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

TL;DR

This comprehensive review analyzes recent deep learning methods for vision-based traffic accident anticipation, highlighting current approaches, challenges, and future research directions to improve road safety.

Contribution

The paper systematically categorizes existing deep learning techniques for Vision-TAA and identifies key challenges and promising future research avenues.

Findings

Deep learning models show promise but face data scarcity issues.

Current methods struggle with generalization to complex scenarios.

Integration of multi modal data and Transformer architectures offers future potential.

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, spatio-temporal feature-based prediction, scene understanding, and multi modal 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 multi modal 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.