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.

翻译：人类行为模型在自动车辆（AV）虚拟安全评估中既是行为基准，也是模拟人类智能体的关键工具，然而现有模型在可解释性与灵活性之间存在权衡。通用大语言模型（LLM）提供了一种富有前景的替代方案：单个模型无需参数拟合即可跨不同场景部署。然而，学界对LLM能否捕捉人类驾驶行为以及其局限性仍知之甚少。为填补这一空白，我们将两个通用LLM（OpenAI o3 和 Google Gemini 2.5 Pro）作为独立的闭环驾驶智能体，嵌入简化的单维并道场景中，并通过定量与定性分析将其行为与人类数据进行对比。两个模型均能复现人类间歇性操作控制行为以及对空间线索的战术依赖性，但均无法一致地捕捉人类对动态速度线索的响应，且模型之间的安全性能存在显著差异。系统性提示消融研究表明，提示组件作为模型特有的归纳偏置，其效果在不同LLM间不可迁移。这些发现表明，通用LLM或可成为AV评估流程中立即可用的独立人类行为模型，但需进一步研究以理解其失效模式并确保其作为人类驾驶行为模型的有效性。