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arXiv · 2606.09542

A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation

Siyuan Li·Xiaoyang Bi·Mengshi Qi
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Abstract

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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Siyuan Li, Xiaoyang Bi, Mengshi Qi. 2026-06-08. A VideoMAE-v2 Approach to Zero-Shot Traffic Accident Anticipation. https://arxiv.org/abs/2606.09542

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