自动驾驶AI的推理被剧透带偏了
训练自动驾驶大模型时，如果提前把“正确答案”告诉它，它就会学会偷懒：不是看路况做判断，而是顺着答案编理由。研究者发现，这种“剧透”会让模型在复杂场景下产生更严重的幻觉。他们提出一个新方法：把驾驶决策变成选择题，先让模型选，再公布正确答案来验证。这样既保留了推理能力，又不会让模型走捷径。这不是你明天就能用上的技术，但它揭示了一个关键问题：AI的“推理”可能只是顺着答案编故事。
📄 原文摘要(英文)
Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision-Language Model (VLM) components, yet existing annotation pipelines commonly expose the teacher model to the logged ground-truth (GT) future trajectory. We empirically show that this induces trajectory anchoring bias: teacher models rationalize the revealed outcome rather than infer a decision from scene evidence, producing less causally faithful CoTs and substantially more severe hallucinations, especially in causally challenging scenes. Removing the GT trajectory eliminates this shortcut, but open-ended trajectory generation entangles high-level decision-making with precise geometric synthesis and low-level dynamics. To make trajectory-level driving decisions verifiable without requiring open-ended trajectory synthesis, we introduce Autonomous-Driving Multiple-Choice Question (AD-MCQ), which casts planning as selection among explicit trajectory candidates. Taking this a step further, we propose Deferred Exposure of Future Trajectories for RLVR (DEFT-RLVR) to transform future trajectories from pre-decision anchors into post-decision verification targets. Experimental results show that DEFT-RLVR improves AD reasoning while preserving or even enhancing general visual capabilities. With VLM-only inference and controllable difficulty through candidate construction, AD-MCQ provides a flexible, scalable, and extensible foundation for future research on verifiable AD reasoning.