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dc.contributor.authorZhou, Mofan
dc.contributor.authorYu, Yang
dc.contributor.authorQu, Xiaobo
dc.date.accessioned2020-05-29T02:42:20Z
dc.date.available2020-05-29T02:42:20Z
dc.date.issued2020
dc.identifier.issn1524-9050
dc.identifier.doi10.1109/TITS.2019.2942014
dc.identifier.urihttp://hdl.handle.net/10072/394235
dc.description.abstractThe concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofpagefrom433
dc.relation.ispartofpageto443
dc.relation.ispartofissue1
dc.relation.ispartofjournalIEEE Transactions on Intelligent Transportation Systems
dc.relation.ispartofvolume21
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchCivil Engineering
dc.subject.fieldofresearchTransportation and Freight Services
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0905
dc.subject.fieldofresearchcode1507
dc.subject.keywordsScience & Technology
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsTransportation Science & Technology
dc.titleDevelopment of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationZhou, M; Yu, Y; Qu, X, Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach, IEEE Transactions on Intelligent Transportation Systems, 2020, 21 (1), pp. 433-443
dc.date.updated2020-05-29T02:41:03Z
gro.hasfulltextNo Full Text
gro.griffith.authorQu, Xiaobo


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