STEER: 経路認識型適応推論による自動運転
STEER: Route-Aware Adaptive Reasoning for Autonomous Driving
シーン難易度ラベルなしで、推論経路の不確実性と環境報酬に基づきCoT推論の過剰発動を抑える適応推論手法STEERを提案し、NAVSIMで検証した。
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著者: Yangang Zou, Pei Liu, Nan Song, Bozhou Zhang, Mingyu Guo, Jun Ma, Jiankang Deng, Xiatian Zhu, Li Zhang
分類: cs.RO
原文アブストラクト
Vision-language action (VLA) models excel for end-to-end autonomous driving, yet their tendency to over-invoke chain-of-thought (CoT) reasoning mirrors a human cognitive pitfall: overthinking. Just as deliberate reasoning can slow or mislead human judgment in routine tasks, excessive CoT in VLA incurs unnecessary compute overhead and can paradoxically degrade planning performance. Recent research tackles this via adaptive reasoning, which dynamically modulates inference complexity by selectively triggering CoT for complex scenes while responding directly to routine ones. While prior adaptive methods rely on implicit adaptation, recent work shows that explicitly optimizing the route between CoT and direct responses yields superior performance, assuming scene-difficulty annotations which are labor-intensive, poorly scalable, and prone to error due to the subjectivity of judging driving complexity. We explore, for the first time, whether adaptive reasoning with explicit route optimization can be achieved without any scene-difficulty labeling. We introduce STEER, a generic over-reasoning mitigation strategy featuring two key components: (i) routing uncertainty-aware rollout, which calibrates sampling to align routing diversity with model uncertainty; and (ii) cross-route advantage credit assignment, which introduces a differential metric to reinforce optimal routing decisions based on environmental rewards rather than subjective labels. Extensive experiments on NAVSIM v1/v2 demonstrate that STEER is competitive with state-of-the-art methods in both planning quality and the Pareto front of inference efficiency.
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