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
