Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909440861536256 |
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| author | Li, Benny Bao-Sheng Wu, Elena Yang, Hins Shao-Xuan Liang, Nicky Yao-Jin |
| author_facet | Li, Benny Bao-Sheng Wu, Elena Yang, Hins Shao-Xuan Liang, Nicky Yao-Jin |
| contents | Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16248 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed Li, Benny Bao-Sheng Wu, Elena Yang, Hins Shao-Xuan Liang, Nicky Yao-Jin Artificial Intelligence Robotics Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (RL), we propose a novel approach to optimize driving policies that enable the vehicle to achieve near-maximum speed without compromising on safety or route accuracy, even in low-speed scenarios. |
| title | Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2412.16248 |