AITP: Traffic Accident Responsibility Allocation via Multimodal Large Language Models

TL;DR

This paper introduces AITP, a multimodal large language model designed for traffic accident responsibility allocation, leveraging reasoning mechanisms and legal knowledge integration, and presents a comprehensive benchmark for traffic accident reasoning tasks.

Contribution

The paper presents AITP, a novel multimodal LLM with reasoning and legal knowledge integration, and introduces DecaTARA, a large benchmark for traffic accident reasoning tasks.

Findings

AITP achieves state-of-the-art performance in responsibility allocation.

DecaTARA includes 67,941 videos and 195,821 QA pairs for comprehensive evaluation.

The approach establishes a new paradigm for reasoning-driven multimodal traffic analysis.

Abstract

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.