Accident prediction and timely preventive actions improve road safety by reducing the risk of injury to road users and minimizing property damage. Hence, they are critical components of advanced driver assistance systems (ADAS) and autonomous vehicles. While many existing systems depend on multiple sensors such as LiDAR, radar, and GPS, relying solely on dash-cam videos presents a more challenging, yet more cost-effective and easily deployable solution. In this work, we incorporate improved spatio-temporal features and aggregate them through a recurrent network to enhance state-of-the-art graph neural networks for predicting accidents from dash-cam videos. Experiments using three publicly available datasets (DAD, DoTA and DADA) show that our proposed STAGNet model achieves higher average precision and mean time-to-accident scores than previous methods, both when cross-validated on a given dataset and when trained and tested on different datasets.
@article{arxiv.2508.15216,
title = {STAGNet: A Spatio-Temporal Graph and LSTM Framework for Accident Anticipation},
author = {Vipooshan Vipulananthan and Kumudu Mohottala and Kavindu Chinthana and Nimsara Paramulla and Charith D Chitraranjan},
journal= {arXiv preprint arXiv:2508.15216},
year = {2025}
}