Cognitive Dual-Process Planning Framework for Autonomous Driving with Structured Scene Knowledge
A new dual-process planning framework for autonomous driving leverages a structured chain-of-thought (S-CoT) schema to represent scene knowledge. The system uses a lightweight Arbiter to route routine scenes to fast meta-action prediction and complex scenes to slower, structured reasoning, with a rule-based validator ensuring consistency between reasoning and actions. On the NAVSIM benchmark, the approach achieves 80.14% planning accuracy, 97.20% logical consistency, and reduces latency by 17.39% compared to always using slow reasoning. The framework also demonstrates strong annotation quality and identifies scenarios where performance may degrade.
Why it matters: This work demonstrates a practical advance in autonomous driving planning by enabling adaptive, verifiable reasoning with reduced manual annotation and improved efficiency.
Full story at: arXiv Robotics ↗