CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving

In three linesCLAP optimizes prompts in the latent space of Vision-Language-Action models to improve autonomous driving in rare safety-critical situations. Using contrastive learning and directional regularization, the method reduces planning error by 24% on challenging scenes (NAVSIM benchmark) with no regression on normal cases.

Summary generated by Claude — human-verified