Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control
While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combine...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/12/585 |
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| _version_ | 1846102346212311040 |
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| author | Badr Ben Elallid Nabil Benamar Miloud Bagaa Sousso Kelouwani Nabil Mrani |
| author_facet | Badr Ben Elallid Nabil Benamar Miloud Bagaa Sousso Kelouwani Nabil Mrani |
| author_sort | Badr Ben Elallid |
| collection | DOAJ |
| description | While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms. |
| format | Article |
| id | doaj-art-5424823f31464c55b1c4bd47b9780ee8 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-5424823f31464c55b1c4bd47b9780ee82024-12-27T14:59:38ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-12-01151258510.3390/wevj15120585Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle ControlBadr Ben Elallid0Nabil Benamar1Miloud Bagaa2Sousso Kelouwani3Nabil Mrani4Computer Sciences at the School of Technology, Moulay Ismail University of Meknes, Meknes 50050, MoroccoComputer Sciences at the School of Technology, Moulay Ismail University of Meknes, Meknes 50050, MoroccoDepartment of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Québec City, QC G9A 5H7, CanadaDepartment of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Québec City, QC G9A 5H7, CanadaComputer Sciences at the School of Technology, Moulay Ismail University of Meknes, Meknes 50050, MoroccoWhile IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms.https://www.mdpi.com/2032-6653/15/12/585autonomous drivingdeep reinforcement learningvision transformerimitation learning |
| spellingShingle | Badr Ben Elallid Nabil Benamar Miloud Bagaa Sousso Kelouwani Nabil Mrani Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control World Electric Vehicle Journal autonomous driving deep reinforcement learning vision transformer imitation learning |
| title | Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control |
| title_full | Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control |
| title_fullStr | Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control |
| title_full_unstemmed | Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control |
| title_short | Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control |
| title_sort | improving reinforcement learning with expert demonstrations and vision transformers for autonomous vehicle control |
| topic | autonomous driving deep reinforcement learning vision transformer imitation learning |
| url | https://www.mdpi.com/2032-6653/15/12/585 |
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