From 7de14c9791d24c3fb3e914cb37bf727fb29aa681 Mon Sep 17 00:00:00 2001 From: Yuchao Feng <99387180+fyctime052@users.noreply.github.com> Date: Fri, 26 Jan 2024 14:17:35 +0800 Subject: [PATCH] Add files via upload --- roach/config/config_agent.yaml | 64 ++++ roach/config/config_agent_original.yaml | 64 ++++ roach/criteria/blocked.py | 30 ++ roach/criteria/collision.py | 117 ++++++++ roach/criteria/encounter_light.py | 26 ++ roach/criteria/outside_route_lane.py | 99 +++++++ roach/criteria/route_deviation.py | 34 +++ roach/criteria/run_red_light.py | 64 ++++ roach/criteria/run_stop_sign.py | 157 ++++++++++ roach/log/ckpt_11833344.pth | Bin 0 -> 8890031 bytes roach/models/distributions.py | 278 ++++++++++++++++++ roach/models/ppo.py | 277 ++++++++++++++++++ roach/models/ppo_buffer.py | 261 +++++++++++++++++ roach/models/ppo_policy.py | 244 ++++++++++++++++ roach/models/torch_layers.py | 114 ++++++++ roach/models/torch_util.py | 104 +++++++ roach/obs_manager/actor_state/control.py | 27 ++ roach/obs_manager/actor_state/route.py | 71 +++++ roach/obs_manager/actor_state/speed.py | 46 +++ roach/obs_manager/actor_state/velocity.py | 43 +++ roach/obs_manager/birdview/chauffeurnet.py | 290 +++++++++++++++++++ roach/obs_manager/birdview/hdmap_generate.py | 33 +++ roach/obs_manager/birdview/maps/Town01.h5 | Bin 0 -> 335340 bytes roach/obs_manager/birdview/maps/Town02.h5 | Bin 0 -> 199449 bytes roach/obs_manager/birdview/maps/Town03.h5 | Bin 0 -> 418827 bytes roach/obs_manager/birdview/maps/Town04.h5 | Bin 0 -> 914150 bytes roach/obs_manager/birdview/maps/Town05.h5 | Bin 0 -> 477203 bytes roach/obs_manager/birdview/maps/Town06.h5 | Bin 0 -> 1201140 bytes roach/obs_manager/birdview/maps/Town07.h5 | Bin 0 -> 354093 bytes roach/obs_manager/birdview/maps/Town10HD.h5 | Bin 0 -> 224960 bytes roach/rl_birdview_agent.py | 58 ++++ roach/utils/config_utils.py | 148 ++++++++++ roach/utils/expert_noiser.py | 167 +++++++++++ roach/utils/rl_birdview_wrapper.py | 142 +++++++++ roach/utils/traffic_light.py | 199 +++++++++++++ roach/utils/transforms.py | 128 ++++++++ roach/utils/wandb_callback.py | 211 ++++++++++++++ 37 files changed, 3496 insertions(+) create mode 100644 roach/config/config_agent.yaml create mode 100644 roach/config/config_agent_original.yaml create mode 100644 roach/criteria/blocked.py create mode 100644 roach/criteria/collision.py create mode 100644 roach/criteria/encounter_light.py create mode 100644 roach/criteria/outside_route_lane.py create mode 100644 roach/criteria/route_deviation.py create mode 100644 roach/criteria/run_red_light.py create mode 100644 roach/criteria/run_stop_sign.py create mode 100644 roach/log/ckpt_11833344.pth create mode 100644 roach/models/distributions.py create mode 100644 roach/models/ppo.py create mode 100644 roach/models/ppo_buffer.py create mode 100644 roach/models/ppo_policy.py create mode 100644 roach/models/torch_layers.py create mode 100644 roach/models/torch_util.py create mode 100644 roach/obs_manager/actor_state/control.py create mode 100644 roach/obs_manager/actor_state/route.py create mode 100644 roach/obs_manager/actor_state/speed.py create mode 100644 roach/obs_manager/actor_state/velocity.py create mode 100644 roach/obs_manager/birdview/chauffeurnet.py create mode 100644 roach/obs_manager/birdview/hdmap_generate.py create mode 100644 roach/obs_manager/birdview/maps/Town01.h5 create mode 100644 roach/obs_manager/birdview/maps/Town02.h5 create mode 100644 roach/obs_manager/birdview/maps/Town03.h5 create mode 100644 roach/obs_manager/birdview/maps/Town04.h5 create mode 100644 roach/obs_manager/birdview/maps/Town05.h5 create mode 100644 roach/obs_manager/birdview/maps/Town06.h5 create mode 100644 roach/obs_manager/birdview/maps/Town07.h5 create mode 100644 roach/obs_manager/birdview/maps/Town10HD.h5 create mode 100644 roach/rl_birdview_agent.py create mode 100644 roach/utils/config_utils.py create mode 100644 roach/utils/expert_noiser.py create mode 100644 roach/utils/rl_birdview_wrapper.py create mode 100644 roach/utils/traffic_light.py create mode 100644 roach/utils/transforms.py create mode 100644 roach/utils/wandb_callback.py diff --git a/roach/config/config_agent.yaml b/roach/config/config_agent.yaml new file mode 100644 index 0000000..dc49aaf --- /dev/null +++ b/roach/config/config_agent.yaml @@ -0,0 +1,64 @@ +entry_point: roach.rl_birdview_agent:RlBirdviewAgent +wb_run_path: null +wb_ckpt_step: null +env_wrapper: + entry_point: roach.utils.rl_birdview_wrapper:RlBirdviewWrapper + kwargs: + input_states: + - control + - vel_xy + acc_as_action: true +policy: + entry_point: roach.models.ppo_policy:PpoPolicy + kwargs: + policy_head_arch: + - 256 + - 256 + value_head_arch: + - 256 + - 256 + features_extractor_entry_point: roach.models.torch_layers:XtMaCNN + features_extractor_kwargs: + states_neurons: + - 256 + - 256 + distribution_entry_point: roach.models.distributions:BetaDistribution + distribution_kwargs: + dist_init: null +training: + entry_point: roach.models.ppo:PPO + kwargs: + learning_rate: 1.0e-05 + n_steps_total: 12288 + batch_size: 256 + n_epochs: 20 + gamma: 0.99 + gae_lambda: 0.9 + clip_range: 0.2 + clip_range_vf: null + ent_coef: 0.01 + explore_coef: 0.05 + vf_coef: 0.5 + max_grad_norm: 0.5 + target_kl: 0.01 + update_adv: false + lr_schedule_step: 8 +obs_configs: + birdview: + module: birdview.chauffeurnet + width_in_pixels: 192 + pixels_ev_to_bottom: 0 + pixels_per_meter: 5.0 + history_idx: + - -1 + - -1 + - -1 + - -1 + scale_bbox: true + scale_mask_col: 1.0 + speed: + module: actor_state.speed + control: + module: actor_state.control + velocity: + module: actor_state.velocity diff --git a/roach/config/config_agent_original.yaml b/roach/config/config_agent_original.yaml new file mode 100644 index 0000000..ef831ef --- /dev/null +++ b/roach/config/config_agent_original.yaml @@ -0,0 +1,64 @@ +entry_point: roach.rl_birdview_agent:RlBirdviewAgent +wb_run_path: null +wb_ckpt_step: null +env_wrapper: + entry_point: roach.utils.rl_birdview_wrapper:RlBirdviewWrapper + kwargs: + input_states: + - control + - vel_xy + acc_as_action: true +policy: + entry_point: roach.models.ppo_policy:PpoPolicy + kwargs: + policy_head_arch: + - 256 + - 256 + value_head_arch: + - 256 + - 256 + features_extractor_entry_point: roach.models.torch_layers:XtMaCNN + features_extractor_kwargs: + states_neurons: + - 256 + - 256 + distribution_entry_point: roach.models.distributions:BetaDistribution + distribution_kwargs: + dist_init: null +training: + entry_point: roach.models.ppo:PPO + kwargs: + learning_rate: 1.0e-05 + n_steps_total: 12288 + batch_size: 256 + n_epochs: 20 + gamma: 0.99 + gae_lambda: 0.9 + clip_range: 0.2 + clip_range_vf: null + ent_coef: 0.01 + explore_coef: 0.05 + vf_coef: 0.5 + max_grad_norm: 0.5 + target_kl: 0.01 + update_adv: false + lr_schedule_step: 8 +obs_configs: + birdview: + module: birdview.chauffeurnet + width_in_pixels: 192 + pixels_ev_to_bottom: 40 + pixels_per_meter: 5.0 + history_idx: + - -16 + - -11 + - -6 + - -1 + scale_bbox: true + scale_mask_col: 1.0 + speed: + module: actor_state.speed + control: + module: actor_state.control + velocity: + module: actor_state.velocity diff --git a/roach/criteria/blocked.py b/roach/criteria/blocked.py new file mode 100644 index 0000000..334e581 --- /dev/null +++ b/roach/criteria/blocked.py @@ -0,0 +1,30 @@ +import numpy as np + + +class Blocked(): + + def __init__(self, speed_threshold=0.1, below_threshold_max_time=90.0): + self._speed_threshold = speed_threshold + self._below_threshold_max_time = below_threshold_max_time + self._time_last_valid_state = None + + def tick(self, vehicle, timestamp): + info = None + linear_speed = self._calculate_speed(vehicle.get_velocity()) + + if linear_speed < self._speed_threshold and self._time_last_valid_state: + if (timestamp['relative_simulation_time'] - self._time_last_valid_state) > self._below_threshold_max_time: + # The actor has been "blocked" for too long + ev_loc = vehicle.get_location() + info = { + 'step': timestamp['step'], + 'simulation_time': timestamp['relative_simulation_time'], + 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z] + } + else: + self._time_last_valid_state = timestamp['relative_simulation_time'] + return info + + @staticmethod + def _calculate_speed(carla_velocity): + return np.linalg.norm([carla_velocity.x, carla_velocity.y]) diff --git a/roach/criteria/collision.py b/roach/criteria/collision.py new file mode 100644 index 0000000..f2f41e4 --- /dev/null +++ b/roach/criteria/collision.py @@ -0,0 +1,117 @@ +import carla +import weakref +import numpy as np + + +class Collision(): + def __init__(self, vehicle, carla_world, intensity_threshold=0.0, + min_area_of_collision=3, max_area_of_collision=5, max_id_time=5): + blueprint = carla_world.get_blueprint_library().find('sensor.other.collision') + self._collision_sensor = carla_world.spawn_actor(blueprint, carla.Transform(), attach_to=vehicle) + self._collision_sensor.listen(lambda event: self._on_collision(weakref.ref(self), event)) + self._collision_info = None + + self.registered_collisions = [] + self.last_id = None + self.collision_time = None + + # If closer than this distance, the collision is ignored + self._min_area_of_collision = min_area_of_collision + # If further than this distance, the area is forgotten + self._max_area_of_collision = max_area_of_collision + # Amount of time the last collision if is remembered + self._max_id_time = max_id_time + # intensity_threshold, LBC uses 400, leaderboard does not use it (set to 0) + self._intensity_threshold = intensity_threshold + + def tick(self, vehicle, timestamp): + ev_loc = vehicle.get_location() + new_registered_collisions = [] + # Loops through all the previous registered collisions + for collision_location in self.registered_collisions: + distance = ev_loc.distance(collision_location) + # If far away from a previous collision, forget it + if distance <= self._max_area_of_collision: + new_registered_collisions.append(collision_location) + + self.registered_collisions = new_registered_collisions + + if self.last_id and timestamp['relative_simulation_time'] - self.collision_time > self._max_id_time: + self.last_id = None + + info = self._collision_info + self._collision_info = None + if info is not None: + info['step'] -= timestamp['start_frame'] + info['simulation_time'] -= timestamp['start_simulation_time'] + return info + + @staticmethod + def _on_collision(weakself, event): + self = weakself() + if not self: + return + # Ignore the current one if it's' the same id as before + if self.last_id == event.other_actor.id: + return + # Ignore if it's too close to a previous collision (avoid micro collisions) + ev_loc = event.actor.get_transform().location + for collision_location in self.registered_collisions: + if ev_loc.distance(collision_location) <= self._min_area_of_collision: + return + # Ignore if its intensity is smaller than self._intensity_threshold + impulse = event.normal_impulse + intensity = np.linalg.norm([impulse.x, impulse.y, impulse.z]) + if intensity < self._intensity_threshold: + return + + # collision_type + if ('static' in event.other_actor.type_id or 'traffic' in event.other_actor.type_id) \ + and 'sidewalk' not in event.other_actor.type_id: + collision_type = 0 # TrafficEventType.COLLISION_STATIC + elif 'vehicle' in event.other_actor.type_id: + collision_type = 1 # TrafficEventType.COLLISION_VEHICLE + elif 'walker' in event.other_actor.type_id: + collision_type = 2 # TrafficEventType.COLLISION_PEDESTRIAN + else: + collision_type = -1 + + # write to info, all quantities in in world coordinate + event_loc = event.transform.location + event_rot = event.transform.rotation + oa_loc = event.other_actor.get_transform().location + oa_rot = event.other_actor.get_transform().rotation + oa_vel = event.other_actor.get_velocity() + ev_rot = event.actor.get_transform().rotation + ev_vel = event.actor.get_velocity() + + self._collision_info = { + 'step': event.frame, + 'simulation_time': event.timestamp, + 'collision_type': collision_type, + 'other_actor_id': event.other_actor.id, + 'other_actor_type_id': event.other_actor.type_id, + 'intensity': intensity, + 'normal_impulse': [impulse.x, impulse.y, impulse.z], + 'event_loc': [event_loc.x, event_loc.y, event_loc.z], + 'event_rot': [event_rot.roll, event_rot.pitch, event_rot.yaw], + 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z], + 'ev_rot': [ev_rot.roll, ev_rot.pitch, ev_rot.yaw], + 'ev_vel': [ev_vel.x, ev_vel.y, ev_vel.z], + 'oa_loc': [oa_loc.x, oa_loc.y, oa_loc.z], + 'oa_rot': [oa_rot.roll, oa_rot.pitch, oa_rot.yaw], + 'oa_vel': [oa_vel.x, oa_vel.y, oa_vel.z] + } + + self.collision_time = event.timestamp + + self.registered_collisions.append(ev_loc) + + # Number 0: static objects -> ignore it + if event.other_actor.id != 0: + self.last_id = event.other_actor.id + + def clean(self): + self._collision_sensor.stop() + self._collision_sensor.destroy() + self._collision_sensor = None diff --git a/roach/criteria/encounter_light.py b/roach/criteria/encounter_light.py new file mode 100644 index 0000000..e5ab4ac --- /dev/null +++ b/roach/criteria/encounter_light.py @@ -0,0 +1,26 @@ +from carla_gym.utils.traffic_light import TrafficLightHandler + + +class EncounterLight(): + + def __init__(self, dist_threshold=7.5): + self._last_light_id = None + self._dist_threshold = dist_threshold + + def tick(self, vehicle, timestamp): + info = None + + light_state, light_loc, light_id = TrafficLightHandler.get_light_state( + vehicle, dist_threshold=self._dist_threshold) + + if light_id is not None: + if light_id != self._last_light_id: + self._last_light_id = light_id + info = { + 'step': timestamp['step'], + 'simulation_time': timestamp['relative_simulation_time'], + 'id': light_id, + 'tl_loc': light_loc.tolist() + } + + return info diff --git a/roach/criteria/outside_route_lane.py b/roach/criteria/outside_route_lane.py new file mode 100644 index 0000000..117aff9 --- /dev/null +++ b/roach/criteria/outside_route_lane.py @@ -0,0 +1,99 @@ +import carla +from carla_gym.utils.transforms import cast_angle + + +class OutsideRouteLane(): + + def __init__(self, carla_map, vehicle_loc, + allowed_out_distance=1.3, max_allowed_vehicle_angle=120.0, max_allowed_waypint_angle=150.0): + self._map = carla_map + self._pre_ego_waypoint = self._map.get_waypoint(vehicle_loc) + + self._allowed_out_distance = allowed_out_distance + self._max_allowed_vehicle_angle = max_allowed_vehicle_angle + self._max_allowed_waypint_angle = max_allowed_waypint_angle + + self._outside_lane_active = False + self._wrong_lane_active = False + self._last_road_id = None + self._last_lane_id = None + + def tick(self, vehicle, timestamp, distance_traveled): + ev_loc = vehicle.get_location() + ev_yaw = vehicle.get_transform().rotation.yaw + self._is_outside_driving_lanes(ev_loc) + self._is_at_wrong_lane(ev_loc, ev_yaw) + + info = None + if self._outside_lane_active or self._wrong_lane_active: + info = { + 'step': timestamp['step'], + 'simulation_time': timestamp['relative_simulation_time'], + 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z], + 'distance_traveled': distance_traveled, + 'outside_lane': self._outside_lane_active, + 'wrong_lane': self._wrong_lane_active + } + return info + + def _is_outside_driving_lanes(self, location): + """ + Detects if the ego_vehicle is outside driving/parking lanes + """ + + current_driving_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True) + current_parking_wp = self._map.get_waypoint(location, lane_type=carla.LaneType.Parking, project_to_road=True) + + driving_distance = location.distance(current_driving_wp.transform.location) + if current_parking_wp is not None: # Some towns have no parking + parking_distance = location.distance(current_parking_wp.transform.location) + else: + parking_distance = float('inf') + + if driving_distance >= parking_distance: + distance = parking_distance + lane_width = current_parking_wp.lane_width + else: + distance = driving_distance + lane_width = current_driving_wp.lane_width + + self._outside_lane_active = distance > (lane_width / 2 + self._allowed_out_distance) + + def _is_at_wrong_lane(self, location, yaw): + """ + Detects if the ego_vehicle has invaded a wrong driving lane + """ + + current_waypoint = self._map.get_waypoint(location, lane_type=carla.LaneType.Driving, project_to_road=True) + current_lane_id = current_waypoint.lane_id + current_road_id = current_waypoint.road_id + + # Lanes and roads are too chaotic at junctions + if current_waypoint.is_junction: + self._wrong_lane_active = False + elif self._last_road_id != current_road_id or self._last_lane_id != current_lane_id: + + # Route direction can be considered continuous, except after exiting a junction. + if self._pre_ego_waypoint.is_junction: + # cast angle to [-180, +180) + vehicle_lane_angle = cast_angle( + current_waypoint.transform.rotation.yaw - yaw) + + self._wrong_lane_active = abs(vehicle_lane_angle) > self._max_allowed_vehicle_angle + + else: + # Check for a big gap in waypoint directions. + waypoint_angle = cast_angle( + current_waypoint.transform.rotation.yaw - self._pre_ego_waypoint.transform.rotation.yaw) + + if abs(waypoint_angle) >= self._max_allowed_waypint_angle: + # Is the ego vehicle going back to the lane, or going out? Take the opposite + self._wrong_lane_active = not bool(self._wrong_lane_active) + else: + # Changing to a lane with the same direction + self._wrong_lane_active = False + + # Remember the last state + self._last_lane_id = current_lane_id + self._last_road_id = current_road_id + self._pre_ego_waypoint = current_waypoint diff --git a/roach/criteria/route_deviation.py b/roach/criteria/route_deviation.py new file mode 100644 index 0000000..1990db7 --- /dev/null +++ b/roach/criteria/route_deviation.py @@ -0,0 +1,34 @@ +class RouteDeviation(): + + def __init__(self, offroad_min=15, offroad_max=30, max_route_percentage=0.3): + self._offroad_min = offroad_min + self._offroad_max = offroad_max + self._max_route_percentage = max_route_percentage + self._out_route_distance = 0.0 + + def tick(self, vehicle, timestamp, ref_waypoint, distance_traveled, route_length): + ev_loc = vehicle.get_location() + + distance = ev_loc.distance(ref_waypoint.transform.location) + + # fail if off_route is True + off_route_max = distance > self._offroad_max + + # fail if off_safe_route more than 30% of total route length + off_route_min = False + if distance > self._offroad_min: + self._out_route_distance += distance_traveled + out_route_percentage = self._out_route_distance / route_length + if out_route_percentage > self._max_route_percentage: + off_route_min = True + + info = None + if off_route_max or off_route_min: + info = { + 'step': timestamp['step'], + 'simulation_time': timestamp['relative_simulation_time'], + 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z], + 'off_route_max': off_route_max, + 'off_route_min': off_route_min + } + return info diff --git a/roach/criteria/run_red_light.py b/roach/criteria/run_red_light.py new file mode 100644 index 0000000..908cba5 --- /dev/null +++ b/roach/criteria/run_red_light.py @@ -0,0 +1,64 @@ +import carla +import shapely.geometry +from carla_gym.utils.traffic_light import TrafficLightHandler + + +class RunRedLight(): + + def __init__(self, carla_map, distance_light=30): + self._map = carla_map + self._last_red_light_id = None + self._distance_light = distance_light + + def tick(self, vehicle, timestamp): + ev_tra = vehicle.get_transform() + ev_loc = ev_tra.location + ev_dir = ev_tra.get_forward_vector() + ev_extent = vehicle.bounding_box.extent.x + + tail_close_pt = ev_tra.transform(carla.Location(x=-0.8 * ev_extent)) + tail_far_pt = ev_tra.transform(carla.Location(x=-ev_extent - 1.0)) + tail_wp = self._map.get_waypoint(tail_far_pt) + + info = None + for idx_tl in range(TrafficLightHandler.num_tl): + traffic_light = TrafficLightHandler.list_tl_actor[idx_tl] + tl_tv_loc = TrafficLightHandler.list_tv_loc[idx_tl] + if tl_tv_loc.distance(ev_loc) > self._distance_light: + continue + if traffic_light.state != carla.TrafficLightState.Red: + continue + if self._last_red_light_id and self._last_red_light_id == traffic_light.id: + continue + + for idx_wp in range(len(TrafficLightHandler.list_stopline_wps[idx_tl])): + wp = TrafficLightHandler.list_stopline_wps[idx_tl][idx_wp] + wp_dir = wp.transform.get_forward_vector() + dot_ve_wp = ev_dir.x * wp_dir.x + ev_dir.y * wp_dir.y + ev_dir.z * wp_dir.z + + if tail_wp.road_id == wp.road_id and tail_wp.lane_id == wp.lane_id and dot_ve_wp > 0: + # This light is red and is affecting our lane + stop_left_loc, stop_right_loc = TrafficLightHandler.list_stopline_vtx[idx_tl][idx_wp] + # Is the vehicle traversing the stop line? + if self._is_vehicle_crossing_line((tail_close_pt, tail_far_pt), (stop_left_loc, stop_right_loc)): + tl_loc = traffic_light.get_location() + # loc_in_ev = trans_utils.loc_global_to_ref(tl_loc, ev_tra) + self._last_red_light_id = traffic_light.id + info = { + 'step': timestamp['step'], + 'simulation_time': timestamp['relative_simulation_time'], + 'id': traffic_light.id, + 'tl_loc': [tl_loc.x, tl_loc.y, tl_loc.z], + 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z] + } + return info + + @staticmethod + def _is_vehicle_crossing_line(seg1, seg2): + """ + check if vehicle crosses a line segment + """ + line1 = shapely.geometry.LineString([(seg1[0].x, seg1[0].y), (seg1[1].x, seg1[1].y)]) + line2 = shapely.geometry.LineString([(seg2[0].x, seg2[0].y), (seg2[1].x, seg2[1].y)]) + inter = line1.intersection(line2) + return not inter.is_empty diff --git a/roach/criteria/run_stop_sign.py b/roach/criteria/run_stop_sign.py new file mode 100644 index 0000000..b13b32a --- /dev/null +++ b/roach/criteria/run_stop_sign.py @@ -0,0 +1,157 @@ +import carla +import numpy as np + + +class RunStopSign(): + + def __init__(self, carla_world, proximity_threshold=50.0, speed_threshold=0.1, waypoint_step=1.0): + self._map = carla_world.get_map() + self._proximity_threshold = proximity_threshold + self._speed_threshold = speed_threshold + self._waypoint_step = waypoint_step + + all_actors = carla_world.get_actors() + self._list_stop_signs = [] + for _actor in all_actors: + if 'traffic.stop' in _actor.type_id: + self._list_stop_signs.append(_actor) + + self._target_stop_sign = None + self._stop_completed = False + self._affected_by_stop = False + + def tick(self, vehicle, timestamp): + info = None + ev_loc = vehicle.get_location() + ev_f_vec = vehicle.get_transform().get_forward_vector() + + if self._target_stop_sign is None: + self._target_stop_sign = self._scan_for_stop_sign(vehicle.get_transform()) + if self._target_stop_sign is not None: + stop_loc = self._target_stop_sign.get_location() + # info = { + # 'event': 'encounter', + # 'step': timestamp['step'], + # 'simulation_time': timestamp['relative_simulation_time'], + # 'id': self._target_stop_sign.id, + # 'stop_loc': [stop_loc.x, stop_loc.y, stop_loc.z], + # 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z] + # } + else: + # we were in the middle of dealing with a stop sign + if not self._stop_completed: + # did the ego-vehicle stop? + current_speed = self._calculate_speed(vehicle.get_velocity()) + if current_speed < self._speed_threshold: + self._stop_completed = True + + if not self._affected_by_stop: + stop_t = self._target_stop_sign.get_transform() + transformed_tv = stop_t.transform(self._target_stop_sign.trigger_volume.location) + stop_extent = self._target_stop_sign.trigger_volume.extent + if self.point_inside_boundingbox(ev_loc, transformed_tv, stop_extent): + self._affected_by_stop = True + + if not self.is_affected_by_stop(ev_loc, self._target_stop_sign): + # is the vehicle out of the influence of this stop sign now? + if not self._stop_completed and self._affected_by_stop: + # did we stop? + stop_loc = self._target_stop_sign.get_transform().location + # info = { + # 'event': 'run', + # 'step': timestamp['step'], + # 'simulation_time': timestamp['relative_simulation_time'], + # 'id': self._target_stop_sign.id, + # 'stop_loc': [stop_loc.x, stop_loc.y, stop_loc.z], + # 'ev_loc': [ev_loc.x, ev_loc.y, ev_loc.z] + # } + # reset state + self._target_stop_sign = None + self._stop_completed = False + self._affected_by_stop = False + + # return info + + def _scan_for_stop_sign(self, vehicle_transform): + target_stop_sign = None + + ve_dir = vehicle_transform.get_forward_vector() + + wp = self._map.get_waypoint(vehicle_transform.location) + wp_dir = wp.transform.get_forward_vector() + + dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z + + if dot_ve_wp > 0: # Ignore all when going in a wrong lane + for stop_sign in self._list_stop_signs: + if self.is_affected_by_stop(vehicle_transform.location, stop_sign): + # this stop sign is affecting the vehicle + target_stop_sign = stop_sign + break + + return target_stop_sign + + def is_affected_by_stop(self, vehicle_loc, stop, multi_step=20): + """ + Check if the given actor is affected by the stop + """ + affected = False + # first we run a fast coarse test + stop_t = stop.get_transform() + stop_location = stop_t.location + if stop_location.distance(vehicle_loc) > self._proximity_threshold: + return affected + + transformed_tv = stop_t.transform(stop.trigger_volume.location) + + # slower and accurate test based on waypoint's horizon and geometric test + list_locations = [vehicle_loc] + waypoint = self._map.get_waypoint(vehicle_loc) + for _ in range(multi_step): + if waypoint: + next_wps = waypoint.next(self._waypoint_step) + if not next_wps: + break + waypoint = next_wps[0] + if not waypoint: + break + list_locations.append(waypoint.transform.location) + + for actor_location in list_locations: + if self.point_inside_boundingbox(actor_location, transformed_tv, stop.trigger_volume.extent): + affected = True + + return affected + + @staticmethod + def _calculate_speed(carla_velocity): + return np.linalg.norm([carla_velocity.x, carla_velocity.y]) + + @staticmethod + def point_inside_boundingbox(point, bb_center, bb_extent): + """ + X + :param point: + :param bb_center: + :param bb_extent: + :return: + """ + # bugfix slim bbox + bb_extent.x = max(bb_extent.x, bb_extent.y) + bb_extent.y = max(bb_extent.x, 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