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Deep reinforcement-learning-based driving policy for autonomous road vehicles

Makantasis Konstantinos, Kontorinaki Maria, Nikolos Ioannis

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URIhttp://purl.tuc.gr/dl/dias/69488AA7-A216-4C80-97E5-0C90B45B04D4-
Identifierhttps://doi.org/10.1049/iet-its.2019.0249-
Identifierhttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-its.2019.0249-
Languageen-
Extent12 pagesen
TitleDeep reinforcement-learning-based driving policy for autonomous road vehiclesen
CreatorMakantasis Konstantinosen
CreatorΜακαντασης Κωνσταντινοςel
CreatorKontorinaki Mariaen
CreatorΚοντορινακη Μαριαel
CreatorNikolos Ioannisen
CreatorΝικολος Ιωαννηςel
PublisherInstitution of Engineering and Technology (IET)en
Content SummaryIn this work, the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of the authors’ knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles’ behaviour on the overall traffic flow are presented.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2021-10-12-
Date of Publication2020-
SubjectOptimal controlen
SubjectPath planningen
SubjectTraffic engineering computingen
SubjectRoad vehiclesen
SubjectLearning (artificial intelligence)en
SubjectRoad trafficen
SubjectDynamic programmingen
SubjectDriver information systemsen
SubjectAutonomous road vehiclesen
SubjectAutonomous vehicleen
SubjectOptimal control methodsen
SubjectSystem dynamicsen
SubjectDriving policyen
SubjectDriving scenariosen
SubjectAutonomous driving vehiclesen
SubjectManual driving vehiclesen
SubjectMixed driving environmentsen
SubjectDerived reinforcement learning policyen
SubjectOptimal policyen
SubjectAutonomous vehiclesen
SubjectDeep reinforcement-learning-baseden
Bibliographic CitationK. Makantasis, M. Kontorinaki, and I. Nikolos, “Deep reinforcement‐learning‐based driving policy for autonomous road vehicles,” IET Intell. Transp. Syst., vol. 14, no. 1, pp. 13–24, Jan. 2020. doi: 10.1049/iet-its.2019.0249en

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