用可执行的短推理令牌提升自动驾驶实时决策效率
XCoT-VLA: Executable Chain-of-Thought for Vision-Language-Action Driving
- 用自动构建的因果监督训练紧凑可执行的推理令牌
- 纵向误差降低至1.323，变道场景横向误差降至0.648
- 仅需2-6个令牌即可实现实时规划，适合部署于车载系统
视觉-语言-动作（VLA）模型可连接场景理解、语义推理与轨迹生成以实现自动驾驶。然而，传统的自然语言链式思维（CoT）冗长且难以优化，不适用于实时控制。本文提出XCoT-VLA，将描述性推理替换为从自动构建的“原因-动作”监督中学习到的紧凑可执行推理令牌。日志轨迹提供动作证据，场景上下文提供因果语义。预测的XCoT序列保持上下文一致性，并通过共享多模态自注意力条件化固定轨迹查询。确定性令牌函数路由分别对XCoT令牌使用推理前馈网络，对轨迹查询使用控制前馈网络，实现流匹配轨迹生成。进一步引入同空间的可执行策略优化（XCPO）作为可选扩展。在通用分布数据集上，纵向平均距离误差（ADE）从1.645降至1.323；在变道场景中，横向最终距离误差（FDE）从1.616降至0.648。仅用2-6个可执行令牌即可实现驾驶推理，显著降低自回归推理开销并满足实时规划需求。结果表明，面向驾驶的推理可实现紧凑、可执行且直接关联轨迹生成。
原文摘要 · Abstract (English)
Vision-Language-Action (VLA) models can connect scene understanding, semantic reasoning, and trajectory generation for autonomous driving. However, verbose natural-language Chain-of-Thought (CoT) is poorly suited to real-time control because it is open-ended, costly to decode, and difficult to optimize as an action-facing representation. We propose XCoT-VLA, which replaces descriptive rationales with compact executable CoT tokens learned from automatically constructed Reason-Action supervision. Logged trajectories provide action evidence, while scene context supplies causal semantics. The predicted XCoT sequence remains in context and conditions fixed trajectory queries through shared multimodal self-attention. Deterministic token-function routing applies the Reason FFN to XCoT tokens and the Control FFN to trajectory queries for flow-matching trajectory generation. We further introduce XCoT Policy Optimization (XCPO) as an optional refinement extension in the same executable token space. XCoT-VLA reduces longitudinal ADE from 1.645 to 1.323 on a general-distribution set and lateral FDE from 1.616 to 0.648 in lane-change scenarios. By representing driving-oriented reasoning with only 2-6 executable XCoT tokens, our method substantially reduces autoregressive reasoning overhead and remains within the real-time planning budget. These results demonstrate that driving-oriented reasoning can be compact, executable, and directly connected to trajectory generation.
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