Simulation scenario description:
Scene name: Urban road intersection
Scene description:
Vehicles travel on a two-way, four-lane city main road with sidewalks and green belts on both sides. Weather conditions were clear with high visibility. Not far ahead was an intersection with a working traffic light.
Simulation environment Settings:
Road layout: two-way four-lane urban main road with isolation belt in the middle. Footpaths are set on both sides of the road and are separated by green belts.
Traffic facilities: The intersection is equipped with traffic lights, the initial state is green, allowing vehicles to go straight and turn left. There are pedestrian waiting areas at four corners of the intersection.
Other traffic participants: When the simulation begins, several other vehicles are already on the road, and they will obey the traffic rules. There are a few pedestrians waiting to cross the road near the junction.
Weather and time: Set for a clear day, sunshine, dry road, good visibility.
Autonomous vehicle tasks:
Starting from the starting point, the self-driving vehicle needs to pass the intersection in front of it in accordance with the traffic rules, during which it needs to identify the status of the traffic light, determine whether it can pass, and pay attention to avoid other vehicles and pedestrians.
Simulation process observation point:
Whether the vehicle can accurately recognize the traffic light status and react correctly.
Whether the vehicle is able to maintain a safe distance from other vehicles and pedestrians while passing through the intersection.
The stability and comfort of the vehicle during driving.
Expected results:
Autonomous vehicles are able to navigate intersections smoothly and safely, obeying traffic rules, while ensuring ride comfort.

仿真场景描述：
场景名称： 城市道路交叉口
场景描述：
车辆行驶在一条双向四车道的城市主干道上，道路两侧设有人行道和绿化带。天气条件晴朗，能见度高。前方不远处是一个十字路口，交通信号灯正常工作。
仿真环境设定：
道路布局：双向四车道城市主干道，中间设有隔离带。道路两侧设置人行道，并有绿化带分隔。
交通设施：十字路口设有红绿灯，初始状态为绿灯，允许车辆直行和左转。路口四角设有行人等待区。
其他交通参与者：仿真开始时，道路上已有若干其他车辆在行驶，它们将遵守交通规则。路口附近有少量行人等待过马路。
天气与时间：设定为晴朗的白天，阳光照射，路面干燥，能见度良好。
自动驾驶车辆任务：
自动驾驶车辆从起点出发，需要按照交通规则通过前方的十字路口，期间需要识别红绿灯状态，判断是否可以通行，并注意避让其他车辆和行人。
仿真过程观察点：
车辆是否能够准确识别红绿灯状态并作出正确反应。
车辆在通过路口时是否能够与其他车辆和行人保持安全距离。
车辆在行驶过程中的稳定性和舒适性。
期望结果：
自动驾驶车辆能够顺利、安全地通过十字路口，遵守交通规则，同时保证乘坐的舒适性。

# 导入仿真环境库  
from simulation_environment import SimulationEnvironment  
from vehicle import AutonomousVehicle  
from traffic_light import TrafficLight  
from road import Road, Intersection  
from pedestrian import Pedestrian  
  
# 创建仿真环境  
env = SimulationEnvironment()  
  
# 创建道路布局  
road = Road(lanes=4, is_bidirectional=True, has_median=True)  
intersection = Intersection(road, traffic_lights=True)  
env.add_road(road)  
env.add_intersection(intersection)  
  
# 添加交通设施：红绿灯  
traffic_light = TrafficLight(intersection, initial_state='green')  
env.add_traffic_light(traffic_light)  
  
# 添加其他交通参与者  
for _ in range(5):  
    other_vehicle = AutonomousVehicle(road)  
    env.add_vehicle(other_vehicle)  
  
for _ in range(3):  
    pedestrian = Pedestrian(intersection)  
    env.add_pedestrian(pedestrian)  
  
# 设定天气与时间  
env.set_weather(sunny=True, visibility='high')  
env.set_time_of_day('day')  
  
# 创建自动驾驶车辆并添加到环境中  
autonomous_vehicle = AutonomousVehicle(road, is_autonomous=True)  
env.add_vehicle(autonomous_vehicle)  
  
# 设置自动驾驶车辆的任务  
autonomous_vehicle.set_task(cross_intersection=intersection)  
  
# 开始仿真  
env.start_simulation()  
  
# 仿真循环  
while env.is_running():  
    # 更新环境状态  
    env.update()  
      
    # 检查自动驾驶车辆是否完成任务  
    if autonomous_vehicle.has_completed_task():  
        print("自动驾驶车辆成功通过交叉口！")  
        break  
  
# 结束仿真  
env.stop_simulation()

Adding a traffic light in CARLA usually involves several steps, including locating the location of the light in the scene, creating the light object, and placing it in a simulation environment. Here is a basic guide on how to add traffic lights in CARLA:
Determine the signal light position:
First, you need to determine exactly where to place your traffic lights on the map. This is usually at an intersection of a road or where traffic flow needs to be controlled.
Edit map (optional) :
If you're using a custom map, you can add the semaphore directly in a map editor such as RoadRunner and export it to a format CARLA can recognize.
Add semaphore using CARLA's API:
If you are adding semaphore dynamically at runtime, you can use CARLA's Python API. Here's a simplified example of how to add a simple traffic light to CARLA using the Python API:

import carla  
  
# 连接到CARLA服务器  
client = carla.Client('localhost', 2000)  
client.set_timeout(10.0)  
world = client.get_world()  
  
# 定义信号灯的位置和旋转  
location = carla.Location(x=100, y=200, z=2)  # 示例坐标，需要根据实际情况调整  
rotation = carla.Rotation(pitch=0, yaw=0, roll=0)  # 根据需要调整旋转角度  
  
# 创建交通信号灯蓝图  
bp_library = world.get_blueprint_library()  
traffic_light_bp = bp_library.find('static.traffic_light')  
if traffic_light_bp is None:  
    print("Traffic light blueprint not found!")  
    exit()  
  
# 设置信号灯的状态和灯组（可选）  
# 例如，可以通过修改属性来改变信号灯的颜色和闪烁模式  
  
# 在世界中生成交通信号灯  
traffic_light = world.spawn_actor(traffic_light_bp, carla.Transform(location, rotation))  
  
# 如果需要，可以保存交通信号灯对象的引用以便后续操作