TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

TrafficRAG presents a multimodal retrieval-augmented generation framework that automates traffic accident liability analysis by combining vision-language models, hybrid legal document retrieval, and large language models to generate standardized liability reports. The system achieves 77.32% legal norm accuracy and demonstrates that integrating multimodal evidence with legal knowledge significantly improves accident analysis reliability.

TrafficRAG addresses a significant gap in intelligent transportation and legal automation by tackling the subjective and inefficient nature of manual traffic accident liability determination. Current approaches rely heavily on human judgment, leading to inconsistent outcomes and delayed legal proceedings. This research bridges that gap through a technically sophisticated pipeline that transforms raw accident data into legally grounded analysis.

The framework's design reflects broader trends in AI development toward domain-specific applications. Rather than relying solely on general-purpose large language models, the authors implement a hybrid approach combining sparse and dense retrieval methods to access relevant traffic regulations and historical precedents. This retrieval-augmented strategy directly addresses a known weakness of LLMs: hallucination and lack of specialized legal knowledge. By grounding the model's reasoning in actual traffic codes and case law, the system produces more defensible and consistent outcomes.

The practical implications extend beyond traffic authorities to insurance companies, legal firms, and autonomous vehicle developers. Automated liability determination could accelerate claim processing, reduce litigation costs, and provide objective decision-making frameworks for emerging autonomous vehicle incidents. Insurance companies particularly benefit from faster claims resolution and reduced human bias in liability assessment.

The reported performance metrics—77.32% legal norm adaptation accuracy and 5.48% liability ratio mean absolute error—indicate substantial progress, though not yet approaching perfect accuracy. Future applications may require human verification for edge cases, but the system establishes a reliable foundation for semi-automated processing. Development of similar domain-specific RAG systems across legal, medical, and regulatory domains suggests this represents a maturing pattern in applied AI deployment.

• →TrafficRAG combines vision-language models with hybrid retrieval and LLMs to automate traffic accident liability analysis with 77.32% legal accuracy.
• →The framework addresses LLM limitations through retrieval augmentation, grounding legal reasoning in actual traffic codes and historical cases rather than relying on model hallucination.
• →Insurance companies and legal firms could significantly accelerate claim processing and reduce litigation costs through automated, objective liability determination.
• →Achieved 81.71% factual faithfulness and 5.48% liability ratio error, demonstrating practical viability though suggesting need for human verification on complex cases.
• →The approach establishes a replicable pattern for domain-specific AI applications beyond traffic analysis to legal, medical, and regulatory automation.