SanDRA: 到達可能性解析を用いた自動運転車のための安全な大規模言語モデルベース意思決定
SanDRA: Safe Large-Language-Model-Based Decision Making for Automated Vehicles Using Reachability Analysis
LLMが生成した運転行動を時相論理式と到達可能性解析で検証し、安全で法的に準拠した意思決定を実現するフレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yuanfei Lin, Sebastian Illing, Matthias Althoff
分類: cs.RO
原文アブストラクト
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.
