Creates a Vehicle instance based on the last seen vehicle information. Args: vehicle_info (Dict): Information about the vehicle.

Assume constant speed and orientation, find center of long box generated by trajectory Args: state (EvaluationState): current stat

Loads the configuration file from the given path. Args: config_file_path (str): Path to the configuration file. Returns:

normalize an angle such that the angle lies in [0, pi) Args: yaw (Union[float, np.ndarray]): angle in radians Returns: Union

return the last state of the trajectory: x, y, yaw, vel, acc, laneID, lanPos

the time-to-collision (ttc) to the agent which is in front of and slower than ego vehicle Returns: float: ttc

the instantaneous energy consumption of ego car, measured by (v * a) ** 2 Returns: float: result

the jerk of the ego car Returns: float: jerk

the offset to the center line of current lane, measured by d**2 Returns: float: the square of lateral offset with respect to cent

The orientation offset with respect to center line of lane Returns: float: orientation offset

compute time-to-collision information from a given scenario database Args: database_name (str): a SQL database contains scenario informat

compute time-to-collision for all states of ego Args: ego_vehicle (EvaluationVehicle): ego other_vehicles (List[EvaluationVehicl

Concatenate a list of trajectories into one trajectory.

extract static obstacles for planning Returns: List[StaticObstacle]: static obstacles in frame

extract vehicles information from scenario information and vehicle model information only ego vehicle and vehicles in AoI will be considered.