\n prev_position = position\n continue\n\n # Calculate distance between previous and current route point\n interval_length_squared = ((prev_position.x - position.x) ** 2) + ((prev_position.y - position.y) ** 2)\n distance_squared = ((location.x - prev_position.x) ** 2) + ((location.y - prev_position.y) ** 2)\n\n # Close to the current position? Stop calculation\n if distance_squared < 0.01:\n break\n\n if distance_squared < 400 and not distance_squared < interval_length_squared:\n # Check if a neighbor lane is closer to the route\n # Do this only in a close distance to correct route interval, otherwise the computation load is too high\n starting_wp = wmap.get_waypoint(location)\n wp = starting_wp.get_left_lane()\n while wp is not None:\n new_location = wp.transform.location\n new_distance_squared = ((new_location.x - prev_position.x) ** 2) + (\n (new_location.y - prev_position.y) ** 2)\n\n if np.sign(starting_wp.lane_id) != np.sign(wp.lane_id):\n break\n\n if new_distance_squared < distance_squared:\n distance_squared = new_distance_squared\n location = new_location\n else:\n break\n\n wp = wp.get_left_lane()\n\n wp = starting_wp.get_right_lane()\n while wp is not None:\n new_location = wp.transform.location\n new_distance_squared = ((new_location.x - prev_position.x) ** 2) + (\n (new_location.y - prev_position.y) ** 2)\n\n if np.sign(starting_wp.lane_id) != np.sign(wp.lane_id):\n break\n\n if new_distance_squared < distance_squared:\n distance_squared = new_distance_squared\n location = new_location\n else:\n break\n\n wp = wp.get_right_lane()\n\n if distance_squared < interval_length_squared:\n # The location could be inside the current route interval, if route/lane ids match\n # Note: This assumes a sufficiently small route interval\n # An alternative is to compare orientations, however, this also does not work for\n # long route intervals\n\n curr_wp = wmap.get_waypoint(position)\n prev_wp = wmap.get_waypoint(prev_position)\n wp = wmap.get_waypoint(location)\n\n if prev_wp and curr_wp and wp:\n if wp.road_id == prev_wp.road_id or wp.road_id == curr_wp.road_id:\n # Roads match, now compare the sign of the lane ids\n if (np.sign(wp.lane_id) == np.sign(prev_wp.lane_id) or\n np.sign(wp.lane_id) == np.sign(curr_wp.lane_id)):\n # The location is within the current route interval\n covered_distance += math.sqrt(distance_squared)\n found = True\n break\n\n covered_distance += math.sqrt(interval_length_squared)\n prev_position = position\n\n return covered_distance, found\n\n\ndef get_crossing_point(actor):\n \"\"\"\n Get the next crossing point location in front of the ego vehicle\n\n @return point of crossing\n \"\"\"\n wp_cross = CarlaDataProvider.get_map().get_waypoint(actor.get_location())\n\n while not wp_cross.is_intersection:\n wp_cross = wp_cross.next(2)[0]\n\n crossing = carla.Location(x=wp_cross.transform.location.x,\n y=wp_cross.transform.location.y, z=wp_cross.transform.location.z)\n\n return crossing\n\n\ndef get_geometric_linear_intersection(ego_actor, other_actor):\n \"\"\"\n Obtain a intersection point between two actor's location by using their waypoints (wp)\n\n @return point of intersection of the two vehicles\n \"\"\"\n\n wp_ego_1 = CarlaDataProvider.get_map().get_waypoint(ego_actor.get_location())\n wp_ego_2 = wp_ego_1.next(1)[0]\n x_ego_1 = wp_ego_1.transform.location.x\n y_ego_1 = wp_ego_1.transform.location.y\n x_ego_2 = wp_ego_2.transform.location.x\n y_ego_2 = wp_ego_2.transform.location.y\n\n wp_other_1 = CarlaDataProvider.get_world().get_map().get_waypoint(other_actor.get_location())\n wp_other_2 = wp_other_1.next(1)[0]\n x_other_1 = wp_other_1.transform.location.x\n y_other_1 = wp_other_1.transform.location.y\n x_other_2 = wp_other_2.transform.location.x\n y_other_2 = wp_other_2.transform.location.y\n\n s = np.vstack([(x_ego_1, y_ego_1), (x_ego_2, y_ego_2), (x_other_1, y_other_1), (x_other_2, y_other_2)])\n h = np.hstack((s, np.ones((4, 1))))\n line1 = np.cross(h[0], h[1])\n line2 = np.cross(h[2], h[3])\n x, y, z = np.cross(line1, line2)\n if z == 0:\n return (float('inf'), float('inf'))\n\n intersection = carla.Location(x=x / z, y=y / z, z=0)\n\n return intersection\n\n\ndef get_location_in_distance(actor, distance):\n \"\"\"\n Obtain a location in a given distance from the current actor's location.\n Note: Search is stopped on first intersection.\n\n @return obtained location and the traveled distance\n \"\"\"\n waypoint = CarlaDataProvider.get_map().get_waypoint(actor.get_location())\n traveled_distance = 0\n while not waypoint.is_intersection and traveled_distance < distance:\n waypoint_new = waypoint.next(1.0)[-1]\n traveled_distance += waypoint_new.transform.location.distance(waypoint.transform.location)\n waypoint = waypoint_new\n\n return waypoint.transform.location, traveled_distance\n\n\ndef get_location_in_distance_from_wp(waypoint, distance, stop_at_junction=True):\n \"\"\"\n Obtain a location in a given distance from the current actor's location.\n Note: Search is stopped on first intersection.\n\n @return obtained location and the traveled distance\n \"\"\"\n traveled_distance = 0\n while not (waypoint.is_intersection and stop_at_junction) and traveled_distance < distance:\n wp_next = waypoint.next(1.0)\n if wp_next:\n waypoint_new = wp_next[-1]\n traveled_distance += waypoint_new.transform.location.distance(waypoint.transform.location)\n waypoint = waypoint_new\n else:\n break\n\n return waypoint.transform.location, traveled_distance\n\n\ndef get_waypoint_in_distance(waypoint, distance):\n \"\"\"\n Obtain a waypoint in a given distance from the current actor's location.\n Note: Search is stopped on first intersection.\n @return obtained waypoint and the traveled distance\n \"\"\"\n traveled_distance = 0\n while not waypoint.is_intersection and traveled_distance < distance:\n waypoint_new = waypoint.next(1.0)[-1]\n traveled_distance += waypoint_new.transform.location.distance(waypoint.transform.location)\n waypoint = waypoint_new\n\n return waypoint, traveled_distance\n\n\ndef generate_target_waypoint_list(waypoint, turn=0):\n \"\"\"\n This method follow waypoints to a junction and choose path based on turn input.\n Turn input: LEFT -> -1, RIGHT -> 1, STRAIGHT -> 0\n @returns a waypoint list from the starting point to the end point according to turn input\n \"\"\"\n reached_junction = False\n threshold = math.radians(0.1)\n plan = []\n while True:\n wp_choice = waypoint.next(2)\n if len(wp_choice) > 1:\n reached_junction = True\n waypoint = choose_at_junction(waypoint, wp_choice, turn)\n else:\n waypoint = wp_choice[0]\n plan.append((waypoint, RoadOption.LANEFOLLOW))\n # End condition for the behavior\n if turn != 0 and reached_junction and len(plan) >= 3:\n v_1 = vector(\n plan[-2][0].transform.location,\n plan[-1][0].transform.location)\n v_2 = vector(\n plan[-3][0].transform.location,\n plan[-2][0].transform.location)\n angle_wp = math.acos(\n np.dot(v_1, v_2) / abs((np.linalg.norm(v_1) * np.linalg.norm(v_2))))\n if angle_wp < threshold:\n break\n elif reached_junction and not plan[-1][0].is_intersection:\n break\n\n return plan, plan[-1][0]\n\n\ndef generate_target_waypoint_list_multilane(waypoint, change='left',\n distance_same_lane=10,\n distance_other_lane=25,\n total_lane_change_distance=25, check='true'):\n \"\"\"\n This methods generates a waypoint list which leads the vehicle to a parallel lane.\n The change input must be 'left' or 'right', depending on which lane you want to change.\n\n The step distance between waypoints on the same lane is 2m.\n The step distance between the lane change is set to 25m.\n\n @returns a waypoint list from the starting point to the end point on a right or left parallel lane.\n \"\"\"\n plan = []\n plan.append((waypoint, RoadOption.LANEFOLLOW)) # start position\n\n step_distance = 2\n\n # check if lane change possible\n if check == 'true':\n lane_change_possibilities = ['Left', 'Right', 'Both']\n if str(waypoint.lane_change) not in lane_change_possibilities:\n # ERROR, lane change is not possible\n return None\n\n # same lane\n distance = 0\n while distance < distance_same_lane:\n next_wp = plan[-1][0].next(step_distance)\n distance += next_wp[0].transform.location.distance(plan[-1][0].transform.location)\n plan.append((next_wp[0], RoadOption.LANEFOLLOW))\n\n target_lane_id = None\n if change == 'left':\n # go left\n wp_left = plan[-1][0].get_left_lane()\n target_lane_id = wp_left.lane_id\n next_wp = wp_left.next(total_lane_change_distance)\n plan.append((next_wp[0], RoadOption.LANEFOLLOW))\n elif change == 'right':\n # go right\n wp_right = plan[-1][0].get_right_lane()\n target_lane_id = wp_right.lane_id\n next_wp = wp_right.next(total_lane_change_distance)\n plan.append((next_wp[0], RoadOption.LANEFOLLOW))\n else:\n # ERROR, input value for change must be 'left' or 'right'\n return None\n\n # other lane\n distance = 0\n while distance < distance_other_lane:\n next_wp = plan[-1][0].next(step_distance)\n distance += next_wp[0].transform.location.distance(plan[-1][0].transform.location)\n plan.append((next_wp[0], RoadOption.LANEFOLLOW))\n\n return plan, target_lane_id\n\n\ndef generate_target_waypoint(waypoint, turn=0):\n \"\"\"\n This method follow waypoints to a junction and choose path based on turn input.\n Turn input: LEFT -> -1, RIGHT -> 1, STRAIGHT -> 0\n @returns a waypoint list according to turn input\n \"\"\"\n sampling_radius = 1\n reached_junction = False\n wp_list = []\n while True:\n\n wp_choice = waypoint.next(sampling_radius)\n # Choose path at intersection\n if not reached_junction and (len(wp_choice) > 1 or wp_choice[0].is_junction):\n reached_junction = True\n waypoint = choose_at_junction(waypoint, wp_choice, turn)\n else:\n waypoint = wp_choice[0]\n wp_list.append(waypoint)\n # End condition for the behavior\n if reached_junction and not wp_list[-1].is_junction:\n break\n return wp_list[-1]\n\n\ndef generate_target_waypoint_in_route(waypoint, route):\n \"\"\"\n This method follow waypoints to a junction\n @returns a waypoint list according to turn input\n \"\"\"\n wmap = CarlaDataProvider.get_map()\n reached_junction = False\n\n # Get the route location\n shortest_distance = float('inf')\n for index, route_pos in enumerate(route):\n wp = route_pos[0]\n trigger_location = waypoint.transform.location\n\n dist_to_route = trigger_location.distance(wp)\n if dist_to_route <= shortest_distance:\n closest_index = index\n shortest_distance = dist_to_route\n\n route_location = route[closest_index][0]\n index = closest_index\n\n while True:\n # Get the next route location\n index = min(index + 1, len(route))\n route_location = route[index][0]\n road_option = route[index][1]\n\n # Enter the junction\n if not reached_junction and (road_option in (RoadOption.LEFT, RoadOption.RIGHT, RoadOption.STRAIGHT)):\n reached_junction = True\n\n # End condition for the behavior, at the end of the junction\n if reached_junction and (road_option not in (RoadOption.LEFT, RoadOption.RIGHT, RoadOption.STRAIGHT)):\n break\n\n return wmap.get_waypoint(route_location)\n\n\ndef choose_at_junction(current_waypoint, next_choices, direction=0):\n \"\"\"\n This function chooses the appropriate waypoint from next_choices based on direction\n \"\"\"\n current_transform = current_waypoint.transform\n current_location = current_transform.location\n projected_location = current_location + \\\n carla.Location(\n x=math.cos(math.radians(current_transform.rotation.yaw)),\n y=math.sin(math.radians(current_transform.rotation.yaw)))\n current_vector = vector(current_location, projected_location)\n cross_list = []\n cross_to_waypoint = dict()\n for waypoint in next_choices:\n waypoint = waypoint.next(10)[0]\n select_vector = vector(current_location, waypoint.transform.location)\n cross = np.cross(current_vector, select_vector)[2]\n cross_list.append(cross)\n cross_to_waypoint[cross] = waypoint\n select_cross = None\n if direction > 0:\n select_cross = max(cross_list)\n elif direction < 0:\n select_cross = min(cross_list)\n else:\n select_cross = min(cross_list, key=abs)\n\n return cross_to_waypoint[select_cross]\n\n\ndef get_intersection(ego_actor, other_actor):\n \"\"\"\n Obtain a intersection point between two actor's location\n @return the intersection location\n \"\"\"\n waypoint = CarlaDataProvider.get_map().get_waypoint(ego_actor.get_location())\n waypoint_other = CarlaDataProvider.get_map().get_waypoint(other_actor.get_location())\n max_dist = float(\"inf\")\n distance = float(\"inf\")\n while distance <= max_dist:\n max_dist = distance\n current_location = waypoint.transform.location\n waypoint_choice = waypoint.next(1)\n # Select the straighter path at intersection\n if len(waypoint_choice) > 1:\n max_dot = -1 * float('inf')\n loc_projection = current_location + carla.Location(\n x=math.cos(math.radians(waypoint.transform.rotation.yaw)),\n y=math.sin(math.radians(waypoint.transform.rotation.yaw)))\n v_current = vector(current_location, loc_projection)\n for wp_select in waypoint_choice:\n v_select = vector(current_location, wp_select.transform.location)\n dot_select = np.dot(v_current, v_select)\n if dot_select > max_dot:\n max_dot = dot_select\n waypoint = wp_select\n else:\n waypoint = waypoint_choice[0]\n distance = current_location.distance(waypoint_other.transform.location)\n\n return current_location\n\n\ndef detect_lane_obstacle(actor, extension_factor=3, margin=1.02):\n \"\"\"\n This function identifies if an obstacle is present in front of the reference actor\n \"\"\"\n world = CarlaDataProvider.get_world()\n world_actors = world.get_actors().filter('vehicle.*')\n actor_bbox = actor.bounding_box\n actor_transform = actor.get_transform()\n actor_location = actor_transform.location\n actor_vector = actor_transform.rotation.get_forward_vector()\n actor_vector = np.array([actor_vector.x, actor_vector.y])\n actor_vector = actor_vector / np.linalg.norm(actor_vector)\n actor_vector = actor_vector * (extension_factor - 1) * actor_bbox.extent.x\n actor_location = actor_location + carla.Location(actor_vector[0], actor_vector[1])\n actor_yaw = actor_transform.rotation.yaw\n\n is_hazard = False\n for adversary in world_actors:\n if adversary.id != actor.id and \\\n actor_transform.location.distance(adversary.get_location()) < 50:\n adversary_bbox = adversary.bounding_box\n adversary_transform = adversary.get_transform()\n adversary_loc = adversary_transform.location\n adversary_yaw = adversary_transform.rotation.yaw\n overlap_adversary = RotatedRectangle(\n adversary_loc.x, adversary_loc.y,\n 2 * margin * adversary_bbox.extent.x, 2 * margin * adversary_bbox.extent.y, adversary_yaw)\n overlap_actor = RotatedRectangle(\n actor_location.x, actor_location.y,\n 2 * margin * actor_bbox.extent.x * extension_factor, 2 * margin * actor_bbox.extent.y, actor_yaw)\n overlap_area = overlap_adversary.intersection(overlap_actor).area\n if overlap_area > 0:\n is_hazard = True\n break\n\n return is_hazard\n\n\nclass RotatedRectangle(object):\n\n \"\"\"\n This class contains method to draw rectangle and find intersection point.\n \"\"\"\n\n def __init__(self, c_x, c_y, width, height, angle):\n self.c_x = c_x\n self.c_y = c_y\n self.w = width # pylint: disable=invalid-name\n self.h = height # pylint: disable=invalid-name\n self.angle = angle\n\n def get_contour(self):\n \"\"\"\n create contour\n \"\"\"\n w = self.w\n h = self.h\n c = shapely.geometry.box(-w / 2.0, -h / 2.0, w / 2.0, h / 2.0)\n rc = shapely.affinity.rotate(c, self.angle)\n return shapely.affinity.translate(rc, self.c_x, self.c_y)\n\n def intersection(self, other):\n \"\"\"\n Obtain a intersection point between two contour.\n \"\"\"\n return self.get_contour().intersection(other.get_contour())\n" }, { "path": "scenario_runner/srunner/tools/scenario_parser.py", "content": "#!/usr/bin/env python\n\n# Copyright (c) 2019 Intel Corporation\n#\n# This work is licensed under the terms of the MIT license.\n# For a copy, see .\n\n\"\"\"\nThis module provides access to a scenario configuration parser\n\"\"\"\n\nimport glob\nimport os\nimport xml.etree.ElementTree as ET\n\nfrom srunner.scenarioconfigs.scenario_configuration import ScenarioConfiguration, ActorConfigurationData\nfrom srunner.scenarioconfigs.route_scenario_configuration import RouteConfiguration\n\n\nclass ScenarioConfigurationParser(object):\n\n \"\"\"\n Pure static class providing access to parser methods for scenario configuration files (*.xml)\n \"\"\"\n\n @staticmethod\n def parse_scenario_configuration(scenario_name, config_file_name):\n \"\"\"\n Parse all scenario configuration files at srunner/examples and the additional\n config files, providing a list of ScenarioConfigurations @return\n\n If scenario_name starts with \"group:\" all scenarios that\n have that type are parsed and returned. Otherwise only the\n scenario that matches the scenario_name is parsed and returned.\n \"\"\"\n\n list_of_config_files = glob.glob(\"{}/srunner/examples/*.xml\".format(os.getenv('SCENARIO_RUNNER_ROOT', \"./\")))\n\n if config_file_name != '':\n list_of_config_files.append(config_file_name)\n\n single_scenario_only = True\n if scenario_name.startswith(\"group:\"):\n single_scenario_only = False\n scenario_name = scenario_name[6:]\n\n scenario_configurations = []\n\n for file_name in list_of_config_files:\n tree = ET.parse(file_name)\n\n for scenario in tree.iter(\"scenario\"):\n\n scenario_config_name = scenario.attrib.get('name', None)\n scenario_config_type = scenario.attrib.get('type', None)\n\n if single_scenario_only:\n # Check the scenario is the correct one\n if scenario_config_name != scenario_name:\n continue\n else:\n # Check the scenario is of the correct type\n if scenario_config_type != scenario_name:\n continue\n\n new_config = ScenarioConfiguration()\n new_config.town = scenario.attrib.get('town', None)\n new_config.name = scenario_config_name\n new_config.type = scenario_config_type\n new_config.other_actors = []\n new_config.ego_vehicles = []\n new_config.trigger_points = []\n\n for weather in scenario.iter(\"weather\"):\n new_config.weather.cloudiness = float(weather.attrib.get(\"cloudiness\", 0))\n new_config.weather.precipitation = float(weather.attrib.get(\"precipitation\", 0))\n new_config.weather.precipitation_deposits = float(weather.attrib.get(\"precipitation_deposits\", 0))\n new_config.weather.wind_intensity = float(weather.attrib.get(\"wind_intensity\", 0.35))\n new_config.weather.sun_azimuth_angle = float(weather.attrib.get(\"sun_azimuth_angle\", 0.0))\n new_config.weather.sun_altitude_angle = float(weather.attrib.get(\"sun_altitude_angle\", 15.0))\n new_config.weather.fog_density = float(weather.attrib.get(\"fog_density\", 0.0))\n new_config.weather.fog_distance = float(weather.attrib.get(\"fog_distance\", 0.0))\n new_config.weather.wetness = float(weather.attrib.get(\"wetness\", 0.0))\n\n for ego_vehicle in scenario.iter(\"ego_vehicle\"):\n\n new_config.ego_vehicles.append(ActorConfigurationData.parse_from_node(ego_vehicle, 'hero'))\n new_config.trigger_points.append(new_config.ego_vehicles[-1].transform)\n\n for route in scenario.iter(\"route\"):\n route_conf = RouteConfiguration()\n route_conf.parse_xml(route)\n new_config.route = route_conf\n\n for other_actor in scenario.iter(\"other_actor\"):\n new_config.other_actors.append(ActorConfigurationData.parse_from_node(other_actor, 'scenario'))\n\n scenario_configurations.append(new_config)\n\n return scenario_configurations\n\n @staticmethod\n def get_list_of_scenarios(config_file_name):\n \"\"\"\n Parse *all* config files and provide a list with all scenarios @return\n \"\"\"\n\n list_of_config_files = glob.glob(\"{}/srunner/examples/*.xml\".format(os.getenv('SCENARIO_RUNNER_ROOT', \"./\")))\n list_of_config_files += glob.glob(\"{}/srunner/examples/*.xosc\".format(os.getenv('SCENARIO_RUNNER_ROOT', \"./\")))\n\n if config_file_name != '':\n list_of_config_files.append(config_file_name)\n\n scenarios = []\n for file_name in list_of_config_files:\n if \".xosc\" in file_name:\n tree = ET.parse(file_name)\n scenarios.append(\"{} (OpenSCENARIO)\".format(tree.find(\"FileHeader\").attrib.get('description', None)))\n else:\n tree = ET.parse(file_name)\n for scenario in tree.iter(\"scenario\"):\n scenarios.append(scenario.attrib.get('name', None))\n\n return scenarios\n" }, { "path": "scenario_runner/srunner/utilities/code_check_and_formatting.sh", "content": "#!/bin/bash\n\nautopep8 scenario_runner.py --in-place --max-line-length=120\nautopep8 srunner/scenariomanager/*.py --in-place --max-line-length=120\nautopep8 srunner/scenariomanager/actorcontrols/*.py --in-place --max-line-length=120\nautopep8 srunner/scenariomanager/scenarioatomics/*.py --in-place --max-line-length=120\nautopep8 srunner/scenarios/*.py --in-place --max-line-length=120\nautopep8 srunner/autoagents/*.py --in-place --max-line-length=120\nautopep8 srunner/tools/*.py --in-place --max-line-length=120\nautopep8 srunner/scenarioconfigs/*.py --in-place --max-line-length=120\n\n\npylint --rcfile=.pylintrc --disable=I srunner/scenariomanager/\npylint --rcfile=.pylintrc srunner/scenarios/\npylint --rcfile=.pylintrc srunner/autoagents/\npylint --rcfile=.pylintrc srunner/tools/\npylint --rcfile=.pylintrc srunner/scenarioconfigs/\npylint --rcfile=.pylintrc scenario_runner.py\n" } ]