Understanding and adhering to traffic regulations is essential for autonomous vehicles to ensure safety and trustworthiness. However, traffic regulations are complex, context-dependent, and differ between regions, posing a major challenge to conventional rule-based decision-making approaches. We present an interpretable, regulation-aware decision-making framework, DriveReg, which enables autonomous vehicles to understand and adhere to region-specific traffic laws and safety guidelines. The framework integrates a Retrieval-Augmented Generation (RAG)-based Traffic Regulation Retrieval Agent, which retrieves relevant rules from regulatory documents based on the current situation, and a Large Language Model (LLM)-powered Reasoning Agent that evaluates actions for legal compliance and safety. Our design emphasizes interpretability to enhance transparency and trustworthiness. To support systematic evaluation, we introduce the DriveReg Scenarios Dataset, a comprehensive dataset of driving scenarios across Boston, Singapore, and Los Angeles, with both hypothesized text-based cases and real-world driving data, constructed and annotated to evaluate models' capacity for regulation understanding and reasoning. We validate our framework on the DriveReg Scenarios Dataset and real-world deployment, demonstrating strong performance and robustness across diverse environments.

翻译：理解并遵守交通规则对于自动驾驶车辆确保安全性与可信度至关重要。然而，交通规则具有复杂性、情境依赖性及地域差异性，这对传统的基于规则的决策方法构成了重大挑战。本文提出了一种可解释、具备规则感知能力的决策框架DriveReg，使自动驾驶车辆能够理解并遵守特定区域的交通法规与安全准则。该框架集成了基于检索增强生成（RAG）的交通规则检索代理，可根据当前情境从法规文件中检索相关规则，以及由大语言模型（LLM）驱动的推理代理，用于评估行动是否符合法律规范与安全性要求。我们的设计强调可解释性，以提升透明度和可信度。为支持系统化评估，我们构建了DriveReg场景数据集——一个涵盖波士顿、新加坡和洛杉矶的综合性驾驶场景数据集，包含假设性文本案例与真实驾驶数据，经标注用于评估模型对交通规则的理解与推理能力。我们在DriveReg场景数据集及实际部署场景中验证了该框架，结果表明其在多样化环境中具有优异的性能与鲁棒性。