Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow

doi:10.1016/j.trc.2021.103478

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow
	Wang, Yibing 1; Wang, Long 1; Guo, Jingqiu 2; Papamichail, Ioannis 3; Papageorgiou, Markos 3; Wang, Fei-Yue4 ; Bertini, Robert 5; Hua, Wei 6; Yang, Qinmin 7
发表期刊	TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
ISSN	0968-090X
	2022-05-01
卷号	138 页码:25
通讯作者	Wang, Yibing(wangyibing@zju.edu.cn) ; Guo, Jingqiu(guojingqiu@hotmail.com)
摘要	Connected and automated vehicles (CAVs) enabled by wireless communication and vehicle automation are believed to revolutionize the form and operation of road transport in the next decades. This paper addresses traffic flow effects of CAVs, and focuses on their lane-changing impacts on the mixed traffic flow of CAVs and human-driven vehicles (HVs). At present technical paths towards the development and deployment of CAVs are still uncertain. With CAV technologies getting matured, CAVs are supposed to provide rides of higher efficiency than HVs, beyond improved safety and comfort. In heavy traffic, this would only be achievable via agile and flexible lane changes of CAVs, because longitudinal acceleration would be unhelpful or even impossible. Such lane changes are expected to be ego-efficient in that they serve solely CAVs' interests without much considering surrounding vehicles, as long as safety constraints are not violated. As road resources are limited, the growth of the CAV population adopting such ego efficient lane-changing strategies would probably lead to renowned "Tragedy of the Commons". In this context, this paper considers three important prospective questions: A: How to determine an ego-efficient lane-changing strategy for CAVs? B: With more CAVs introduced each adopting the ego-efficient lane-changing strategy, what is the impact on traffic flow? C: How to determine a system-efficient lane-changing strategy for CAVs? These forward-looking issues are addressed from the perspectives of microscopic traffic simulation and reinforcement learning. Without any constraint on the lane-changing incentive, the developed lane-changing strategy was found to be beneficial for CAVs and the entire traffic flow only if the market penetration rate (MPR) of CAVs is less than 50%. With an appropriate constraint placed, however, the lane-changing strategy was found to become consistently beneficial for the entire traffic flow at any MPR. These findings suggest that CAVs may not simply be a magic cure for traffic problems that the society is currently facing, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic. This is also consistent with what "Tragedy of the Commons" suggests.
关键词	Ego-efficient Lane Changes Traffic Flow Impacts Microscopic Simulation Reinforcement Learning
DOI	10.1016/j.trc.2021.103478
关键词[WOS]	ADAPTIVE CRUISE CONTROL ; MODEL-PREDICTIVE CONTROL ; DECISION-MAKING ; SYSTEMS
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1600500] ; National Key Research and Development Program of China[2017YFE9134700] ; National Natural Science Foundation of China[71771200] ; National Natural Science Foundation of China[52172306] ; Provincial Key R&D Program of Zhejiang[2021C01012]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Provincial Key R&D Program of Zhejiang
WOS研究方向	Transportation
WOS类目	Transportation Science & Technology
WOS记录号	WOS:000792881700001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49387
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wang, Yibing; Guo, Jingqiu
作者单位	1.Zhejiang Univ, Inst Intelligent Transportat Syst, Hangzhou 310058, Peoples R China 2.Tongji Univ, Minist Educ China, Key Lab Rd & Traff Engn, Shanghai 201804, Peoples R China 3.Tech Univ Crete, Sch Prod Engn & Management, Dynam Syst & Simulat Lab, Khania, Greece 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Oregon State Univ, Sch Civil & Construct Engn, Corvallis, OR 97331 USA 6.Zhejiang Lab, Res Ctr Smart Transportat, Hangzhou 311121, Peoples R China 7.Zhejiang Univ, Coll Control Sci & Engn, State Key Lab Ind Control Technol, Hangzhou 310027, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yibing,Wang, Long,Guo, Jingqiu,et al. Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow[J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,2022,138:25.
APA	Wang, Yibing.,Wang, Long.,Guo, Jingqiu.,Papamichail, Ioannis.,Papageorgiou, Markos.,...&Yang, Qinmin.(2022).Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow.TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES,138,25.
MLA	Wang, Yibing,et al."Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow".TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 138(2022):25.