本文基于前几篇对highway场景的介绍，来说明如何实现自定义仿真场景。

1. set up files

定义自己的Env.py，继承AbstractEnv

抽象类中的几个重点函数：

• default_config()：配置文件的载入
• define_spaces()：选择observation和action类型
• step()：按照策略更新频率执行action
• render()：渲染整个环境

2. create the scene

首先创建RoadNetwork

对应method：YourEnv._make_road()

调用method：yourEnv.reset()设置self.road

举例:

以下代码用于构建一个十字路口：

    def _make_road(self) -> None:
        """
        Make an 4-way intersection.

        The horizontal road has the right of way. More precisely, the levels of priority are:
            - 3 for horizontal straight lanes and right-turns
            - 1 for vertical straight lanes and right-turns
            - 2 for horizontal left-turns
            - 0 for vertical left-turns

        The code for nodes in the road network is:
        (o:outer | i:inner + [r:right, l:left]) + (0:south | 1:west | 2:north | 3:east)

        :return: the intersection road
        """
        lane_width = AbstractLane.DEFAULT_WIDTH
        right_turn_radius = lane_width + 5  # [m}
        left_turn_radius = right_turn_radius + lane_width  # [m}
        outer_distance = right_turn_radius + lane_width / 2
        access_length = 50 + 50  # [m]

        net = RoadNetwork()
        n, c, s = LineType.NONE, LineType.CONTINUOUS, LineType.STRIPED
        for corner in range(4):
            angle = np.radians(90 * corner)
            is_horizontal = corner % 2
            priority = 3 if is_horizontal else 1
            rotation = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])
            # Incoming
            start = rotation @ np.array([lane_width / 2, access_length + outer_distance])
            end = rotation @ np.array([lane_width / 2, outer_distance])
            net.add_lane("o" + str(corner), "ir" + str(corner),
                         StraightLane(start, end, line_types=[s, c], priority=priority, speed_limit=10))
            # Right turn
            r_center = rotation @ (np.array([outer_distance, outer_distance]))
            net.add_lane("ir" + str(corner), "il" + str((corner - 1) % 4),
                         CircularLane(r_center, right_turn_radius, angle + np.radians(180), angle + np.radians(270),
                                      line_types=[n, c], priority=priority, speed_limit=10))
            # Left turn
            l_center = rotation @ (np.array([-left_turn_radius + lane_width / 2, left_turn_radius - lane_width / 2]))
            net.add_lane("ir" + str(corner), "il" + str((corner + 1) % 4),
                         CircularLane(l_center, left_turn_radius, angle + np.radians(0), angle + np.radians(-90),
                                      clockwise=False, line_types=[n, n], priority=priority - 1, speed_limit=10))
            # Straight
            start = rotation @ np.array([lane_width / 2, outer_distance])
            end = rotation @ np.array([lane_width / 2, -outer_distance])
            net.add_lane("ir" + str(corner), "il" + str((corner + 2) % 4),
                         StraightLane(start, end, line_types=[s, n], priority=priority, speed_limit=10))
            # Exit
            start = rotation @ np.flip([lane_width / 2, access_length + outer_distance], axis=0)
            end = rotation @ np.flip([lane_width / 2, outer_distance], axis=0)
            net.add_lane("il" + str((corner - 1) % 4), "o" + str((corner - 1) % 4),
                         StraightLane(end, start, line_types=[n, c], priority=priority, speed_limit=10))

        road = RegulatedRoad(network=net, np_random=self.np_random, record_history=self.config["show_trajectories"])
        self.road = road

3. create the vehicles

populate road network with vehicles. 用车填充road

    def _make_vehicles(self, n_vehicles: int = 10) -> None:
        """
        Populate a road with several vehicles on the highway and on the merging lane

        :return: the ego-vehicle
        """
        # Configure vehicles
        vehicle_type = utils.class_from_path(self.config["other_vehicles_type"])
        vehicle_type.DISTANCE_WANTED = 7  # Low jam distance
        vehicle_type.COMFORT_ACC_MAX = 6
        vehicle_type.COMFORT_ACC_MIN = -3

        # Random vehicles
        simulation_steps = 3
        for t in range(n_vehicles - 1):
            self._spawn_vehicle(np.linspace(0, 80, n_vehicles)[t])
        for _ in range(simulation_steps):
            [(self.road.act(), self.road.step(1 / self.config["simulation_frequency"])) for _ in range(self.config["simulation_frequency"])]

        # Challenger vehicle
        self._spawn_vehicle(60, spawn_probability=1, go_straight=True, position_deviation=0.1, speed_deviation=0)

        # Controlled vehicles
        self.controlled_vehicles = []
        for ego_id in range(0, self.config["controlled_vehicles"]):
            ego_lane = self.road.network.get_lane(("o{}".format(ego_id % 4), "ir{}".format(ego_id % 4), 0))
            destination = self.config["destination"] or "o" + str(self.np_random.randint(1, 4))
            ego_vehicle = self.action_type.vehicle_class(
                             self.road,
                             ego_lane.position(60 + 5*self.np_random.randn(1), 0),
                             speed=ego_lane.speed_limit,
                             heading=ego_lane.heading_at(60)) \
                .plan_route_to(destination)
            ego_vehicle.SPEED_MIN = 0
            ego_vehicle.SPEED_MAX = 9
            ego_vehicle.SPEED_COUNT = 3
            ego_vehicle.speed_index = ego_vehicle.speed_to_index(ego_lane.speed_limit)
            ego_vehicle.target_speed = ego_vehicle.index_to_speed(ego_vehicle.speed_index)

            self.road.vehicles.append(ego_vehicle)
            self.controlled_vehicles.append(ego_vehicle)
            for v in self.road.vehicles:  # Prevent early collisions
                if v is not ego_vehicle and np.linalg.norm(v.position - ego_vehicle.position) < 20:
                    self.road.vehicles.remove(v)

4. register the environments

highway_env/envs/your_env.py中， add the following line:

register(
    id='your-env-v0',
    entry_point='highway_env.envs:YourEnv',
)

highway_env/envs/__init__.py文件中，添加如下代码：

from highway_env.envs.your_env import *

5. have a try

import gym
import highway_env

env = gym.make('your-env-v0')
obs = env.reset()
obs, reward, done, info = env.step(env.action_space.sample())
env.render()