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A Q-learning foresighted approach to ego-efficient lane changes of connected and automated vehicles on freeways

Wang Long, Ye Fangmin, Wang Yibing, Guo Jingqiu, Papamichail Ioannis, Papageorgiou Markos, Hu Simon, Zhang Lihui

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URIhttp://purl.tuc.gr/dl/dias/E1C1B57C-7501-4AB3-8E25-01114358CDF3-
Identifierhttps://doi.org/10.1109/ITSC.2019.8917036-
Identifierhttps://ieeexplore.ieee.org/document/8917036-
Languageen-
Extent8 pagesen
TitleA Q-learning foresighted approach to ego-efficient lane changes of connected and automated vehicles on freewaysen
CreatorWang Longen
CreatorYe Fangminen
CreatorWang Yibingen
CreatorGuo Jingqiuen
CreatorPapamichail Ioannisen
CreatorΠαπαμιχαηλ Ιωαννηςel
CreatorPapageorgiou Markosen
CreatorΠαπαγεωργιου Μαρκοςel
CreatorHu Simonen
CreatorZhang Lihuien
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryLane changes are a vital part of vehicle motions on roads, affecting surrounding vehicles locally and traffic flow collectively. In the context of connected and automated vehicles (CAVs), this paper is concerned with the impacts of smart lane changes of CAVs on their own travel performance as well as on the entire traffic flow with the increase of the market penetration rate (MPR). On the basis of intensive microscopic traffic simulation and reinforcement learning technique, an ego-efficient lane-changing strategy was first developed in this work to enable foresighted lane changing decisions for CAVs to improve their travel efficiency. The overall impacts of such smart lane changes on traffic flow of both CAVs and human-driven vehicles were then examined on the same simulation platform, which reflects a real freeway infrastructure with real demands. It was found that smart lane changes were beneficial for both CAVs and the entire traffic flow, if MPR was not more than 60%.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-04-10-
Date of Publication2019-
SubjectAdvanced traffic management systemsen
SubjectIntelligent systemsen
SubjectSimulation platformen
SubjectStreet traffic controlen
SubjectVehiclesen
Bibliographic CitationL. Wang, F. Ye, Y. Wang, J. Guo, I. Papamichail, M. Papageorgiou, S. Hu and L. Zhang, "A Q-learning foresighted approach to ego-efficient lane changes of connected and automated vehicles on freeways," in IEEE Intelligent Transportation Systems Conference, 2019, pp. 1385-1392. doi: 10.1109/ITSC.2019.8917036en

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