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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/69488AA7-A216-4C80-97E5-0C90B45B04D4 | - |
| Identifier | https://doi.org/10.1049/iet-its.2019.0249 | - |
| Identifier | https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-its.2019.0249 | - |
| Language | en | - |
| Extent | 12 pages | en |
| Title | Deep reinforcement-learning-based driving policy for autonomous road vehicles | en |
| Creator | Makantasis Konstantinos | en |
| Creator | Μακαντασης Κωνσταντινος | el |
| Creator | Kontorinaki Maria | en |
| Creator | Κοντορινακη Μαρια | el |
| Creator | Nikolos Ioannis | en |
| Creator | Νικολος Ιωαννης | el |
| Publisher | Institution of Engineering and Technology (IET) | en |
| Content Summary | In 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 Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-10-12 | - |
| Date of Publication | 2020 | - |
| Subject | Optimal control | en |
| Subject | Path planning | en |
| Subject | Traffic engineering computing | en |
| Subject | Road vehicles | en |
| Subject | Learning (artificial intelligence) | en |
| Subject | Road traffic | en |
| Subject | Dynamic programming | en |
| Subject | Driver information systems | en |
| Subject | Autonomous road vehicles | en |
| Subject | Autonomous vehicle | en |
| Subject | Optimal control methods | en |
| Subject | System dynamics | en |
| Subject | Driving policy | en |
| Subject | Driving scenarios | en |
| Subject | Autonomous driving vehicles | en |
| Subject | Manual driving vehicles | en |
| Subject | Mixed driving environments | en |
| Subject | Derived reinforcement learning policy | en |
| Subject | Optimal policy | en |
| Subject | Autonomous vehicles | en |
| Subject | Deep reinforcement-learning-based | en |
| Bibliographic Citation | K. 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.2019.0249 | en |
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