用零样本方法预测行车事故，仅靠公开数据就能泛化到新场景。
A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation
- 用VideoMAE-v2+滑动窗口，从粗标签数据中学习帧级风险
- 在CVPR@AUTOPILOT竞赛中获第二名，零样本下表现优异
- 适合无标注数据的交通安全部署场景，实用性强
行车事故预测——在每帧行车记录仪视频中预判碰撞可能性——是关乎安全但难以扩展的任务，因在每个部署场景收集领域内标注事故数据成本过高。本文研究零样本设置下的该任务：模型仅依赖公开的二值标注驾驶事故数据集，需泛化至未见的行车记录仪画面。我们提出一个框架，通过将VideoMAE-v2主干与帧级预测头结合，并采用滑动窗口协议，弥合帧级风险估计任务与粗粒度二值事故数据集之间的差距。该方法在2026年CVPR@AUTOPILOT零样本事故预测竞赛中获得第二名。代码已开源：https://github.com/TimeSouth/zero-shot-taa-solution。
原文摘要 · Abstract (English)
Traffic accident anticipation -- predicting the likelihood of an imminent collision at every frame of a dashcam video -- is safety-critical yet difficult to scale, because collecting in-domain annotated accident footage for every deployment scenario is prohibitively expensive. We study this task under a zero-shot setting where no target-domain training data is available: the model must learn exclusively from a publicly available binary-labelled driving-accident dataset and generalise to unseen dashcam footage. We propose a framework that bridges the gap between the frame-level temporal risk estimation task and coarsely labelled binary accident datasets by coupling a VideoMAE-v2 backbone with a per-frame prediction head under a sliding-window protocol. Our method achieves 2nd place in the 2026 CVPR@AUTOPILOT Zero-Shot Accident Anticipation competition. Code is available at https://github.com/TimeSouth/zero-shot-taa-solution.
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