from typing import Union, Optional, Tuple, List
import numpy as np
import copy
from collections import deque
from highway_env import utils
from highway_env.road.road import Road, LaneIndex
from highway_env.vehicle.objects import RoadObject, Obstacle, Landmark
from highway_env.utils import Vector
[docs]class Vehicle(RoadObject):
"""
A moving vehicle on a road, and its kinematics.
The vehicle is represented by a dynamical system: a modified bicycle model.
It's state is propagated depending on its steering and acceleration actions.
"""
LENGTH = 5.0
""" Vehicle length [m] """
WIDTH = 2.0
""" Vehicle width [m] """
DEFAULT_INITIAL_SPEEDS = [23, 25]
""" Range for random initial speeds [m/s] """
MAX_SPEED = 40.
""" Maximum reachable speed [m/s] """
MIN_SPEED = -40.
""" Minimum reachable speed [m/s] """
HISTORY_SIZE = 30
""" Length of the vehicle state history, for trajectory display"""
def __init__(self,
road: Road,
position: Vector,
heading: float = 0,
speed: float = 0,
predition_type: str = 'constant_steering'):
super().__init__(road, position, heading, speed)
self.prediction_type = predition_type
self.action = {'steering': 0, 'acceleration': 0}
self.crashed = False
self.impact = None
self.log = []
self.history = deque(maxlen=self.HISTORY_SIZE)
[docs] @classmethod
def create_random(cls, road: Road,
speed: float = None,
lane_from: Optional[str] = None,
lane_to: Optional[str] = None,
lane_id: Optional[int] = None,
spacing: float = 1) \
-> "Vehicle":
"""
Create a random vehicle on the road.
The lane and /or speed are chosen randomly, while longitudinal position is chosen behind the last
vehicle in the road with density based on the number of lanes.
:param road: the road where the vehicle is driving
:param speed: initial speed in [m/s]. If None, will be chosen randomly
:param lane_from: start node of the lane to spawn in
:param lane_to: end node of the lane to spawn in
:param lane_id: id of the lane to spawn in
:param spacing: ratio of spacing to the front vehicle, 1 being the default
:return: A vehicle with random position and/or speed
"""
_from = lane_from or road.np_random.choice(list(road.network.graph.keys()))
_to = lane_to or road.np_random.choice(list(road.network.graph[_from].keys()))
_id = lane_id if lane_id is not None else road.np_random.choice(len(road.network.graph[_from][_to]))
lane = road.network.get_lane((_from, _to, _id))
if speed is None:
if lane.speed_limit is not None:
speed = road.np_random.uniform(0.7*lane.speed_limit, 0.8*lane.speed_limit)
else:
speed = road.np_random.uniform(Vehicle.DEFAULT_INITIAL_SPEEDS[0], Vehicle.DEFAULT_INITIAL_SPEEDS[1])
default_spacing = 12+1.0*speed
offset = spacing * default_spacing * np.exp(-5 / 40 * len(road.network.graph[_from][_to]))
x0 = np.max([lane.local_coordinates(v.position)[0] for v in road.vehicles]) \
if len(road.vehicles) else 3*offset
x0 += offset * road.np_random.uniform(0.9, 1.1)
v = cls(road, lane.position(x0, 0), lane.heading_at(x0), speed)
return v
[docs] @classmethod
def create_from(cls, vehicle: "Vehicle") -> "Vehicle":
"""
Create a new vehicle from an existing one.
Only the vehicle 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, vehicle.heading, vehicle.speed)
if hasattr(vehicle, 'color'):
v.color = vehicle.color
return v
[docs] def act(self, action: Union[dict, str] = None) -> None:
"""
Store an action to be repeated.
:param action: the input action
"""
if action:
self.action = action
[docs] def step(self, dt: float) -> None:
"""
Propagate the vehicle state given its actions.
Integrate a modified bicycle model with a 1st-order response on the steering wheel dynamics.
If the vehicle is crashed, the actions are overridden with erratic steering and braking until complete stop.
The vehicle's current lane is updated.
:param dt: timestep of integration of the model [s]
"""
self.clip_actions()
delta_f = self.action['steering']
beta = np.arctan(1 / 2 * np.tan(delta_f))
v = self.speed * np.array([np.cos(self.heading + beta),
np.sin(self.heading + beta)])
self.position += v * dt
if self.impact is not None:
self.position += self.impact
self.crashed = True
self.impact = None
self.heading += self.speed * np.sin(beta) / (self.LENGTH / 2) * dt
self.speed += self.action['acceleration'] * dt
self.on_state_update()
def clip_actions(self) -> None:
if self.crashed:
self.action['steering'] = 0
self.action['acceleration'] = -1.0*self.speed
self.action['steering'] = float(self.action['steering'])
self.action['acceleration'] = float(self.action['acceleration'])
if self.speed > self.MAX_SPEED:
self.action['acceleration'] = min(self.action['acceleration'], 1.0 * (self.MAX_SPEED - self.speed))
elif self.speed < self.MIN_SPEED:
self.action['acceleration'] = max(self.action['acceleration'], 1.0 * (self.MIN_SPEED - self.speed))
def on_state_update(self) -> None:
if self.road:
self.lane_index = self.road.network.get_closest_lane_index(self.position, self.heading)
self.lane = self.road.network.get_lane(self.lane_index)
if self.road.record_history:
self.history.appendleft(self.create_from(self))
def predict_trajectory_constant_speed(self, times: np.ndarray) -> Tuple[List[np.ndarray], List[float]]:
if self.prediction_type == 'zero_steering':
action = {'acceleration': 0.0, 'steering': 0.0}
elif self.prediction_type == 'constant_steering':
action = {'acceleration': 0.0, 'steering': self.action['steering']}
else:
raise ValueError("Unknown predition type")
dt = np.diff(np.concatenate(([0.0], times)))
positions = []
headings = []
v = copy.deepcopy(self)
v.act(action)
for t in dt:
v.step(t)
positions.append(v.position.copy())
headings.append(v.heading)
return (positions, headings)
@property
def velocity(self) -> np.ndarray:
return self.speed * self.direction # TODO: slip angle beta should be used here
@property
def destination(self) -> np.ndarray:
if getattr(self, "route", None):
last_lane_index = self.route[-1]
last_lane_index = last_lane_index if last_lane_index[-1] is not None else (*last_lane_index[:-1], 0)
last_lane = self.road.network.get_lane(last_lane_index)
return last_lane.position(last_lane.length, 0)
else:
return self.position
@property
def destination_direction(self) -> np.ndarray:
if (self.destination != self.position).any():
return (self.destination - self.position) / np.linalg.norm(self.destination - self.position)
else:
return np.zeros((2,))
@property
def lane_offset(self) -> np.ndarray:
if self.lane is not None:
long, lat = self.lane.local_coordinates(self.position)
ang = self.lane.local_angle(self.heading, long)
return np.array([long, lat, ang])
else:
return np.zeros((3,))
def to_dict(self, origin_vehicle: "Vehicle" = None, observe_intentions: bool = True) -> dict:
d = {
'presence': 1,
'x': self.position[0],
'y': self.position[1],
'vx': self.velocity[0],
'vy': self.velocity[1],
'heading': self.heading,
'cos_h': self.direction[0],
'sin_h': self.direction[1],
'cos_d': self.destination_direction[0],
'sin_d': self.destination_direction[1],
'long_off': self.lane_offset[0],
'lat_off': self.lane_offset[1],
'ang_off': self.lane_offset[2],
}
if not observe_intentions:
d["cos_d"] = d["sin_d"] = 0
if origin_vehicle:
origin_dict = origin_vehicle.to_dict()
for key in ['x', 'y', 'vx', 'vy']:
d[key] -= origin_dict[key]
return d
def __str__(self):
return "{} #{}: {}".format(self.__class__.__name__, id(self) % 1000, self.position)
def __repr__(self):
return self.__str__()
