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