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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.03935 |
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| _version_ | 1866929502813159424 |
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| author | Wang, Kui She, Changyang Li, Zongdian Yu, Tao Li, Yonghui Sakaguchi, Kei |
| author_facet | Wang, Kui She, Changyang Li, Zongdian Yu, Tao Li, Yonghui Sakaguchi, Kei |
| contents | Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering system aimed at enhancing road safety and traveling efficiency at intersections for CAVs. We utilize RSUs for real-time traffic data acquisition and train an offline reinforcement learning (RL) algorithm based on human driving data. Evaluation results obtained from hardware-in-loop autonomous driving simulations show that our approach employing the twin delayed deep deterministic policy gradient and behavior cloning (TD3+BC), achieves performance comparable to state-of-the-art autonomous driving systems in terms of safety measures while significantly enhancing travel efficiency by up to 17.38% in intersection areas. This paper makes a pivotal contribution to the field of intelligent transportation systems, presenting a breakthrough solution for improving urban traffic flow and safety at intersections. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03935 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach Wang, Kui She, Changyang Li, Zongdian Yu, Tao Li, Yonghui Sakaguchi, Kei Systems and Control Traffic intersections present significant challenges for the safe and efficient maneuvering of connected and automated vehicles (CAVs). This research proposes an innovative roadside unit (RSU)-assisted cooperative maneuvering system aimed at enhancing road safety and traveling efficiency at intersections for CAVs. We utilize RSUs for real-time traffic data acquisition and train an offline reinforcement learning (RL) algorithm based on human driving data. Evaluation results obtained from hardware-in-loop autonomous driving simulations show that our approach employing the twin delayed deep deterministic policy gradient and behavior cloning (TD3+BC), achieves performance comparable to state-of-the-art autonomous driving systems in terms of safety measures while significantly enhancing travel efficiency by up to 17.38% in intersection areas. This paper makes a pivotal contribution to the field of intelligent transportation systems, presenting a breakthrough solution for improving urban traffic flow and safety at intersections. |
| title | Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.03935 |