AI RESEARCH
CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving
arXiv CS.LG
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ArXi:2605.17284v1 Announce Type: cross 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