将自动驾驶的决策与规划分离，提升推理能力并降低训练成本
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
- 用大模型做导航推理，小模型做驾驶控制，解耦设计提升效率
- 在nuScenes上优于大型VLM基线，实现更优端到端规划性能
- 适合关注可解释性与高效部署的自动驾驶研发人员
视觉语言模型（VLM）通过联合建模视觉感知、驾驶上下文和基于语言的推理，成为端到端自动驾驶的有前途方向。然而，现有VLM系统在高层推理与运动规划之间存在权衡：大模型具备强语义理解能力，但难以高效适配精确控制；小VLM模型虽可快速微调，但推理能力较弱。我们提出NaviDriveVLM，一种解耦框架，采用大规模导航器（Navigator）进行高层推理，轻量级可训练驱动器（Driver）生成动作。该设计保留了强推理能力，降低训练成本，并为下游规划提供显式可解释的中间表示。在nuScenes基准上的实验表明，NaviDriveVLM在端到端运动规划任务中优于大型VLM基线。
原文摘要 · Abstract (English)
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-based reasoning. However, existing VLM-based systems face a trade-off between high-level reasoning and motion planning: large models offer strong semantic understanding but are costly to adapt for precise control, whereas small VLM models can be fine-tuned efficiently but often exhibit weaker reasoning. We propose NaviDriveVLM, a decoupled framework that separates reasoning from action generation using a large-scale Navigator and a lightweight trainable Driver. This design preserves reasoning ability, reduces training cost, and provides an explicit interpretable intermediate representation for downstream planning. Experiments on the nuScenes benchmark show that NaviDriveVLM outperforms large VLM baselines in end-to-end motion planning.
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