用视频预测事故，比现有方法更准更快。
STAGNet: A Spatio-Temporal Graph and LSTM Framework for Accident Anticipation
- 结合时空图与LSTM，从行车视频中提取事故前兆特征
- 在三个数据集上平均精度和距事故发生时间得分均超越旧方法
- 适合自动驾驶与智能车载系统开发者参考
事故预测与及时预警能有效降低道路使用者受伤风险和财产损失，是高级驾驶辅助系统（ADAS）和自动驾驶车辆的关键组成部分。尽管许多现有系统依赖激光雷达、雷达和GPS等多传感器，但仅使用行车记录仪视频的方案更具成本优势且易于部署，挑战也更大。本文提出STAGNet框架，通过改进时空特征并利用循环网络聚合，增强现有图神经网络在行车视频中预测事故的能力。在三个公开数据集（DAD、DoTA、DADA）上的实验表明，无论是在单个数据集内交叉验证，还是跨数据集训练测试，STAGNet均取得更高的平均精度和平均距事故时间得分，优于以往方法。
原文摘要 · Abstract (English)
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.
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