学习驾驶是免费的礼物：从无位姿野外视频进行大规模无标签自动驾驶预训练
Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos
利用无位姿视频和教师模型生成的伪监督信号，联合预测点图、相机位姿、语义布局和运动掩码，学习统一的伪4D表征，可迁移到自动驾驶规划任务。
原文依据 We therefore propose a label-free, teacher-guided framework for learning autonomous driving representations directly from unposed videos.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在线可用的自我中心驾驶视频为自动驾驶提供了丰富的视觉数据来源，然而其缺乏标注使得学习同时捕捉语义结构和3D几何的表征变得困难。近期大规模前馈空间模型的进展表明，点图和自我运动可以在单次前向传播中推断，这为可扩展的驾驶感知指明了一个有前景的方向。因此，我们提出了一种无标签、教师引导的框架，直接从无位姿视频中学习自动驾驶表征。与先前主要关注帧间一致性的自监督方法不同，我们认为安全且反应灵敏的驾驶关键取决于时序上下文。为此，我们利用配备轻量级自回归模块的前馈架构，使用多模态监督信号进行训练，引导模型联合预测当前和未来的点图、相机位姿、语义布局和运动掩码。多模态教师提供序列级伪监督，使LFG能够从原始YouTube视频中学习统一的伪4D表征，无需位姿、标签或激光雷达。所得编码器不仅在NAVSIM基准上有效迁移到下游自动驾驶规划，仅用单个单目相机就超越了多相机和激光雷达基线，而且在一系列语义、几何和运动预测任务上评估时也表现出强劲性能。这些几何和运动感知的特征使LFG成为自动驾驶领域一个引人注目的以视频为中心的基础模型。
英文原文摘要 1639 字符
Ego-centric driving videos available online provide an abundant source of visual data for autonomous driving, yet their lack of annotations makes it difficult to learn representations that capture both semantic structure and 3D geometry. Recent advances in large feedforward spatial models demonstrate that point maps and ego-motion can be inferred in a single forward pass, suggesting a promising direction for scalable driving perception. We therefore propose a label-free, teacher-guided framework for learning autonomous driving representations directly from unposed videos. Unlike prior self-supervised approaches that focus primarily on frame-to-frame consistency, we posit that safe and reactive driving depends critically on temporal context. To this end, we leverage a feedforward architecture equipped with a lightweight autoregressive module, trained using multi-modal supervisory signals that guide the model to jointly predict current and future point maps, camera poses, semantic layouts, and motion masks. Multi-modal teachers provide sequence-level pseudo-supervision, enabling LFG to learn a unified pseudo-4D representation from raw YouTube videos without poses, labels, or LiDAR. The resulting encoder not only transfers effectively to downstream autonomous driving planning on the NAVSIM benchmark, surpassing multi-camera and LiDAR baselines with only a single monocular camera, but also yields strong performance when evaluated on a range of semantic, geometric, and motion prediction tasks. These geometry- and motion-aware features position LFG as a compelling video-centric foundation model for autonomous driving.