摘要
The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.
在汽车跟驰(CF)过程中,智能网联自动驾驶技术为驾驶员提供了更多的信息。与人类驾驶车辆(HVs)只考虑前方信息不同,智能网联自动驾驶车辆(CAVs)能够获取前方和后方的信息,增强了车辆的感知能力。本文提出了一种考虑回望距离的智能驾驶模型(IBDM),考虑了CAVs环境下车辆跟驰行驶时相对于前后车的期望距离。基于智能驾驶模型(IDM),IBDM将车辆后方信息作为控制项集成进模型中。利用线性稳定性原理和李雅普诺夫稳定性分析方法,分析了均质交通流中在小扰动下的稳定性条件。为了验证理论分析的正确性,在开放边界条件下,在单车道上进行了仿真,并与不考虑后车的IDM和考虑前后车信息的扩展IDM进行了比较。设计了六种方案,对不同干扰强度、干扰位置和初始队列间距下的结果进行评估。结果表明,IBDM在控制CAVs跟驰过程中保持弦稳定性方面优于IDM和扩展IDM,并通过增加考虑前车的比重来提高稳定性。
作者
YI Zi-wei
LU Wen-qi
XU Ling-hui
QU Xu
RAN Bin
易紫薇;陆文琦;徐凌慧;曲栩;冉斌(School of Transportation,Southeast University,Nanjing 211189,China;Joint Research Institute on Internet of Mobility,Southeast University and University of Wisconsin-Madison,Nanjing 211189,China;Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies,Nanjing 211189,China)
基金
Project(2018YFB1600600)supported by the National Key Research and Development Program,China
Project(20YJAZH083)supported by the Ministry of Education,China
Project(20YJAZH083)supported by the Humanities and Social Sciences,China
Project(51878161)supported by the National Natural Science Foundation of China。
作者简介
Corresponding author:QU Xu,PhD,Associate Professor,Tel:+86-13584010880,E-mail:quxu@seu.edu.cn,ORCID:https://orcid.org/0000-0003-3256-8920。
