FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving

Understand

Vision-Language-Action (VLA) models offer significant potential for end-to-end driving, yet their reasoning is often constrained by textual Chains-of-Thought (CoT).

• This symbolic compression of visual information creates a modality gap between perception and planning by blurring spatio-temporal relations and discarding fine-grained cues.
• We introduce FSDrive, a framework that empowers VLAs to "think visually" using a novel visual spatio-temporal CoT.
• FSDrive first operates as a world model, generating a unified future frame that combines a predicted background with explicit, physically-plausible priors like future lane dividers and 3D object boxes.