def get_object_reward_params(self, racecar_name, model_point, car_pose):
"""Returns a dictionary with object-related reward function params."""
with self._lock_:
try:
object_locations = [[
pose.position.x, pose.position.y
] for name, pose in self.object_poses.items()
if racecar_name not in name]
object_poses = [
pose for name, pose in self.object_poses.items()
if racecar_name not in name
]
if not object_locations:
return {}
# Sort the object locations based on projected distance
num_objects = len(object_locations)
object_pdists = [
self.center_line.project(Point(p))
for p in object_locations
]
object_headings = [0.0] * num_objects
object_speeds = [0.0] * num_objects
if self._is_bot_car_:
for i, object_pose in enumerate(object_poses):
_, _, yaw = quaternion_to_euler(
x=object_pose.orientation.x,
y=object_pose.orientation.y,
z=object_pose.orientation.z,
w=object_pose.orientation.w,
)
object_headings[i] = yaw
object_speeds[i] = self._bot_car_speed_
# Find the prev/next objects
model_pdist = self.center_line.project(model_point)
object_order = np.argsort(object_pdists)
object_pdists_ordered = [
object_pdists[i] for i in object_order
]
prev_object_index, next_object_index = find_prev_next(
object_pdists_ordered, model_pdist)
prev_object_index = object_order[prev_object_index]
next_object_index = object_order[next_object_index]
# Figure out which one is the closest
object_points = [
Point([object_location[0], object_location[1]])
for object_location in object_locations
]
prev_object_point = object_points[prev_object_index]
next_object_point = object_points[next_object_index]
prev_object_dist = model_point.distance(prev_object_point)
next_object_dist = model_point.distance(next_object_point)
if prev_object_dist < next_object_dist:
closest_object_point = prev_object_point
else:
closest_object_point = next_object_point
# Figure out whether objects is left of center based on direction
objects_left_of_center = [
self._inner_poly_.contains(p) ^ (not self.is_ccw)
for p in object_points
]
# Get object distances to centerline
objects_distance_to_center = [
self.center_line.distance(p) for p in object_points
]
# Figure out if the next object is in the camera view
objects_in_camera = self.get_objects_in_camera_frustums(
agent_name=racecar_name, car_pose=car_pose)
is_next_object_in_camera = any(
object_in_camera_idx == next_object_index
for object_in_camera_idx, _ in objects_in_camera)
# Determine if they are in the same lane
return {
RewardParam.CLOSEST_OBJECTS.value[0]:
[prev_object_index, next_object_index],
RewardParam.OBJECT_LOCATIONS.value[0]:
object_locations,
RewardParam.OBJECTS_LEFT_OF_CENTER.value[0]:
objects_left_of_center,
RewardParam.OBJECT_SPEEDS.value[0]:
object_speeds,
RewardParam.OBJECT_HEADINGS.value[0]:
object_headings,
RewardParam.OBJECT_CENTER_DISTS.value[0]:
objects_distance_to_center,
RewardParam.OBJECT_CENTERLINE_PROJECTION_DISTANCES.value[0]:
object_pdists,
RewardParam.OBJECT_IN_CAMERA.value[0]:
is_next_object_in_camera,
}
except Exception as ex:
raise GenericRolloutException(
"Unable to get object reward params: {}".format(ex))
def find_prev_next_waypoints(self, distance, normalized=False):
ndist = distance if normalized else distance / self.line.length
return find_prev_next(self.ndists, ndist)
def get_object_reward_params(self, racecar_name, model_point, reverse_dir,
car_pose):
'''Returns a dictionary with object-related reward function params.'''
with self._lock_:
try:
object_locations = [[
pose.position.x, pose.position.y
] for name, pose in self.object_poses.items()
if racecar_name not in name]
object_poses = [
pose for name, pose in self.object_poses.items()
if racecar_name not in name
]
if not object_locations:
return {}
# Sort the object locations based on projected distance
object_pdists = [
self.center_line.project(Point(p))
for p in object_locations
]
object_order = np.argsort(object_pdists)
object_pdists = [object_pdists[i] for i in object_order]
object_locations = [object_locations[i] for i in object_order]
object_headings = []
object_speeds = []
object_headings = [0.0 for _ in object_order]
object_speeds = [0.0 for _ in object_order]
if self._is_bot_car_:
for i in object_order:
object_pose = object_poses[i]
_, _, yaw = quaternion_to_euler(
x=object_pose.orientation.x,
y=object_pose.orientation.y,
z=object_pose.orientation.z,
w=object_pose.orientation.w)
object_headings[i] = yaw
object_speeds[i] = self._bot_car_speed_
# Find the prev/next objects
model_pdist = self.center_line.project(model_point)
prev_object_index, next_object_index = find_prev_next(
object_pdists, model_pdist)
# Figure out which one is the closest
object_points = [
Point([object_location[0], object_location[1]])
for object_location in object_locations
]
prev_object_point = object_points[prev_object_index]
next_object_point = object_points[next_object_index]
prev_object_dist = model_point.distance(prev_object_point)
next_object_dist = model_point.distance(next_object_point)
if prev_object_dist < next_object_dist:
closest_object_point = prev_object_point
else:
closest_object_point = next_object_point
# Figure out whether objects is left of center based on direction
is_ccw = self._is_ccw_ ^ reverse_dir
objects_left_of_center = [self._inner_poly_.contains(p) ^ (not is_ccw) \
for p in object_points]
# Figure out which lane the model is in
model_nearest_pnts_dict = self.get_nearest_points(model_point)
model_nearest_dist_dict = self.get_nearest_dist(
model_nearest_pnts_dict, model_point)
model_is_inner = \
model_nearest_dist_dict[TrackNearDist.NEAR_DIST_IN.value] < \
model_nearest_dist_dict[TrackNearDist.NEAR_DIST_OUT.value]
# Figure out which lane the object is in
closest_object_nearest_pnts_dict = self.get_nearest_points(
closest_object_point)
closest_object_nearest_dist_dict = \
self.get_nearest_dist(closest_object_nearest_pnts_dict, closest_object_point)
closest_object_is_inner = \
closest_object_nearest_dist_dict[TrackNearDist.NEAR_DIST_IN.value] < \
closest_object_nearest_dist_dict[TrackNearDist.NEAR_DIST_OUT.value]
objects_in_camera = self.get_objects_in_camera_frustums(
agent_name=racecar_name,
car_pose=car_pose,
object_order=object_order)
is_next_object_in_camera = any(
object_in_camera_idx == next_object_index
for object_in_camera_idx, _ in objects_in_camera)
# Determine if they are in the same lane
return {
RewardParam.CLOSEST_OBJECTS.value[0]:
[prev_object_index, next_object_index],
RewardParam.OBJECT_LOCATIONS.value[0]:
object_locations,
RewardParam.OBJECTS_LEFT_OF_CENTER.value[0]:
objects_left_of_center,
RewardParam.OBJECT_SPEEDS.value[0]:
object_speeds,
RewardParam.OBJECT_HEADINGS.value[0]:
object_headings,
RewardParam.OBJECT_CENTERLINE_PROJECTION_DISTANCES.value[0]:
object_pdists,
RewardParam.OBJECT_IN_CAMERA.value[0]:
is_next_object_in_camera
}
except Exception as ex:
raise GenericRolloutException(
"Unable to get object reward params: {}".format(ex))
