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A reinforcement learning approach to smart lane changes of self-driving cars

Ye Fangmin, Wang Long, Wang Yibing, Guo Jingqiu, Papamichail Ioannis, Papageorgiou Markos

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URIhttp://purl.tuc.gr/dl/dias/BCBDDCEA-3FEB-4C4E-A9F9-C0376F71F859-
Identifierhttps://doi.org/10.1007/978-3-030-30241-2_47-
Identifierhttps://link.springer.com/chapter/10.1007/978-3-030-30241-2_47-
Languageen-
Extent13 pagesen
TitleA reinforcement learning approach to smart lane changes of self-driving carsen
CreatorYe Fangminen
CreatorWang Longen
CreatorWang Yibingen
CreatorGuo Jingqiuen
CreatorPapamichail Ioannisen
CreatorΠαπαμιχαηλ Ιωαννηςel
CreatorPapageorgiou Markosen
CreatorΠαπαγεωργιου Μαρκοςel
PublisherSpringer Natureen
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, a selfish 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. 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 ItemΚεφάλαιο σε Βιβλίοel
Type of ItemBook Chapteren
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2020-11-02-
Date of Publication2019-
SubjectConnected and automated vehiclesen
SubjectMicroscopic simulationen
SubjectQ-learningen
SubjectSmart lane changesen
SubjectTraffic flow impactsen
Bibliographic CitationF. Ye, L. Wang, Y. Wang, J. Guo, I. Papamichail and M. Papageorgiou, "A reinforcement learning approach to smart lane changes of self-driving cars," in Progress in Artificial Intelligence. EPIA 2019, vol. 11804, Lecture Notes in Computer Science, O. P. Moura, P. Novais, L. Reis, Eds., Cham, Switzerland: Springer Nature, 2019, pp. 559-571. doi: 10.1007/978-3-030-30241-2_47en
Book TitleProgress in Artificial Intelligence. EPIA 2019en
Book SeriesLecture Notes in Computer Scienceen

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