CARA: Concept-Aware Risk Attention for Interpretable Collision Anticipation
Collision anticipation in autonomous driving requires not only accurate early warnings but also interpretable reasoning about what risk factors are being tracked and how risk evolves over time. Existing methods fall short in this regard: feature-driven models are opaque, post-hoc explanations often lack fidelity, and concept-based methods are mostly designed for static recognition rather than dynamic driving scenes. We propose CARA (Concept-Aware Risk Attention), an intrinsically interpretable spatio-temporal framework for collision anticipation. CARA derives domain-grounded risk concepts from accident narratives, aligns them with video frames via vision-language similarity, and organizes...
| Authors | Zhishan Tao, Ruoyu Wang, Yucheng Wu et al. |
|---|---|
| Primary category | cs.MM |
| Published | 24 July 2026 |
| Last updated at source | 28 August 2026 |
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