用可达性分析让大模型决策自动驾驶更安全
SanDRA: Safe Large-Language-Model-Based Decision Making for Automated Vehicles Using Reachability Analysis
- 用场景描述引导大模型生成驾驶动作并排序
- 将交通规则转为逻辑公式，结合可达性分析剔除危险动作
- 在高密度车流下仍能保证决策安全合规，适合自动驾驶安全研究
大语言模型（LLMs）因强大的泛化与推理能力被广泛用于自动驾驶的知识驱动决策，但其决策安全性难以保障，主要因可能产生幻觉且缺乏车辆动力学整合。为此，我们提出SanDRA——首个基于可达性分析的、面向自动驾驶的安全大模型决策框架。该方法首先通过全面描述驾驶场景，引导LLMs生成并排序可行驾驶动作；这些动作被转化为包含形式化交通规则的时序逻辑公式，并集成至可达性分析中以消除不安全动作。我们在开环与闭环驾驶环境中，使用现成及微调后的LLMs验证了该方法，结果表明其能在高密度交通条件下提供可证明安全且尽可能合法的驾驶动作。为确保透明性并促进后续研究，所有代码与实验配置已公开于github.com/CommonRoad/SanDRA。
原文摘要 · Abstract (English)
Large language models (LLMs) 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 LLMs 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 LLMs, 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.
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