DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916351922143232 |
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| author | Lin, Yuanfei Li, Chenran Ding, Mingyu Tomizuka, Masayoshi Zhan, Wei Althoff, Matthias |
| author_facet | Lin, Yuanfei Li, Chenran Ding, Mingyu Tomizuka, Masayoshi Zhan, Wei Althoff, Matthias |
| contents | Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we present DrPlanner, the first framework designed to automatically diagnose and repair motion planners using large language models. Initially, we generate a structured description of the planner and its planned trajectories from both natural and programming languages. Leveraging the profound capabilities of large language models, our framework returns repaired planners with detailed diagnostic descriptions. Furthermore, our framework advances iteratively with continuous feedback from the evaluation of the repaired outcomes. Our approach is validated using both search- and sampling-based motion planners for automated vehicles; experimental results highlight the need for demonstrations in the prompt and show the ability of our framework to effectively identify and rectify elusive issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models Lin, Yuanfei Li, Chenran Ding, Mingyu Tomizuka, Masayoshi Zhan, Wei Althoff, Matthias Robotics Programming Languages Motion planners are essential for the safe operation of automated vehicles across various scenarios. However, no motion planning algorithm has achieved perfection in the literature, and improving its performance is often time-consuming and labor-intensive. To tackle the aforementioned issues, we present DrPlanner, the first framework designed to automatically diagnose and repair motion planners using large language models. Initially, we generate a structured description of the planner and its planned trajectories from both natural and programming languages. Leveraging the profound capabilities of large language models, our framework returns repaired planners with detailed diagnostic descriptions. Furthermore, our framework advances iteratively with continuous feedback from the evaluation of the repaired outcomes. Our approach is validated using both search- and sampling-based motion planners for automated vehicles; experimental results highlight the need for demonstrations in the prompt and show the ability of our framework to effectively identify and rectify elusive issues. |
| title | DrPlanner: Diagnosis and Repair of Motion Planners for Automated Vehicles Using Large Language Models |
| topic | Robotics Programming Languages |
| url | https://arxiv.org/abs/2403.07470 |