Cognitive Dual-Process Planning for Autonomous Driving with Structured Scene Knowledge and Verifiable Reasoning-Action Consistency

TL;DR - A dual-process VLM planner routes routine driving scenes to fast action prediction and complex scenes to structured reasoning with rule-based consistency checks. It improves reasoning-action reliability while reducing latency by 17.39% versus always using slow reasoning.

• An automated data engine generates machine-parsable structured chain-of-thought supervision without manually annotating each rationale.
• A lightweight visual Arbiter estimates scene complexity before language decoding and selects the fast or slow planning path.
• A deterministic validator checks whether structured reasoning agrees with the final action and supplies verifiable rewards for GRPO.
• On 574 verified NAVSIM samples, the planner achieved 80.14% planning accuracy and 97.20% logical consistency.