TrafficRAG：用于交通事故责任认定的多模态RAG框架
TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination
AI总结：
提出TrafficRAG框架，通过视觉语言模型生成结构化描述、混合检索获取法规和案例、大语言模型融合多模态证据进行推理，实现自动化交通事故责任分析报告生成。
摘要：
交通事故责任分析是智能交通和法律辅助中一项关键但具有挑战性的任务。现有方法通常存在效率低、主观判断和不一致的分析结果等问题。同时，大语言模型受到噪声视频输入和法律领域知识不足的限制。为了解决这些问题，本文提出了TrafficRAG，一个用于自动化交通事故分析和报告生成的多模态检索增强框架。具体来说，该框架首先采用视觉语言模型生成事故场景的结构化文本描述，作为准确的检索查询。基于这些文本查询，采用结合BM25稀疏检索和稠密嵌入检索的混合检索策略来获取相关交通法规和类似历史案例。最后，大语言模型整合检索到的法律知识和多模态事故证据进行综合推理，生成标准化、有法律依据的责任分析报告。大量实验表明，TrafficRAG始终优于基线方法，实现了77.32%的法律规范适配准确率、81.71%的事实忠实度以及5.48%的责任比例平均绝对误差。结果验证了通过检索增强将多模态事实证据与法律条款相结合，可以有效提高交通事故责任认定的可靠性和准确性。
英文摘要：
Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance. Existing methods often suffer from low efficiency, subjective judgment, and inconsistent analysis results. Meanwhile, large language models are constrained by noisy video inputs and insufficient legal domain knowledge. To address these issues, this work presents TrafficRAG, a multimodal retrieval-augmented framework for automated traffic accident analysis and report generation. Specifically, the proposed framework first adopts a vision-language model to produce structured textual descriptions of accident scenarios, which serve as accurate retrieval queries. Based on these textual queries, a hybrid retrieval strategy integrating BM25 sparse retrieval and dense embedding retrieval is employed to fetch relevant traffic regulations and similar historical cases. Finally, the large language model incorporates retrieved legal knowledge and multimodal accident evidence for comprehensive reasoning, and generates standardized, legally grounded liability analysis reports. Extensive experiments show that TrafficRAG consistently outperforms baseline methods, achieving 77.32% Legal Norm Adaptation Accuracy, 81.71% Factual Faithfulness, and a Liability Ratio MAE of 5.48%. The results validate that integrating multimodal factual evidence with legal clauses via retrieval augmentation can effectively improve the reliability and accuracy of traffic accident liability determination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
