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”的轨迹喂给模型，它就会学会偷懒：不是看路况推理，而是顺着答案编理由。研究者发现这会让模型在复杂场景下产生更严重的幻觉。他们的解法是把“规划”改成选择题——给出几条候选轨迹让模型选，选完再揭晓正确答案，既保留推理能力，又避免剧透。这不是你明天能开上的技术，但它解释了为什么现在的自动驾驶AI在刁钻路况下会犯低级错误。
📄 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.