def reset(self, ego_vehicle: carla.Vehicle):
if self._early_stop_monitor:
self._early_stop_monitor.close()
timeout_s = (FRAMES_BEFORE_MANUVEUR + FRAMES_AFTER_MANUVEUR) * DT
self._early_stop_monitor = EarlyStopMonitor(ego_vehicle, timeout_s=timeout_s)
self._route = random.choice(self._routes)
min_speed, max_speed = self._speed_range_mps
range_size = max_speed - min_speed
speed_mps = min_speed + np.random.random() * range_size
column_ahead_of_ego_m = np.random.randint(*self._env_vehicle_column_ahead_range_m)
cmds = self._setup_controllers(self._start_lane_waypoint.transform.location, speed_mps,
self._route, column_ahead_of_ego_m)
ego_speed_mps = min_speed + np.random.random() * range_size
t = Transform.from_carla_transform(self._start_lane_waypoint.transform)
velocity = (t.orientation * ego_speed_mps).to_vector3(0).as_carla_vector3d()
cmds.append(carla.command.ApplyTransform(ego_vehicle.id, transform=self._start_lane_waypoint.transform))
cmds.append(carla.command.ApplyVelocity(ego_vehicle.id, velocity))
if cmds:
self._client.apply_batch_sync(cmds, do_tick=False)
self._lane_alignment_monitor.reset()
self._progress_monitor = LaneChangeProgressMonitor(self._world_map,
start_lane_ids=self._start_lane_ids,
target_lane_ids=self._target_lane_ids,
lane_change_command=self._cmd_for_changing_lane)
def reset(self, ego_vehicle: carla.Vehicle):
if self._carla_sync:
self._carla_sync.close()
self._carla_sync = RealTrafficVehiclesInCarla(self._client,
self._world)
if self._early_stop_monitor:
self._early_stop_monitor.close()
session_names = self._dataset.session_names
if self._place_name:
session_names = [
n for n in session_names if self._place_name.lower() in n
]
epseed = os.environ.get("epseed")
if epseed:
epseed = int(epseed)
random.seed(epseed)
session_name = random.choice(session_names)
# Another random is used inside
ego_id, timestamp_start_s, timestamp_end_s = self._recording.reset(
session_name=session_name, seed=epseed)
self._sampled_dataset_excerpt_info = dict(
episode_seed=epseed,
session_name=session_name,
timestamp_start_s=timestamp_start_s,
timestamp_end_s=timestamp_end_s,
original_veh_id=ego_id,
)
env_vehicles = self._recording.step()
other_vehicles = [v for v in env_vehicles if v.id != ego_id]
self._carla_sync.step(other_vehicles)
opendd_ego_vehicle = self._recording._env_vehicles[ego_id]
opendd_ego_vehicle.set_end_of_trajectory_timestamp(timestamp_end_s)
self._chauffeur = Chauffeur(opendd_ego_vehicle,
self._recording.place.roads_utm)
ego_vehicle.set_transform(
opendd_ego_vehicle.transform_carla.as_carla_transform())
ego_vehicle.set_velocity(
opendd_ego_vehicle.velocity.as_carla_vector3d())
self._current_progress = 0
trajectory_carla = [
t.as_carla_transform() for t in opendd_ego_vehicle.trajectory_carla
]
self._trajectory = Trajectory(trajectory_carla=trajectory_carla)
timeout_s = (timestamp_end_s - timestamp_start_s) * 1.5
timeout_s = min(timeout_s,
self._recording._timestamps[-1] - timestamp_start_s)
self._early_stop_monitor = EarlyStopMonitor(
ego_vehicle,
timeout_s=timeout_s,
trajectory=self._trajectory,
max_trajectory_distance_m=MAX_DISTANCE_FROM_REF_TRAJECTORY_M)
def reset(self, vehicle: carla.Vehicle):
if self._ngsim_vehicles_in_carla:
self._ngsim_vehicles_in_carla.close()
self._ngsim_vehicles_in_carla = RealTrafficVehiclesInCarla(self._client, self._world)
if self._early_stop_monitor:
self._early_stop_monitor.close()
timeout_s = (FRAMES_BEFORE_MANUVEUR + FRAMES_AFTER_MANUVEUR) * DT
self._early_stop_monitor = EarlyStopMonitor(vehicle, timeout_s=timeout_s)
while True:
epseed = os.environ.get("epseed")
if epseed:
random.seed(int(epseed))
self._lane_change: LaneChangeInstant = random.choice(self._lane_change_instants)
self._sampled_dataset_excerpt_info = dict(
episode_seed=epseed,
file_suffix=self._lane_change.timeslot.file_suffix,
frame_start=self._lane_change.frame_start,
original_veh_id=self._lane_change.vehicle_id
)
frame_manuveur_start = max(self._lane_change.frame_start - FRAMES_BEFORE_MANUVEUR, 0)
self._ngsim_recording.reset(timeslot=self._lane_change.timeslot, frame=frame_manuveur_start - 1)
ngsim_vehicles = self._ngsim_recording.step()
agent_ngsim_vehicle = find_first_matching(ngsim_vehicles, lambda v: v.id == self._lane_change.vehicle_id)
t = agent_ngsim_vehicle.transform
self._start_lane_waypoint = self._world_map.get_waypoint(t.as_carla_transform().location)
self._target_lane_waypoint = {
ChauffeurCommand.CHANGE_LANE_LEFT: self._start_lane_waypoint.get_left_lane,
ChauffeurCommand.CHANGE_LANE_RIGHT: self._start_lane_waypoint.get_right_lane,
}[self._lane_change.chauffeur_command]()
if self._start_lane_waypoint and self._target_lane_waypoint:
self._start_lane_ids = get_lane_ids(self._start_lane_waypoint)
self._target_lane_ids = get_lane_ids(self._target_lane_waypoint)
assert not (set(self._start_lane_ids) & set(self._target_lane_ids)) # ensure disjoint sets of ids
break
self._lane_alignment_monitor.reset()
self._progress_monitor = LaneChangeProgressMonitor(self._world_map,
start_lane_ids=self._start_lane_ids,
target_lane_ids=self._target_lane_ids,
lane_change_command=self._lane_change.chauffeur_command)
vehicle.set_transform(t.as_carla_transform())
v = t.orientation * agent_ngsim_vehicle.speed * PIXELS_TO_METERS
vehicle.set_velocity(v.to_vector3(0).as_carla_vector3d()) # meters per second,
other_ngsim_vehicles = [v for v in ngsim_vehicles if v.id != self._lane_change.vehicle_id]
self._ngsim_vehicles_in_carla.step(other_ngsim_vehicles)