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| 邵明莉,曹鹗,胡铭,章玥,陈闻杰,陈铭松.面向优先车辆感知的交通灯优化控制方法.软件学报,2021,32(8):4-0 |
| 面向优先车辆感知的交通灯优化控制方法 |
| Priority Vehicle Awareness Oriented Traffic Light Optimization Control Method |
| 投稿时间：2020-07-24 修订日期：2020-09-07 |
| DOI：10.13328/j.cnki.jos.006191 |
| 中文关键词: 智慧交通 交通信号控制 强化学习 深度学习 车辆优先级 |
| 英文关键词:intelligent transportation traffic signal control reinforcement learning deep learning vehicle priority |
| 基金项目:国家重点研发计划项目（2018YFB2101300）；国家自然科学基金项目（61872147）；华东师范大学优秀博士生学术创新能力提升计划项目（YBNLTS2020-041） |
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| 摘要点击次数: 55 |
| 全文下载次数: 47 |
| 中文摘要: |
| 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，本文提出了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，本文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，验证了本文方法能够在提高车辆的通行效率的同时体现出对特殊车辆的优先处理.同时实验也表明本文方法能够扩展应用于多路口场景中. |
| 英文摘要: |
| SIntelligent 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 paper proposes a priority vehicle awareness oriented traffic light optimization control method. It learns traffic light control strategies through continuous interaction with the road environment. We increase the weight of special vehicles in state definition and reward function, and use Double DQN and Dueling DQN 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, our 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 our 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 our method can be extended to apply in multi-intersection scenes. |
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