A framework for self-enforced optimal interaction between connected vehicles

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dc.contributor.authorStryszowski, Marcin
dc.contributor.authorLongo, Stefano
dc.contributor.authorD'Alessandro, Dario
dc.contributor.authorVelenis, Efstathios
dc.contributor.authorForostovsky, Gregory
dc.contributor.authorManfredi, Sabato
dc.date.accessioned2020-11-12T15:48:33Z
dc.date.available2020-11-12T15:48:33Z
dc.date.freetoread2020-11-12
dc.date.issued2020-05-06
dc.description.abstractThis paper proposes a decision-making framework for Connected Autonomous Vehicle interactions. It provides and justifies algorithms for strategic selection of control references for cruising, platooning and overtaking. The algorithm is based on the trade-off between energy consumption and time. The consequent cooperation opportunities originating from agent heterogeneity are captured by a game-theoretic cooperative-competitive solution concept to provide a computationally feasible, self-enforced, cooperative traffic management framework.en_UK
dc.identifier.citationStryszowski S, Longo S, D'Alessandro D, et al., (2020) A framework for self-enforced optimal interaction between connected vehicles. IEEE Transactions on Intelligent Transportation Systems, Volume 22, Number 10, October 2021, pp. 6152-6161en_UK
dc.identifier.cris28788447
dc.identifier.issn1524-9050
dc.identifier.urihttps://doi.org/10.1109/TITS.2020.2988150
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15995
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectConnected carsen_UK
dc.subjectgame theory platooningen_UK
dc.subjectnegotiationen_UK
dc.subjectovertakeen_UK
dc.subjectV2Ven_UK
dc.titleA framework for self-enforced optimal interaction between connected vehiclesen_UK
dc.typeArticleen_UK

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