def buffer_cost(traj, target_vehicle, delta, T, predictions): # print("buffer_cost: ", T) """ Penalizes getting close to other vehicles. """ nearest = nearest_approach_to_any_vehicle(traj, predictions) return logistic(2 * VEHICLE_RADIUS / nearest)
def collision_cost(traj, target_vehicle, delta, T, predictions): """ Binary cost function which penalizes collisions. """ nearest = nearest_approach_to_any_vehicle(traj, predictions) if nearest < 2 * VEHICLE_RADIUS: return 1.0 else: return 0.0
def buffer_cost(traj, target_vehicle, delta, T, predictions): """ Penalizes getting close to other vehicles. """ nearest = nearest_approach_to_any_vehicle(traj, predictions) factor = 2 * VEHICLE_RADIUS / nearest return logistic(factor)
def buffer_cost(traj, target_vehicle, delta, T, predictions): """ Penalizes getting close to other vehicles. Args: trajectory (list): list of tuple([s_coefficients, d_coefficients, t]) target_vehicle (int): the label of the target vehicle delta (list): [s, s_dot, s_double_dot, d, d_dot, d_double_dot] goal_t (float): the require time to get to the goal predictions (dict): dictionary of {v_id : vehicle } """ nearest = nearest_approach_to_any_vehicle(traj, predictions) return logistic(2 * VEHICLE_RADIUS / nearest)
def collision_cost(traj, target_vehicle, delta, T, predictions): """ Binary cost function which penalizes collisions. Args: trajectory (list): list of tuple([s_coefficients, d_coefficients, t]) target_vehicle (int): the label of the target vehicle delta (list): [s, s_dot, s_double_dot, d, d_dot, d_double_dot] goal_t (float): the require time to get to the goal predictions (dict): dictionary of {v_id : vehicle } """ nearest = nearest_approach_to_any_vehicle(traj, predictions) if nearest < 2 * VEHICLE_RADIUS: return 1.0 else: return 0.0