from typing import List, Tuple, Union, Optional
import numpy as np
import copy
from highway_env import utils
from highway_env.road.road import Road, LaneIndex, Route
from highway_env.utils import Vector
from highway_env.vehicle.kinematics import Vehicle
[docs]class ControlledVehicle(Vehicle):
"""
A vehicle piloted by two low-level controller, allowing high-level actions such as cruise control and lane changes.
- The longitudinal controller is a speed controller;
- The lateral controller is a heading controller cascaded with a lateral position controller.
"""
target_speed: float
""" Desired velocity."""
"""Characteristic time"""
TAU_ACC = 0.6 # [s]
TAU_HEADING = 0.2 # [s]
TAU_LATERAL = 0.6 # [s]
TAU_PURSUIT = 0.5 * TAU_HEADING # [s]
KP_A = 1 / TAU_ACC
KP_HEADING = 1 / TAU_HEADING
KP_LATERAL = 1 / TAU_LATERAL # [1/s]
MAX_STEERING_ANGLE = np.pi / 3 # [rad]
DELTA_SPEED = 5 # [m/s]
def __init__(self,
road: Road,
position: Vector,
heading: float = 0,
speed: float = 0,
target_lane_index: LaneIndex = None,
target_speed: float = None,
route: Route = None):
super().__init__(road, position, heading, speed)
self.target_lane_index = target_lane_index or self.lane_index
self.target_speed = target_speed or self.speed
self.route = route
[docs] @classmethod
def create_from(cls, vehicle: "ControlledVehicle") -> "ControlledVehicle":
"""
Create a new vehicle from an existing one.
The vehicle dynamics and target dynamics are copied, other properties are default.
:param vehicle: a vehicle
:return: a new vehicle at the same dynamical state
"""
v = cls(vehicle.road, vehicle.position, heading=vehicle.heading, speed=vehicle.speed,
target_lane_index=vehicle.target_lane_index, target_speed=vehicle.target_speed,
route=vehicle.route)
return v
[docs] def plan_route_to(self, destination: str) -> "ControlledVehicle":
"""
Plan a route to a destination in the road network
:param destination: a node in the road network
"""
try:
path = self.road.network.shortest_path(self.lane_index[1], destination)
except KeyError:
path = []
if path:
self.route = [self.lane_index] + [(path[i], path[i + 1], None) for i in range(len(path) - 1)]
else:
self.route = [self.lane_index]
return self
[docs] def act(self, action: Union[dict, str] = None) -> None:
"""
Perform a high-level action to change the desired lane or speed.
- If a high-level action is provided, update the target speed and lane;
- then, perform longitudinal and lateral control.
:param action: a high-level action
"""
self.follow_road()
if action == "FASTER":
self.target_speed += self.DELTA_SPEED
elif action == "SLOWER":
self.target_speed -= self.DELTA_SPEED
elif action == "LANE_RIGHT":
_from, _to, _id = self.target_lane_index
target_lane_index = _from, _to, np.clip(_id + 1, 0, len(self.road.network.graph[_from][_to]) - 1)
if self.road.network.get_lane(target_lane_index).is_reachable_from(self.position):
self.target_lane_index = target_lane_index
elif action == "LANE_LEFT":
_from, _to, _id = self.target_lane_index
target_lane_index = _from, _to, np.clip(_id - 1, 0, len(self.road.network.graph[_from][_to]) - 1)
if self.road.network.get_lane(target_lane_index).is_reachable_from(self.position):
self.target_lane_index = target_lane_index
action = {"steering": self.steering_control(self.target_lane_index),
"acceleration": self.speed_control(self.target_speed)}
action['steering'] = np.clip(action['steering'], -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE)
super().act(action)
[docs] def follow_road(self) -> None:
"""At the end of a lane, automatically switch to a next one."""
if self.road.network.get_lane(self.target_lane_index).after_end(self.position):
self.target_lane_index = self.road.network.next_lane(self.target_lane_index,
route=self.route,
position=self.position,
np_random=self.road.np_random)
[docs] def steering_control(self, target_lane_index: LaneIndex) -> float:
"""
Steer the vehicle to follow the center of an given lane.
1. Lateral position is controlled by a proportional controller yielding a lateral speed command
2. Lateral speed command is converted to a heading reference
3. Heading is controlled by a proportional controller yielding a heading rate command
4. Heading rate command is converted to a steering angle
:param target_lane_index: index of the lane to follow
:return: a steering wheel angle command [rad]
"""
target_lane = self.road.network.get_lane(target_lane_index)
lane_coords = target_lane.local_coordinates(self.position)
lane_next_coords = lane_coords[0] + self.speed * self.TAU_PURSUIT
lane_future_heading = target_lane.heading_at(lane_next_coords)
# Lateral position control
lateral_speed_command = - self.KP_LATERAL * lane_coords[1]
# Lateral speed to heading
heading_command = np.arcsin(np.clip(lateral_speed_command / utils.not_zero(self.speed), -1, 1))
heading_ref = lane_future_heading + np.clip(heading_command, -np.pi/4, np.pi/4)
# Heading control
heading_rate_command = self.KP_HEADING * utils.wrap_to_pi(heading_ref - self.heading)
# Heading rate to steering angle
slip_angle = np.arcsin(np.clip(self.LENGTH / 2 / utils.not_zero(self.speed) * heading_rate_command, -1, 1))
steering_angle = np.arctan(2 * np.tan(slip_angle))
steering_angle = np.clip(steering_angle, -self.MAX_STEERING_ANGLE, self.MAX_STEERING_ANGLE)
return float(steering_angle)
[docs] def speed_control(self, target_speed: float) -> float:
"""
Control the speed of the vehicle.
Using a simple proportional controller.
:param target_speed: the desired speed
:return: an acceleration command [m/s2]
"""
return self.KP_A * (target_speed - self.speed)
[docs] def get_routes_at_intersection(self) -> List[Route]:
"""Get the list of routes that can be followed at the next intersection."""
if not self.route:
return []
for index in range(min(len(self.route), 3)):
try:
next_destinations = self.road.network.graph[self.route[index][1]]
except KeyError:
continue
if len(next_destinations) >= 2:
break
else:
return [self.route]
next_destinations_from = list(next_destinations.keys())
routes = [self.route[0:index+1] + [(self.route[index][1], destination, self.route[index][2])]
for destination in next_destinations_from]
return routes
[docs] def set_route_at_intersection(self, _to: int) -> None:
"""
Set the road to be followed at the next intersection.
Erase current planned route.
:param _to: index of the road to follow at next intersection, in the road network
"""
routes = self.get_routes_at_intersection()
if routes:
if _to == "random":
_to = self.road.np_random.randint(len(routes))
self.route = routes[_to % len(routes)]
[docs] def predict_trajectory_constant_speed(self, times: np.ndarray) -> Tuple[List[np.ndarray], List[float]]:
"""
Predict the future positions of the vehicle along its planned route, under constant speed
:param times: timesteps of prediction
:return: positions, headings
"""
coordinates = self.lane.local_coordinates(self.position)
route = self.route or [self.lane_index]
return tuple(zip(*[self.road.network.position_heading_along_route(route, coordinates[0] + self.speed * t, 0)
for t in times]))
[docs]class MDPVehicle(ControlledVehicle):
"""A controlled vehicle with a specified discrete range of allowed target speeds."""
DEFAULT_TARGET_SPEEDS = np.linspace(20, 30, 3)
def __init__(self,
road: Road,
position: List[float],
heading: float = 0,
speed: float = 0,
target_lane_index: Optional[LaneIndex] = None,
target_speed: Optional[float] = None,
target_speeds: Optional[Vector] = None,
route: Optional[Route] = None) -> None:
"""
Initializes an MDPVehicle
:param road: the road on which the vehicle is driving
:param position: its position
:param heading: its heading angle
:param speed: its speed
:param target_lane_index: the index of the lane it is following
:param target_speed: the speed it is tracking
:param target_speeds: the discrete list of speeds the vehicle is able to track, through faster/slower actions
:param route: the planned route of the vehicle, to handle intersections
"""
super().__init__(road, position, heading, speed, target_lane_index, target_speed, route)
self.target_speeds = np.array(target_speeds) if target_speeds is not None else self.DEFAULT_TARGET_SPEEDS
self.speed_index = self.speed_to_index(self.target_speed)
self.target_speed = self.index_to_speed(self.speed_index)
[docs] def act(self, action: Union[dict, str] = None) -> None:
"""
Perform a high-level action.
- If the action is a speed change, choose speed from the allowed discrete range.
- Else, forward action to the ControlledVehicle handler.
:param action: a high-level action
"""
if action == "FASTER":
self.speed_index = self.speed_to_index(self.speed) + 1
elif action == "SLOWER":
self.speed_index = self.speed_to_index(self.speed) - 1
else:
super().act(action)
return
self.speed_index = int(np.clip(self.speed_index, 0, self.target_speeds.size - 1))
self.target_speed = self.index_to_speed(self.speed_index)
super().act()
[docs] def index_to_speed(self, index: int) -> float:
"""
Convert an index among allowed speeds to its corresponding speed
:param index: the speed index []
:return: the corresponding speed [m/s]
"""
return self.target_speeds[index]
[docs] def speed_to_index(self, speed: float) -> int:
"""
Find the index of the closest speed allowed to a given speed.
Assumes a uniform list of target speeds to avoid searching for the closest target speed
:param speed: an input speed [m/s]
:return: the index of the closest speed allowed []
"""
x = (speed - self.target_speeds[0]) / (self.target_speeds[-1] - self.target_speeds[0])
return np.int64(np.clip(np.round(x * (self.target_speeds.size - 1)), 0, self.target_speeds.size - 1))
[docs] @classmethod
def speed_to_index_default(cls, speed: float) -> int:
"""
Find the index of the closest speed allowed to a given speed.
Assumes a uniform list of target speeds to avoid searching for the closest target speed
:param speed: an input speed [m/s]
:return: the index of the closest speed allowed []
"""
x = (speed - cls.DEFAULT_TARGET_SPEEDS[0]) / (cls.DEFAULT_TARGET_SPEEDS[-1] - cls.DEFAULT_TARGET_SPEEDS[0])
return np.int64(np.clip(
np.round(x * (cls.DEFAULT_TARGET_SPEEDS.size - 1)), 0, cls.DEFAULT_TARGET_SPEEDS.size - 1))
@classmethod
def get_speed_index(cls, vehicle: Vehicle) -> int:
return getattr(vehicle, "speed_index", cls.speed_to_index_default(vehicle.speed))
[docs] def predict_trajectory(self, actions: List, action_duration: float, trajectory_timestep: float, dt: float) \
-> List[ControlledVehicle]:
"""
Predict the future trajectory of the vehicle given a sequence of actions.
:param actions: a sequence of future actions.
:param action_duration: the duration of each action.
:param trajectory_timestep: the duration between each save of the vehicle state.
:param dt: the timestep of the simulation
:return: the sequence of future states
"""
states = []
v = copy.deepcopy(self)
t = 0
for action in actions:
v.act(action) # High-level decision
for _ in range(int(action_duration / dt)):
t += 1
v.act() # Low-level control action
v.step(dt)
if (t % int(trajectory_timestep / dt)) == 0:
states.append(copy.deepcopy(v))
return states
