SanDRA: Safe Large-Language-Model-Based Decision Making for Automated Vehicles Using Reachability Analysis
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916996098031616 |
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| author | Lin, Yuanfei Illing, Sebastian Althoff, Matthias |
| author_facet | Lin, Yuanfei Illing, Sebastian Althoff, Matthias |
| contents | Large language models have been widely applied to knowledge-driven decision-making for automated vehicles due to their strong generalization and reasoning capabilities. However, the safety of the resulting decisions cannot be ensured due to possible hallucinations and the lack of integrated vehicle dynamics. To address this issue, we propose SanDRA, the first safe large-language-model-based decision making framework for automated vehicles using reachability analysis. Our approach starts with a comprehensive description of the driving scenario to prompt large language models to generate and rank feasible driving actions. These actions are translated into temporal logic formulas that incorporate formalized traffic rules, and are subsequently integrated into reachability analysis to eliminate unsafe actions. We validate our approach in both open-loop and closed-loop driving environments using off-the-shelf and finetuned large language models, showing that it can provide provably safe and, where possible, legally compliant driving actions, even under high-density traffic conditions. To ensure transparency and facilitate future research, all code and experimental setups are publicly available at github.com/CommonRoad/SanDRA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SanDRA: Safe Large-Language-Model-Based Decision Making for Automated Vehicles Using Reachability Analysis Lin, Yuanfei Illing, Sebastian Althoff, Matthias Robotics Large language models have been widely applied to knowledge-driven decision-making for automated vehicles due to their strong generalization and reasoning capabilities. However, the safety of the resulting decisions cannot be ensured due to possible hallucinations and the lack of integrated vehicle dynamics. To address this issue, we propose SanDRA, the first safe large-language-model-based decision making framework for automated vehicles using reachability analysis. Our approach starts with a comprehensive description of the driving scenario to prompt large language models to generate and rank feasible driving actions. These actions are translated into temporal logic formulas that incorporate formalized traffic rules, and are subsequently integrated into reachability analysis to eliminate unsafe actions. We validate our approach in both open-loop and closed-loop driving environments using off-the-shelf and finetuned large language models, showing that it can provide provably safe and, where possible, legally compliant driving actions, even under high-density traffic conditions. To ensure transparency and facilitate future research, all code and experimental setups are publicly available at github.com/CommonRoad/SanDRA. |
| title | SanDRA: Safe Large-Language-Model-Based Decision Making for Automated Vehicles Using Reachability Analysis |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.06717 |