# Vision-Based Traffic Accident Detection and Anticipation: A Survey

**Authors:** Jianwu Fang, iahuan Qiao, Jianru Xue, Zhengguo Li  
**arXiv:** [2308.15985](https://arxiv.org/abs/2308.15985)  
**Date:** 2023-08-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.15985v1/

## Key points

- Traffic accident detection and anticipation is a persistent problem in road safety
- Vision-based Traffic Accident Detection and Anticipation (Vision-TAD and Vision-TAA) are crucial for safe driving and surveillance safety
- Challenges include long-tailed distribution, unbalanced characteristics, high dynamics, complexity, and uncertainty
- Researchers have developed artificial intelligence (AI) models to address these challenges
- This survey paper provides an overview of advancements in deep learning era for Vision-TAD and Vision-TAA
- It is the first survey specifically focusing on Vision-TAA
- The authors discuss research prototypes in detail, highlighting strengths and weaknesses
- They conduct a critical review of 31 publicly available benchmarks and evaluation metrics used in this domain
- The paper aims to offer new insights into the current state of research while identifying potential future directions

## Summary

In the field of road safety, traffic accident detection and anticipation is a persistent problem that requires significant attention. With the increasing availability of video data, Vision-based Traffic Accident Detection and Anticipation (Vision-TAD and Vision-TAA) have emerged as crucial components for ensuring safe driving and surveillance safety. However, these tasks face numerous challenges due to the complex nature of traffic accidents, including their long-tailed distribution, unbalanced characteristics, high dynamics, complexity, and uncertainty. These factors contribute to the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. To address these challenges, researchers have dedicated considerable efforts to developing artificial intelligence (AI) models for Vision-TAD and Vision-TAA. This survey paper aims to provide a comprehensive overview of the advancements made in this area during the deep learning era. It also serves as the first ever survey specifically focusing on Vision-TAA. The authors discuss various research prototypes in detail while highlighting their strengths and weaknesses. Additionally they conduct a critical review of 31 publicly available benchmarks and related evaluation metrics used in this domain. By presenting this survey paper the authors aim to offer new insights into the current state of Vision TAD and TAA research while identifying potential future directions for addressing these important problems with an ultimate goal to foster innovation in this field and pave way for new trends in tackling traffic accident detection and anticipation tasks.