# TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

arXiv: https://arxiv.org/abs/2606.01737 · HTML: https://mlsanity.com/paper/2606.01737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 21 of 21)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis](https://mlsanity.com/paper/2501.10604/) (2025)
- [VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding](https://mlsanity.com/paper/2507.09815/) (2025)
- [JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System](https://mlsanity.com/paper/2503.14258/) (2025)
- [TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning](https://mlsanity.com/paper/2404.09275/) (2024)
- [GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events](https://mlsanity.com/paper/2402.02205/) (2024)
- [Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference](https://mlsanity.com/paper/2408.04652/) (2024)
- [AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis](https://mlsanity.com/paper/2401.03040/) (2024)
- [Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses](https://mlsanity.com/paper/2406.10789/) (2024)
- [Benchmarking Multi-Step Legal Reasoning and Analyzing Chain-of-Thought Effects in Large Language Models](https://mlsanity.com/paper/2511.07979/) (2025)
- [Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors](https://mlsanity.com/paper/2505.09949/) (2025)
- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [CADD: A Chinese Traffic Accident Dataset for Statute-Based Liability Attribution](https://mlsanity.com/paper/2511.11715/) (2025)
- [Surveillance Video-Based Traffic Accident Detection Using Transformer Architecture](https://mlsanity.com/paper/2512.11350/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Ontology-Guided Multi-Anchor Graph Retrieval Framework for Traffic Legal Liability Determination](https://mlsanity.com/paper/2606.11910/) (2026)

## Abstract (arXiv)

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.