用大模型模拟人类驾驶行为，让自动驾驶测试更真实。
LLM-based Human-like Traffic Simulation for Self-driving Tests
- 构建分层驾驶风格模型，融合认知理论与大模型生成多样行为
- 通过感知引导行为策略，使车辆反应更贴近真人驾驶
- 提升自动驾驶系统缺陷检测率68%，适合安全验证场景
确保交通动态的真实性是自动驾驶系统在部署前进行仿真评估的前提。由于大多数道路使用者是人类驾驶员，还原其多样化行为对仿真平台至关重要。然而，现有方法通常依赖手工设计规则或单一数据驱动模型，仅能捕捉真实驾驶行为的局部特征，且风格多样性与可解释性有限。为此，我们提出HDSim——一种基于大语言模型（LLM）与认知理论融合的高保真交通生成框架，可在仿真平台中生成可扩展、真实的交通场景。该框架从两方面推进当前技术：(i) 引入分层驾驶员模型以表征多样驾驶风格特征；(ii) 设计感知引导的行为影响机制，由大模型指导感知模块间接塑造驾驶员行为。实验表明，将HDSim集成至仿真平台后，对自动驾驶系统中安全关键缺陷的检测能力提升最高达68%，并实现事故解释的一致性与真实性。
原文摘要 · Abstract (English)
Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.
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