摘要:智慧交通灯控制能够有效地改善道路交通的秩序和效率.在城市交通网络中，具有紧急任务的特殊车辆对于通行效率的要求更高.目前已有的智慧交通灯控制算法通常对路网中的所有车辆一视同仁，没有考虑到特殊车辆的优先性；而传统的控制特殊车辆优先通行的方法基本上都是采用信号抢占的方式，对普通车辆的通行干扰过大.为此，提出一种面向优先车辆感知的交通灯优化控制方法，通过与道路环境的不断交互来学习交通灯控制策略，在设置状态和奖励函数时增加特殊车辆的权重，并利用Double DQN和Dueling DQN来提升模型表现，最终在城市交通模拟器SUMO中进行仿真实验.在训练趋于稳定之后，与固定时长控制方法的对比实验结果显示，该方法能够将特殊车辆与普通车辆的平均等待时间分别缩短68%与22%左右；与不考虑优先级的方法相比，特殊车辆的平均等待时间也有35%左右的优化.验证了该方法能够在提高车辆通行效率的同时，体现出对特殊车辆的优先处理.同时，实验也表明该方法能够扩展应用于多路口场景中.



Abstract:Intelligent traffic light control can effectively improve the order and efficiency of road traffic. In urban traffic networks, special vehicles with urgent tasks have higher requirements for traffic efficiency. However, current intelligent traffic light control algorithms generally treat all vehicles equally, without considering the priority of special vehicles, while the traditional methods basically adopt signal preemption to ensure the priority of special vehicles, which has a great influence on the passage of ordinary vehicles. Therefore, this study proposes a traffic light optimization control method orient priority vehicle awareness. It learns traffic light control strategies through continuous interaction with the road environment. the weight of special vehicles is increased in state definition and reward function, and Double DQN and Dueling DQN are used to improve the performance of the model. Finally, the experiments are carried out in the urban traffic simulator SUMO. After the training stabilizes, compared with the fixed time control method, the proposed method can reduce the average waiting time of special vehicles and ordinary vehicles by about 68% and 22%, respectively. Compared with the method without considering priority, the average waiting time of special vehicles is also optimized by about 35%, all these results prove that the proposed method can not only improve the efficiency of all vehicles, but also give special vehicles higher priority. At the same time, the experiment also shows that the proposed method can be extended to apply in multi-intersection scenes.



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