Vault: One-Step Latent Generation with Positive-Anchored Rewards for Autonomous Driving
End-to-end autonomous driving must balance multimodal maneuver generation against real-time inference constraints. Diffusion planners capture diverse behaviors, but their iterative denoising incurs considerable inference cost in safety-critical deployment, while one-step alternatives still imitate only the single expert demonstration, so neither family certifies both the diversity and the quality of its outputs. To address this, we propose Vault, a framework that couples one-step latent generation with sample-based reinforcement learning guidance. Built on a V-JEPA 2.1 visual representation pretrained on the full NAVSIM training imagery, Vault generates trajectory candidates in a single f...
| Authors | Yining Xing, Zehong Ke, Zhiyuan Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 June 2026 |
| Last updated at source | 28 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.