"""Actions for dynamic agents in CARLA scenarios."""
import math as _math
import carla as _carla
from scenic.domains.driving.actions import *
import scenic.simulators.carla.utils.utils as _utils
################################################
# Actions available to all carla.Actor objects #
################################################
SetLocationAction = SetPositionAction # TODO refactor
class SetAngularVelocityAction(Action):
def __init__(self, angularVel):
self.angularVel = angularVel
def applyTo(self, obj, sim):
xAngularVel = self.angularVel * _math.cos(obj.heading)
yAngularVel = self.angularVel * _math.sin(obj.heading)
newAngularVel = _utils.scalarToCarlaVector3D(xAngularVel, yAngularVel)
obj.carlaActor.set_angular_velocity(newAngularVel)
class SetTransformAction(Action): # TODO eliminate
def __init__(self, pos, heading):
self.pos = pos
self.heading = heading
def applyTo(self, obj, sim):
loc = _utils.scenicToCarlaLocation(self.pos, z=obj.elevation)
rot = _utils.scenicToCarlaRotation(self.heading)
transform = _carla.Transform(loc, rot)
obj.carlaActor.set_transform(transform)
#############################################
# Actions specific to carla.Vehicle objects #
#############################################
class _CarlaVehicle:
# Mixin identifying CARLA vehicles.
# Used to avoid importing the Vehicle class from the CARLA model, which is
# a Scenic module not importable from Python.
pass
class VehicleAction(Action):
def canBeTakenBy(self, agent):
return isinstance(agent, _CarlaVehicle)
class SetManualGearShiftAction(VehicleAction):
def __init__(self, manualGearShift):
if not isinstance(manualGearShift, bool):
raise RuntimeError("Manual gear shift must be a boolean.")
self.manualGearShift = manualGearShift
def applyTo(self, obj, sim):
vehicle = obj.carlaActor
ctrl = vehicle.get_control()
ctrl.manual_gear_shift = self.manualGearShift
vehicle.apply_control(ctrl)
class SetGearAction(VehicleAction):
def __init__(self, gear):
if not isinstance(gear, int):
raise RuntimeError("Gear must be an int.")
self.gear = gear
def applyTo(self, obj, sim):
vehicle = obj.carlaActor
ctrl = vehicle.get_control()
ctrl.gear = self.gear
vehicle.apply_control(ctrl)
class SetManualFirstGearShiftAction(VehicleAction): # TODO eliminate
def applyTo(self, obj, sim):
ctrl = _carla.VehicleControl(manual_gear_shift=True, gear=1)
obj.carlaActor.apply_control(ctrl)
[docs]class SetTrafficLightAction(VehicleAction):
"""Set the traffic light to desired color. It will only take
effect if the car is within a given distance of the traffic light.
Arguments:
color: the string red/yellow/green/off/unknown
distance: the maximum distance to search for traffic lights from the current position
"""
def __init__(self, color, distance=100, group=False):
self.color = _utils.scenicToCarlaTrafficLightStatus(color)
if color is None:
raise RuntimeError("Color must be red/yellow/green/off/unknown.")
self.distance = distance
def applyTo(self, obj, sim):
traffic_light = obj._getClosestTrafficLight(self.distance)
if traffic_light is not None:
traffic_light.set_state(self.color)
class SetAutopilotAction(VehicleAction):
def __init__(self, enabled):
if not isinstance(enabled, bool):
raise RuntimeError("Enabled must be a boolean.")
self.enabled = enabled
def applyTo(self, obj, sim):
vehicle = obj.carlaActor
vehicle.set_autopilot(self.enabled, sim.tm.get_port())
[docs]class SetVehicleLightStateAction(VehicleAction):
"""Set the vehicle lights' states.
Arguments:
vehicleLightState: Which lights are on.
"""
def __init__(self, vehicleLightState):
self.vehicleLightState = vehicleLightState
def applyTo(self, obj, sim):
obj.carlaActor.set_light_state(self.vehicleLightState)
#################################################
# Actions available to all carla.Walker objects #
#################################################
class _CarlaPedestrian:
# Mixin identifying CARLA pedestrians. (see _CarlaVehicle)
pass
class PedestrianAction(Action):
def canBeTakenBy(self, agent):
return isinstance(agent, _CarlaPedestrian)
class SetJumpAction(PedestrianAction):
def __init__(self, jump):
if not isinstance(jump, bool):
raise RuntimeError("Jump must be a boolean.")
self.jump = jump
def applyTo(self, obj, sim):
walker = obj.carlaActor
ctrl = walker.get_control()
ctrl.jump = self.jump
walker.apply_control(ctrl)
class SetWalkAction(PedestrianAction):
def __init__(self, enabled, maxSpeed=1.4):
if not isinstance(enabled, bool):
raise RuntimeError("Enabled must be a boolean.")
self.enabled = enabled
self.maxSpeed = maxSpeed
def applyTo(self, obj, sim):
controller = obj.carlaController
if self.enabled:
controller.start()
controller.go_to_location(sim.world.get_random_location_from_navigation())
controller.set_max_speed(self.maxSpeed)
else:
controller.stop()
class TrackWaypointsAction(Action):
def __init__(self, waypoints, cruising_speed=10):
self.waypoints = np.array(waypoints)
self.curr_index = 1
self.cruising_speed = cruising_speed
def canBeTakenBy(self, agent):
# return agent.lgsvlAgentType is lgsvl.AgentType.EGO
return True
def LQR(v_target, wheelbase, Q, R):
A = np.matrix([[0, v_target * (5.0 / 18.0)], [0, 0]])
B = np.matrix([[0], [(v_target / wheelbase) * (5.0 / 18.0)]])
V = np.matrix(linalg.solve_continuous_are(A, B, Q, R))
K = np.matrix(linalg.inv(R) * (B.T * V))
return K
def applyTo(self, obj, sim):
carlaObj = obj.carlaActor
transform = carlaObj.get_transform()
pos = transform.location
rot = transform.rotation
velocity = carlaObj.get_velocity()
th, x, y, v = (
rot.y / 180.0 * np.pi,
pos.x,
pos.z,
(velocity.x**2 + velocity.z**2) ** 0.5,
)
# print('state:', th, x, y, v)
PREDICTIVE_LENGTH = 3
MIN_SPEED = 1
WHEEL_BASE = 3
v = max(MIN_SPEED, v)
x = x + PREDICTIVE_LENGTH * np.cos(-th + np.pi / 2)
y = y + PREDICTIVE_LENGTH * np.sin(-th + np.pi / 2)
# print('car front:', x, y)
dists = np.linalg.norm(self.waypoints - np.array([x, y]), axis=1)
dist_pos = np.argpartition(dists, 1)
index = dist_pos[0]
if index > self.curr_index and index < len(self.waypoints) - 1:
self.curr_index = index
p1, p2, p3 = (
self.waypoints[self.curr_index - 1],
self.waypoints[self.curr_index],
self.waypoints[self.curr_index + 1],
)
p1_a = np.linalg.norm(p1 - np.array([x, y]))
p3_a = np.linalg.norm(p3 - np.array([x, y]))
p1_p2 = np.linalg.norm(p1 - p2)
p3_p2 = np.linalg.norm(p3 - p2)
if p1_a - p1_p2 > p3_a - p3_p2:
p1 = p2
p2 = p3
# print('points:',p1, p2)
x1, y1, x2, y2 = p1[0], p1[1], p2[0], p2[1]
th_n = -math.atan2(y2 - y1, x2 - x1) + np.pi / 2
d_th = (th - th_n + 3 * np.pi) % (2 * np.pi) - np.pi
d_x = (x2 - x1) * y - (y2 - y1) * x + y2 * x1 - y1 * x2
d_x /= np.linalg.norm(np.array([x1, y1]) - np.array([x2, y2]))
# print('d_th, d_x:',d_th, d_x)
K = TrackWaypoints.LQR(
v, WHEEL_BASE, np.array([[1, 0], [0, 3]]), np.array([[10]])
)
u = -K * np.matrix([[-d_x], [d_th]])
u = np.double(u)
u_steering = min(max(u, -1), 1)
K = 1
u = -K * (v - self.cruising_speed)
u_thrust = min(max(u, -1), 1)
# print('u:', u_thrust, u_steering)
ctrl = carlaObj.get_control()
ctrl.steering = u_steering
if u_thrust > 0:
ctrl.throttle = u_thrust
elif u_thrust < 0.1:
ctrl.braking = -u_thrust
carlaObj.apply_control(ctrl)