🤖 AI Summary
This study addresses the challenges of low efficiency, high subjectivity, and inconsistent outcomes in traffic accident liability determination, which are exacerbated by the limitations of existing large language models in handling video noise and insufficient legal knowledge. To overcome these issues, the authors propose TrafficRAG, a novel framework that introduces multimodal retrieval-augmented generation to this domain for the first time. TrafficRAG leverages a vision-language model to produce structured accident descriptions, retrieves relevant legal statutes and precedents through a hybrid approach combining BM25 and dense embeddings, and synthesizes multimodal evidence with legal knowledge via a large language model to generate standardized liability analysis reports. Experimental results demonstrate that the proposed method achieves 77.32% accuracy in legal applicability, 81.71% factual faithfulness, and a mean absolute error of 5.48% in liability proportion estimation, significantly outperforming baseline models.
📝 Abstract
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.