CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving
End-to-end autonomous driving systems powered by Vision-Language-Action (VLA) models achieve strong performance on common driving scenarios, yet remain brittle in rare but safety-critical long-tail situations such as active construction zones and complex yielding geometries. In this paper, we present a method that addresses the long-tail challenging scenes beyond data scaling and model training. We introduce CLAP (Contrastive Latent-space Prompt optimization), a location-aware adaptation framework that augments a frozen VLA driving model with per-roadblock soft prompts, optimized from crowdsourced data and retrieved on demand via Vehicle-to-Everything (V2X) communication. Our approach res...
| Authors | Ruiyang Zhu, Yuehan He, Boyuan Zheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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