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.

翻译：交通事故检测与预测是一个长期存在的道路安全问题，研究者已为此付出大量努力。随着视频数据的快速增长，基于视觉的交通事故检测与预测（简称Vision-TAD与Vision-TAA）成为安全驾驶与监控安全领域的"最后一公里"问题。然而，交通事故的长尾分布、不均衡性、高度动态性、复杂性和不确定性，使得Vision-TAD与Vision-TAA呈现出分布外（OOD）特征。当前人工智能发展可能聚焦于这些重要但具有OOD特性的问题。针对Vision-TAD与Vision-TAA已经开展了哪些研究？未来应重点关注哪些方向？一份全面的综述至关重要。我们首次对深度学习时代的Vision-TAD进行了综述，并首次对Vision-TAA进行了全面总结。研究过程中，我们详细讨论了每种研究范式的优缺点。此外，我们还对31个公开基准数据集及相关评估指标进行了批判性评述。通过这篇综述，我们期望为Vision-TAD与Vision-TAA任务催生新见解并开拓可能的发展趋势。