def acceleration(self,
ego_vehicle: ControlledVehicle,
front_vehicle: Vehicle = None,
rear_vehicle: Vehicle = None) -> float:
"""
Compute an acceleration command with the Intelligent Driver Model.
The acceleration is chosen so as to:
- reach a target speed;
- maintain a minimum safety distance (and safety time) w.r.t the front vehicle.
:param ego_vehicle: the vehicle whose desired acceleration is to be computed. It does not have to be an
IDM vehicle, which is why this method is a class method. This allows an IDM vehicle to
reason about other vehicles behaviors even though they may not IDMs.
:param front_vehicle: the vehicle preceding the ego-vehicle
:param rear_vehicle: the vehicle following the ego-vehicle
:return: the acceleration command for the ego-vehicle [m/s2]
"""
if not ego_vehicle or not isinstance(ego_vehicle, Vehicle):
return 0
ego_target_speed = utils.not_zero(
getattr(ego_vehicle, "target_speed", 0))
acceleration = self.COMFORT_ACC_MAX * (1 - np.power(
max(ego_vehicle.speed, 0) / ego_target_speed, self.DELTA))
if front_vehicle:
d = ego_vehicle.lane_distance_to(front_vehicle)
acceleration -= self.COMFORT_ACC_MAX * \
np.power(self.desired_gap(ego_vehicle, front_vehicle) / utils.not_zero(d), 2)
return acceleration
def acceleration_features(self, ego_vehicle: ControlledVehicle,
front_vehicle: Vehicle = None,
rear_vehicle: Vehicle = None) -> np.ndarray:
vt, dv, dp = 0, 0, 0
if ego_vehicle:
vt = ego_vehicle.target_speed - ego_vehicle.speed
d_safe = self.DISTANCE_WANTED + np.maximum(ego_vehicle.speed, 0) * self.TIME_WANTED
if front_vehicle:
d = ego_vehicle.lane_distance_to(front_vehicle)
dv = min(front_vehicle.speed - ego_vehicle.speed, 0)
dp = min(d - d_safe, 0)
return np.array([vt, dv, dp])