Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929354034905088 |
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| author | Xu, Yuejiao Wang, Ruolin Xu, Chengpeng Ji, Jianmin |
| author_facet | Xu, Yuejiao Wang, Ruolin Xu, Chengpeng Ji, Jianmin |
| contents | Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration into autonomous driving trajectory planning frameworks, specifically tailored for highway scenarios. The LGL guides the trajectory planning with a local target area determined through scenario reasoning, scenario evaluation, and guidance area calculation. Integrating the Responsibility-Sensitive Safety (RSS) model, the LGL ensures formal safety guarantees while accommodating various user preferences defined by logical formulae. Experimental validation demonstrates the effectiveness of the LGL in achieving a balance between safety and efficiency, and meeting user preferences in autonomous highway driving scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13704 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving Xu, Yuejiao Wang, Ruolin Xu, Chengpeng Ji, Jianmin Robotics Logic in Computer Science Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration into autonomous driving trajectory planning frameworks, specifically tailored for highway scenarios. The LGL guides the trajectory planning with a local target area determined through scenario reasoning, scenario evaluation, and guidance area calculation. Integrating the Responsibility-Sensitive Safety (RSS) model, the LGL ensures formal safety guarantees while accommodating various user preferences defined by logical formulae. Experimental validation demonstrates the effectiveness of the LGL in achieving a balance between safety and efficiency, and meeting user preferences in autonomous highway driving scenarios. |
| title | Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving |
| topic | Robotics Logic in Computer Science |
| url | https://arxiv.org/abs/2405.13704 |