AIGC 领域多智能体系统在智能交通领域的自动驾驶协同应用

    [感知层] → [通信层] → [决策层(AIGC)] → [控制层]
       ↑           ↑             ↑              ↑
    传感器数据  V2V/V2I通信  多智能体协同算法  车辆执行机构
    
    

    import torch
    import torch.nn as nn
    import torch.optim as optim
    import numpy as np
    
    class MultiAgentDRL:
    def __init__(self, num_agents, state_dim, action_dim):
        self.num_agents = num_agents
        self.agents = [DRLAgent(state_dim, action_dim) for _ in range(num_agents)]
        self.memory = ReplayBuffer(capacity=10000)
        self.gamma = 0.95  # 折扣因子
        self.batch_size = 64
        
    def select_actions(self, states):
        """为所有智能体选择动作"""
        actions = []
        for agent, state in zip(self.agents, states):
            action = agent.select_action(state)
            actions.append(action)
        return np.array(actions)
    
    def update(self):
        """更新所有智能体的策略"""
        if len(self.memory) < self.batch_size:
            return
        
        # 从记忆库中采样一批经验
        batch = self.memory.sample(self.batch_size)
        states, actions, rewards, next_states, dones = batch
        
        for i, agent in enumerate(self.agents):
            # 计算当前Q值
            current_q = agent.q_network(states[:,i]).gather(1, actions[:,i].long())
            
            # 计算目标Q值
            with torch.no_grad():
                next_q = agent.target_network(next_states[:,i]).max(1)[0].unsqueeze(1)
                target_q = rewards[:,i] + self.gamma * next_q * (1 - dones[:,i])
            
            # 计算损失并更新
            loss = nn.MSELoss()(current_q, target_q)
            agent.optimizer.zero_grad()
            loss.backward()
            agent.optimizer.step()
            
            # 更新目标网络
            agent.update_target()
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/UPpFt4EClZ7TS9mVaQgHkKMB06Ru.png)

    # 推荐使用Python 3.8+和以下库
    pip install torch numpy gym pygame matplotlib
    
    
    bash

    import gym
    from gym import spaces
    import numpy as np
    import pygame
    
    class IntersectionEnv(gym.Env):
    def __init__(self, num_vehicles=4):
        super(IntersectionEnv, self).__init__()
        self.num_vehicles = num_vehicles
        self.action_space = spaces.Discrete(3)  # 0:减速, 1:保持, 2:加速
        self.observation_space = spaces.Box(low=0, high=100, shape=(num_vehicles, 5))
        
        # 初始化车辆位置和速度
        self.positions = np.array([[0, 50], [50, 0], [100, 50], [50, 100]])  # 四向进入
        self.velocities = np.ones(num_vehicles) 
        self.destinations = np.array([[100, 50], [50, 100], [0, 50], [50, 0]])
        
        # 可视化设置
        pygame.init()
        self.screen = pygame.display.set_mode((600, 600))
        self.clock = pygame.time.Clock()
    
    def step(self, actions):
        # 执行动作
        for i in range(self.num_vehicles):
            if actions[i] == 0:  # 减速
                self.velocities[i] = max(0.5, self.velocities[i] - 0.5)
            elif actions[i] == 2:  # 加速
                self.velocities[i] = min(5, self.velocities[i] + 0.5)
        
        # 更新位置
        directions = self.destinations - self.positions
        norms = np.linalg.norm(directions, axis=1)
        directions = directions / norms[:, np.newaxis]
        self.positions += directions * self.velocities[:, np.newaxis]
        
        # 计算奖励
        rewards = np.zeros(self.num_vehicles)
        collisions = self._check_collisions()
        
        # 基础奖励：距离目的地更近
        new_dists = np.linalg.norm(self.destinations - self.positions, axis=1)
        rewards += (self.last_dists - new_dists) * 0.1
        self.last_dists = new_dists.copy()
        
        # 碰撞惩罚
        rewards -= collisions 
        
        # 完成奖励
        done_mask = new_dists < 5
        rewards += done_mask 
        
        # 构建观察
        obs = self._get_obs()
        
        # 判断是否结束
        done = np.all(done_mask) or np.sum(collisions) > 0
        
        return obs, rewards, done, {}
    
    def _check_collisions(self):
        # 简化碰撞检测
        dists = np.zeros((self.num_vehicles, self.num_vehicles))
        for i in range(self.num_vehicles):
            for j in range(i+1, self.num_vehicles):
                dists[i,j] = np.linalg.norm(self.positions[i] - self.positions[j])
        return dists < 10  # 简化碰撞阈值
    
    def _get_obs(self):
        # 构建每个车辆的观察：自身位置、速度+其他车辆相对位置
        obs = np.zeros((self.num_vehicles, 5))
        for i in range(self.num_vehicles):
            obs[i,0] = self.positions[i,0]
            obs[i,1] = self.positions[i,1]
            obs[i,2] = self.velocities[i]
            # 最近车辆的相对位置
            rel_pos = self.positions - self.positions[i]
            dists = np.linalg.norm(rel_pos, axis=1)
            dists[i] = float('inf')  # 忽略自身
            closest = np.argmin(dists)
            obs[i,3:] = rel_pos[closest]
        return obs
    
    def render(self):
        self.screen.fill((255, 255, 255))
        # 绘制交叉路口
        pygame.draw.line(self.screen, (0,0,0), (300,100), (300,500), 2)
        pygame.draw.line(self.screen, (0,0,0), (100,300), (500,300), 2)
        
        # 绘制车辆
        for i in range(self.num_vehicles):
            pos = self.positions[i] * 5 + np.array([50,50])  # 缩放和偏移
            pygame.draw.circle(self.screen, (255,0,0), pos.astype(int), 8)
        
        pygame.display.flip()
        self.clock.tick(30)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/CqZNigIKyd7r8aGmERxu2DjFYspn.png)

 * PyTorch/TensorFlow：深度学习框架
 * RLlib：分布式强化学习库
 * SMARTS：自动驾驶仿真平台

 * CARLA：开源自动驾驶仿真器
 * SUMO：交通流仿真软件
 * Flow：多智能体交通控制框架

 * Argoverse：自动驾驶轨迹预测数据集
 * nuScenes：自动驾驶多传感器数据集
 * HighD：高速公路车辆轨迹数据集

 * 《Multi-Agent Reinforcement Learning: A Survey》
 * 《Autonomous Driving in Multi-Agent Traffic Environments》
 * DeepMind多智能体学习教程