General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging
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 ...
| Authors | Samir H. A. Mohammad, Wouter Mooi, Arkady Zgonnikov |
|---|---|
| Primary category | cs.AI |
| Published | 11 March 2026 |
| Last updated at source | 18 May 2026 |
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