Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions

• URL: http://arxiv.org/abs/2505.07611v1
• Date: Mon, 12 May 2025 14:34:22 GMT
• Title: Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions
• Authors: Yi Zhang, Wenye Zhou, Ruonan Lin, Xin Yang, Hao Zheng,
• Abstract summary: Vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.<n>This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid learning models for accident prediction.
• Score: 10.3325464784641
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid deep learning models for accident prediction,alongside the use of real-world and synthetic datasets.Current methodologies are categorized into four key approaches: image and video feature-based prediction, spatiotemporal feature-based prediction, scene understanding,and multimodal data fusion.While these methods demonstrate significant potential,challenges such as data scarcity,limited generalization to complex scenarios,and real-time performance constraints remain prevalent. This review highlights opportunities for future research,including the integration of multimodal data fusion, self-supervised learning,and Transformer-based architectures to enhance prediction accuracy and scalability.By synthesizing existing advancements and identifying critical gaps, this paper provides a foundational reference for developing robust and adaptive Vision-TAA systems,contributing to road safety and traffic management.

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