Traffic accident detection and anticipation is an obstinate road safety
problem and painstaking efforts have been devoted. With the rapid growth of
video data, Vision-based Traffic Accident Detection and Anticipation (named
Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving
and surveillance safety. However, the long-tailed, unbalanced, highly dynamic,
complex, and uncertain properties of traffic accidents form the
Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI
development may focus on these OOD but important problems. What has been done
for Vision-TAD and Vision-TAA? What direction we should focus on in the future
for this problem? A comprehensive survey is important. We present the first
survey on Vision-TAD in the deep learning era and the first-ever survey for
Vision-TAA. The pros and cons of each research prototype are discussed in
detail during the investigation. In addition, we also provide a critical review
of 31 publicly available benchmarks and related evaluation metrics. Through
this survey, we want to spawn new insights and open possible trends for
Vision-TAD and Vision-TAA tasks.

交通事故检测与预测是一项棘手的交通安全问题，本文提出了一项对深度学习时代中视觉交通事故检测（Vision-TAD）和预测（Vision-TAA）的全面调查，探讨了每个研究样本的优劣，并提供了 31 个公开可用的基准和相关评估指标的关键评论，旨在为 Vision-TAD 和 Vision-TAA 任务带来新的见解和可能的趋势。