LaST-VLA: Thinking in Latent Spatio-Temporal Space for Vision-Language-Action in Autonomous Driving

摘要

While Vision-Language-Action (VLA) models have revolutionized autonomous driving by unifying perception and planning, their reliance on explicit textual Chain-of-Thought (CoT) leads to semantic-perceptual decoupling and perceptual-symbolic conflicts. Recent shifts toward latent reasoning attempt to bypass these bottlenecks by thinking in continuous hidden space. However, without explicit intermediate constraints, standard latent CoT often operates as a physics-agnostic representation. To address this, we propose the **La**tent **S**patio-**T**emporal **VLA** (**LaST-VLA**), a framework shifting the reasoning paradigm from discrete symbolic processing into a physically grounded Latent Spatio-Temporal CoT. By implementing a dual-feature alignment mechanism, we distill geometric constraints from 3D foundation models and dynamic foresight from world models directly into the latent space. Coupled with a progressive SFT training strategy that transitions from feature alignment to trajectory generation, and further refined via Reinforcement Learning with Group Relative Policy Optimization (GRPO) for safety and rule compliance, LaST-VLA achieves state-of-the-art performance across multiple autonomous driving benchmarks, including **NAVSIM v1**, **NAVSIM v2**, **Bench2Drive**, **SURDS**, and **NuDynamics**.