# Global Feature Aggregation for Accident Anticipation

arXiv: https://arxiv.org/abs/2006.08942 · HTML: https://mlsanity.com/paper/2006.08942/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 17 of 17)

- [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)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Nexar Dashcam Collision Prediction Dataset and Challenge](https://mlsanity.com/paper/2503.03848/) (2025)
- [CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions](https://mlsanity.com/paper/2407.17757/) (2024)
- [World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving](https://mlsanity.com/paper/2507.12762/) (2025)
- [Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation](https://mlsanity.com/paper/2507.12755/) (2025)
- [Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction](https://mlsanity.com/paper/2310.04671/) (2023)
- [Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style Translation](https://mlsanity.com/paper/2301.02726/) (2023)
- [LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks](https://mlsanity.com/paper/2301.04794/) (2023)
- [NsBM-GAT: A Non-stationary Block Maximum and Graph Attention Framework for General Traffic Crash Risk Prediction](https://mlsanity.com/paper/2503.04018/) (2025)
- [MsFIN: Multi-scale Feature Interaction Network for Traffic Accident Anticipation](https://mlsanity.com/paper/2509.19227/) (2025)
- [ROAR: Robust Accident Recognition and Anticipation for Autonomous Driving](https://mlsanity.com/paper/2511.06226/) (2025)
- [RiskCueBench: Benchmarking Anticipatory Reasoning from Early Risk Cues in Video-Language Models](https://mlsanity.com/paper/2601.03369/) (2026)
- [SECURE: Stable Early Collision Understanding via Robust Embeddings in Autonomous Driving](https://mlsanity.com/paper/2604.01337/) (2026)
- [Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation](https://mlsanity.com/paper/2605.00051/) (2026)
- [Progressive Risk Estimation for Accident Anticipation](https://mlsanity.com/paper/2609.32811/) (2026)

## Abstract (arXiv)

Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggregation (FA) block that refines each object's features by computing a weighted sum of the features of all objects in a frame. We use FA block along with Long Short Term Memory (LSTM) network to anticipate accidents in the video sequences. We report mean Average Precision (mAP) and Average Time-to-Accident (ATTA) on Street Accident (SA) dataset. Our proposed method achieves the highest score for risk anticipation by predicting accidents 0.32 sec and 0.75 sec earlier compared to the best results with Adaptive Loss and dynamic parameter prediction based methods respectively.