Synthetic Accidents as Probes for Open-Vocabulary Egocentric Driving

Fine-grained understanding of egocentric traffic accidents is essential for safe autonomous driving and requires large-scale, diverse training data. Synthetic supervision offers a scalable way to expand training coverage beyond what can be readily obtained from real accidents. To investigate whether such supervision can support generalizable accident understanding, we first introduce Ego-view Crashes (EgoCrash), a large-scale synthetic video-text benchmark comprising 20,000 egocentric accident videos across 56 fine-grained traffic-event categories, with rich multimodal annotations covering accident descriptions, question-answer pairs, motion cues, event categories, and entity trajectories. We further formulate open-vocabulary video-text retrieval for traffic accident understanding, where models retrieve accident videos and free-form descriptions bidirectionally and generalize from seen events to category-disjoint unseen events and real-world accident videos. To address this task, we propose maneuver-aware retrieval-augmented video-language matching (MRA-VLM). It learns motion-sensitive video representations by combining RGB appearance and optical-flow cues, and further enhances inference with prototype-based retrieval from seen events and uncertainty-guided score fusion, improving fine-grained matching and unseen-event generalization. In extensive experiments on EgoCrash, MRA-VLM consistently outperforms strong baselines in both retrieval directions and across both seen and unseen category splits, and models trained exclusively on synthetic data also transfer to real accident videos, demonstrating the potential of synthetic video-text supervision for generalizable accident understanding.