Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation

• URL: http://arxiv.org/abs/2507.12755v1
• Date: Thu, 17 Jul 2025 03:16:28 GMT
• Title: Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation
• Authors: Yanchen Guan, Haicheng Liao, Chengyue Wang, Bonan Wang, Jiaxun Zhang, Jia Hu, Zhenning Li,
• Abstract summary: We propose an accident anticipation framework that integrates visual information from dashcam videos with structured textual data derived from accident reports.<n> Comprehensive evaluations conducted on benchmark datasets validate the superior predictive accuracy, enhanced responsiveness, reduced computational overhead, and improved interpretability of our approach.
• Score: 5.188064309021252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing precise and computationally efficient traffic accident anticipation system is crucial for contemporary autonomous driving technologies, enabling timely intervention and loss prevention. In this paper, we propose an accident anticipation framework employing a dual-branch architecture that effectively integrates visual information from dashcam videos with structured textual data derived from accident reports. Furthermore, we introduce a feature aggregation method that facilitates seamless integration of multimodal inputs through large models (GPT-4o, Long-CLIP), complemented by targeted prompt engineering strategies to produce actionable feedback and standardized accident archives. Comprehensive evaluations conducted on benchmark datasets (DAD, CCD, and A3D) validate the superior predictive accuracy, enhanced responsiveness, reduced computational overhead, and improved interpretability of our approach, thus establishing a new benchmark for state-of-the-art performance in traffic accident anticipation.

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