Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation
Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions between road users and the limited availability of diverse, large-scale datasets. To address these issues, we propose a dual-path framework. On the one hand, we employ a video synthesis pipeline that, guided by structured prompts, derives feature distributions from existing corpora and produces high-fidelity synthetic driving scenes consistent with the statistical patterns of real data. On the other hand, we design a graph neural network enriched with semantic cues, enabling dynamic reasoning over both spatial and semantic relations among...
| Authors | Yanchen Guan, Haicheng Liao, Chengyue Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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