用AI模拟交警判责，结合法律知识和多模态推理
AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models
- 通过多模态思维链提升事故责任推理解析能力
- 在67,941段视频上实现领先判责准确率
- 适合交通管理、自动驾驶安全研究者使用
多模态大语言模型在交通事故检测（TAD）与理解（TAU）方面已取得显著进展，但现有研究多聚焦于事故视频的描述与解释，缺乏深层次因果推理及法律知识融合。交通肇事责任分配（TARA）是一项更复杂的任务，需基于交通法规进行多步推理。为此，我们提出AITP（人工智能交警），一个用于责任推理与分配的多模态大语言模型。AITP通过多模态思维链（MCoT）机制增强推理能力，并利用检索增强生成（RAG）整合法律知识。我们还构建了DecaTARA——一个涵盖十项相关任务的十项全能基准，包含67,941个标注视频和195,821个问答对。大量实验表明，AITP在责任分配、TAD与TAU任务上均达到当前最优性能，为推理驱动的多模态交通分析树立新范式。
原文摘要 · Abstract (English)
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Traffic Accident Detection (TAD) and Traffic Accident Understanding (TAU). However, existing studies mainly focus on describing and interpreting accident videos, leaving room for deeper causal reasoning and integration of legal knowledge. Traffic Accident Responsibility Allocation (TARA) is a more challenging task that requires multi-step reasoning grounded in traffic regulations. To address this, we introduce AITP (Artificial Intelligence Traffic Police), a multimodal large language model for responsibility reasoning and allocation. AITP enhances reasoning via a Multimodal Chain-of-Thought (MCoT) mechanism and integrates legal knowledge through Retrieval-Augmented Generation (RAG). We further present DecaTARA, a decathlon-style benchmark unifying ten interrelated traffic accident reasoning tasks with 67,941 annotated videos and 195,821 question-answer pairs. Extensive experiments show that AITP achieves state-of-the-art performance across responsibility allocation, TAD, and TAU tasks, establishing a new paradigm for reasoning-driven multimodal traffic analysis.
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