汎用大規模言語モデルによる人間ドライバー行動のモデル化：簡略化合流シナリオの事例
General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging
自動運転車の安全性評価に使える人間行動モデルとして、汎用LLM（OpenAI o3とGemini 2.5 Pro）を簡略化した合流シナリオでエージェント化し、人間データと比較。人間らしい間欠操作や空間的手がかりへの依存は再現するが、速度変化への応答や安全性はモデル間で乖離し、プロンプトの影響もモデル特異的であることを示した。
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著者: Samir H. A. Mohammad, Wouter Mooi, Arkady Zgonnikov
分類: cs.AI, cs.RO
原文アブストラクト
Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet current models face a trade-off between interpretability and flexibility. General-purpose large language models (LLMs) offer a promising alternative: a single model potentially deployable without parameter fitting across diverse scenarios. However, what LLMs can and cannot capture about human driving behavior remains poorly understood. We address this gap by embedding two general-purpose LLMs (OpenAI o3 and Google Gemini 2.5 Pro) as standalone, closed-loop driver agents in a simplified one-dimensional merging scenario and comparing their behavior against human data using quantitative and qualitative analyses. Both models reproduce human-like intermittent operational control and tactical dependencies on spatial cues. However, neither consistently captures the human response to dynamic velocity cues, and safety performance diverges sharply between models. A systematic prompt ablation study reveals that prompt components act as model-specific inductive biases that do not transfer across LLMs. These findings suggest that general-purpose LLMs could potentially serve as standalone, ready-to-use human behavior models in AV evaluation pipelines, but future research is needed to better understand their failure modes and ensure their validity as models of human driving behavior.
