class ImageStateObservation(ObservationBase):
"""
Use ego state info, navigation info and front cam image/top down image as input
The shape needs special handling
"""
IMAGE = "image"
STATE = "state"
def __init__(self, vehicle_config):
config = vehicle_config
super(ImageStateObservation, self).__init__(config)
self.img_obs = ImageObservation(config, config["image_source"], config["rgb_clip"])
self.state_obs = StateObservation(config)
@property
def observation_space(self):
# TODO it should be specified by different vehicle
return gym.spaces.Dict(
{
self.IMAGE: self.img_obs.observation_space,
self.STATE: self.state_obs.observation_space
}
)
def observe(self, vehicle: BaseVehicle):
return {self.IMAGE: self.img_obs.observe(vehicle), self.STATE: self.state_obs.observe(vehicle)}
class LidarStateObservationMARound(ObservationType):
def __init__(self, vehicle_config):
self.state_obs = StateObservation(vehicle_config)
super(LidarStateObservationMARound, self).__init__(vehicle_config)
self.state_length = list(self.state_obs.observation_space.shape)[0]
@property
def observation_space(self):
shape = list(self.state_obs.observation_space.shape)
if self.config["lidar"]["num_lasers"] > 0 and self.config["lidar"][
"distance"] > 0:
# Number of lidar rays and distance should be positive!
shape[0] += self.config["lidar"]["num_lasers"] + self.config[
"lidar"]["num_others"] * self.state_length
return gym.spaces.Box(-0.0, 1.0, shape=tuple(shape), dtype=np.float32)
def observe(self, vehicle):
num_others = self.config["lidar"]["num_others"]
state = self.state_observe(vehicle)
other_v_info = []
if vehicle.lidar is not None:
if self.config["lidar"]["num_others"] > 0:
surrounding_vehicles = list(
vehicle.lidar.get_surrounding_vehicles())
surrounding_vehicles.sort(
key=lambda v: norm(vehicle.position[0] - v.position[0],
vehicle.position[1] - v.position[1]))
surrounding_vehicles += [None] * num_others
for tmp_v in surrounding_vehicles[:num_others]:
if tmp_v is not None:
tmp_v = tmp_v.get_vehicle()
other_v_info += self.state_observe(tmp_v).tolist()
else:
other_v_info += [0] * self.state_length
other_v_info += self._add_noise_to_cloud_points(
vehicle.lidar.get_cloud_points(),
gaussian_noise=self.config["lidar"]["gaussian_noise"],
dropout_prob=self.config["lidar"]["dropout_prob"])
return np.concatenate((state, np.asarray(other_v_info)))
def state_observe(self, vehicle):
return self.state_obs.observe(vehicle)
def _add_noise_to_cloud_points(self, points, gaussian_noise, dropout_prob):
if gaussian_noise > 0.0:
points = np.asarray(points)
points = np.clip(
points + np.random.normal(
loc=0.0, scale=gaussian_noise, size=points.shape), 0.0,
1.0)
if dropout_prob > 0.0:
assert dropout_prob <= 1.0
points = np.asarray(points)
points[np.random.uniform(0, 1, size=points.shape) <
dropout_prob] = 0.0
return list(points)
def __init__(self, vehicle_config):
config = vehicle_config
super(ImageStateObservation, self).__init__(config)
self.img_obs = ImageObservation(config, config["image_source"], config["rgb_clip"])
self.state_obs = StateObservation(config)
def __init__(self, vehicle_config):
self.state_obs = StateObservation(vehicle_config)
super(LidarStateObservationMARound, self).__init__(vehicle_config)
self.state_length = list(self.state_obs.observation_space.shape)[0]
def __init__(self, vehicle_config):
self.state_obs = StateObservation(vehicle_config)
super(LidarStateObservationMARound, self).__init__(vehicle_config)
self.state_length = list(self.state_obs.observation_space.shape)[0]
self.cloud_points = None
self.detected_objects = None