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

arXiv: https://arxiv.org/abs/2505.07611 · HTML: https://mlsanity.com/paper/2505.07611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 68 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 21 of 21)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [DRAMA: Joint Risk Localization and Captioning in Driving](https://mlsanity.com/paper/2209.10767/) (2022)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events](https://mlsanity.com/paper/2103.15538/) (2021)
- [DADA: Driver Attention Prediction in Driving Accident Scenarios](https://mlsanity.com/paper/1912.12148/) (2019)
- [DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark](https://mlsanity.com/paper/1904.12634/) (2019)
- [Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB](https://mlsanity.com/paper/1804.02675/) (2018)
- [DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation](https://mlsanity.com/paper/2107.10189/) (2021)
- [A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents](https://mlsanity.com/paper/2106.10197/) (2021)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization](https://mlsanity.com/paper/1705.06560/) (2017)
- [RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework](https://mlsanity.com/paper/2003.00819/) (2020)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction](https://mlsanity.com/paper/2111.06123/) (2021)
- [Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark](https://mlsanity.com/paper/2212.09381/) (2022)
- [COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos](https://mlsanity.com/paper/2101.08463/) (2021)
- [Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques](https://mlsanity.com/paper/2406.13968/) (2024)
- [When, Where, and What? A Novel Benchmark for Accident Anticipation and Localization with Large Language Models](https://mlsanity.com/paper/2407.16277/) (2024)

## Built on by (most cited first, 1 of 1)

- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)

## Abstract (arXiv)

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.