自定义构建用于强化学习的自动驾驶仿真场景highway

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自定义构建用于强化学习的自动驾驶仿真场景highway

2023-09-25 22:17| 来源: 网络整理| 查看: 265

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actionrender()：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)

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自定义构建用于强化学习的自动驾驶仿真场景highway

自定义构建用于强化学习的自动驾驶仿真场景highway

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