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汽车安全与节能学报

JASE ›› 2019, Vol. 10 ›› Issue (4): 531-539.DOI: 10.3969/j.issn.1674-8484.2019.04.016

• 汽车节能与环保 • 上一篇    

自动驾驶环境下车辆轨迹及交通信号协同控制

戴荣健1，丁川*1,2，鹿应荣1，赵福全2   

  1. （1. 北京航空航天大学 交通科学与工程学院，北京 100191，中国；
     2. 汽车安全与节能国家重点实验室，清华大学，北京 100084，中国）
  • 收稿日期:2019-05-28 出版日期:2019-12-31 发布日期:2020-01-01
  • 通讯作者: 丁川（ 1986 －），男（汉），北京，副教授。E-mail: cding@buaa.edu.cn。
  • 基金资助:

    汽车安全与节能国家重点实验室开放基金（KF1805）；国家自然科学基金（U1764265）。

Cooperated control of signal and vehicle trajectory under the autonomous vehicle environment

DAI Rongjian1，DING Chuan*1,2，LU Yingrong1，ZHAO Fuquan2   

  1. （1. School of Transportation Science and engineering, Beihang University, Beijing 100191, China;
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China）
  • Received:2019-05-28 Online:2019-12-31 Published:2020-01-01
  • About author:第一作者 / First author ： 戴荣健（1991－），男（汉），北京，博士。E-mail: rjdai0327@buaa.edu.cn。

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摘要： 在自动驾驶环境下的交叉路口，为了提高车辆通行效率并减少停车次数，利用自动驾驶技术 （AV）和车路协同系统 (CVIS)，开发一种交通信号灯和车辆轨迹协同优化控制方法，并利用多智能体 技术进行仿真。该控制方法通过车辆速度、位置等信息对信号灯配时进行优化；并根据信号灯优化配 时，对车辆轨迹进行优化控制。利用 NETLOGO 仿真平台，搭建多智能体仿真平台，进行仿真试验。 试验结果表明和传统固定配时相比本文提出的信号交叉口车路协同控制方法能够降低 53.4% 的车辆平 均通行时间和 61.5% 的平均停车次数，表明：该控制方法提高了信号交叉口通行效率，减少了停车次数。

关键词:  自动驾驶车辆(AV), 路协同系统 (CVIS), 迹控制, 叉口信号优化, 同控制, 智能体仿真

Abstract:  A cooperated control method of signal and vehicle trajectory was proposed for isolate signal intersection based on the technologies of cooperative vehicle and infrastructure system (CVIS) and autonomous vehicle (AV) to improve the traffic efficiency and reduce the stop times of vehicles at the signal intersection under the autonomous vehicle environment. The method was simulated using the multi-agent technology. The signals in this control method were optimized according to the information of AVs, such as position, speed, etc. The optimized signal-timing plan was used to control trajectories of AVs. A multi-agent simulation environment was developed by NETLOGO, by which simulation experiments were conducted. The results indicate that this control method reduces about 53.4% travel time and 61.5% stop times of vehicles compared with traditional signal control strategy. Therefore, the method improves traffic efficiency and reducts stop-times.

Key words:  autonomous vehicles (AV), cooperative vehicle infrastructure system (CVIS), trajectory control, intersection signal optimization, Cooperated control of signal and trajectory, multi-agent simulation