Latent Chain-of-Thought World Modeling for End-to-End Driving

Understand

Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios.

• Most prior work uses natural language to express chain-of-thought (CoT) reasoning before producing driving actions.
• However, text may not be the most efficient representation for reasoning.
• In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered.