TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

• URL: http://arxiv.org/abs/2606.01737v1
• Date: Mon, 01 Jun 2026 06:01:04 GMT
• Title: TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination
• Abstract summary: Existing methods often suffer from low efficiency, subjective judgment, and inconsistent analysis results.<n>This work presents TrafficRAG, a multimodal retrieval-augmented framework for automated traffic accident analysis and report generation.
• Score: 8.432460382640143
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation [16.24388752700607]
  We present TRIP-Evaluate, an open multimodal benchmark for large models in transportation.<n>The benchmark organizes 837 items using a role-task-knowledge taxonomy that covers vehicle, traffic-management, traveler, and planning-and-design functions.<n>Results on a diverse panel of models show that text-based performance is improving, but substantial weaknesses remain in multi-step engineering calculation, rule-constrained reasoning, multimodal scene understanding, and point-cloud understanding.
  arXiv  Detail & Related papers  (2026-04-29T04:29:48Z)
• AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models [3.6393796347315974]
  We introduce AITP (Artificial Intelligence Traffic Police), a multimodal large language model for responsibility reasoning and allocation.<n>We present DecaTARA, a decathlon-style benchmark unifying ten interrelated traffic accident reasoning tasks with 67,941 annotated videos and 195,821 question-answer pairs.
  arXiv  Detail & Related papers  (2026-04-11T09:38:45Z)
• Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking [47.47518672198846]
  Misinformation spreading over the Internet poses a significant threat to both societies and individuals.<n>Previous methods rely on semantic and social-contextual patterns learned from training data.<n>We propose WKGFC, which exploits authorized open knowledge graph as a core resource of evidence.
  arXiv  Detail & Related papers  (2026-02-27T19:29:01Z)
• Multimodal Fact-Level Attribution for Verifiable Reasoning [80.60864342985748]
  Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation.<n>Existing multimodal grounding benchmarks and evaluation methods fail to assess attribution in complex multimodal reasoning.<n>We introduce MuRGAt, a benchmark for evaluating fact-level multimodal attribution in settings that require reasoning beyond direct observation.
  arXiv  Detail & Related papers  (2026-02-12T03:10:02Z)
• MARA: A Multimodal Adaptive Retrieval-Augmented Framework for Document Question Answering [51.19392014547221]
  Retrieval-based multimodal document QA aims to identify and integrate relevant information from visually rich documents with complex multimodal structures.<n>Current approaches rely on query-agnostic document representations that overlook salient content.<n>We propose the Multimodal Adaptive Retrieval-Augmented (MARA) framework, which introduces query-adaptive generation.
  arXiv  Detail & Related papers  (2026-02-01T12:27:40Z)
• Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking [64.97768177044355]
  Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems.<n>We present FactArena, a fully automated arena-style evaluation framework.<n>Our analyses reveal significant discrepancies between static claim-verification accuracy and end-to-end fact-checking competence.
  arXiv  Detail & Related papers  (2026-01-06T02:51:56Z)
• Multimodal Fact-Checking: An Agent-based Approach [9.55806677152407]
  We introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking.<n> RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made.<n>Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow.
  arXiv  Detail & Related papers  (2025-12-28T13:58:33Z)
• Assessing the Performance Gap Between Lexical and Semantic Models for Information Retrieval With Formulaic Legal Language [1.3499500088995462]
  This study investigates the task of retrieving legal passages or paragraphs from decisions of the Court of Justice of the European Union (CJEU)<n>Our experiments show that BM25 is a strong baseline, surpassing off-the-shelf dense models in 4 out of 7 performance metrics.<n>Fine-tuning a dense model on domain-specific data led to improved performance, surpassing BM25 in most metrics.
  arXiv  Detail & Related papers  (2025-06-15T15:53:38Z)
• Evaluating Retrieval Augmented Generative Models for Document Queries in Transportation Safety [0.7373617024876725]
  This study evaluates the performance of three fine-tuned generative models, ChatGPT, Google's Vertex AI, and ORNL Retrieval Augmented Generation augmented LLaMA 2 and LLaMA.<n>We developed 100 realistic queries relevant to route planning and permitting requirements.<n>Results demonstrated that the RAG-augmented LLaMA models significantly outperformed Vertex AI and ChatGPT, providing more detailed and generally accurate information.
  arXiv  Detail & Related papers  (2025-04-09T16:37:03Z)
• Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
  We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports. We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes. Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
  arXiv  Detail & Related papers  (2024-06-16T03:10:16Z)
• From Chaos to Clarity: Claim Normalization to Empower Fact-Checking [57.024192702939736]
  Claim Normalization (aka ClaimNorm) aims to decompose complex and noisy social media posts into more straightforward and understandable forms. We propose CACN, a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures.
  arXiv  Detail & Related papers  (2023-10-22T16:07:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.