自动驾驶不再模仿人类，自己练出了更狠的车技
现在的自动驾驶学开车，基本靠模仿人类司机的驾驶记录——人类开得怎么样，它就学成什么样，天花板就是人类。这篇直接绕开人类示范：把开车拆成「看懂世界」和「动手操作」两半，前者用现成的视觉大模型堆出一个感知骨干，后者用强化学习在由真实路况重建的交互世界里自己试错、自己练。练出来的老师再教给最终的车端模型，还能在测试时按不同驾驶意图生成人类日志里根本没有的多样训练数据。在 nuPlan 和 NAVSIM 等主流基准上，它全面超过人类驾驶回放的上限，而且全程只用摄像头、不需要任何人工标注。它不是你明天就能开上的车，但「学得比老师好」这条路，可能比堆更多人类数据更接近真正的自动驾驶。
📄 原文摘要(英文)
Most end-to-end autonomous-driving systems learn by imitating human driving logs, leaving their learned behavior constrained by the quality and behavioral coverage of the recorded trajectories. This report presents DriveZero, an end-to-end system that learns driving behavior beyond human demonstrations. It decomposes driving into a perception model and an action model, pretrains each in the regime best suited to it, and combines them into one end-to-end planner. The two models call for different learning recipes: perception must understand the world, and benefits from massive and diverse visual data; action must interact with it, and requires closed-loop feedback. On the action side, we introduce DriveRL, a mixed-agent closed-loop reinforcement-learning framework. It converts real driving logs into interactive worlds, where a privileged teacher policy is trained with PPO through closed-loop rollouts. For the perception model, DriveVFM consolidates multiple frozen vision foundation models, including DINOv3, SigLIP2, SAM and Depth Anything V2, into a single backbone from raw images alone, requiring no task-specific annotations. DriveZero then unifies the two: a camera-only planner that distills the frozen DriveRL teacher through its rolled-out trajectories. The goal-conditioned teacher can moreover be queried under augmented driving intents, yielding diverse, goal-consistent supervision that logged data cannot provide. On nuPlan, DriveRL with value-guided test-time action search achieves a mean score of 93.57 across the Val14, Test14-hard, and Test14-random community splits in both non-reactive and reactive modes, exceeding the Log-Replay expert on all three splits. DriveZero achieves state-of-the-art performance on NAVSIMv1, NAVSIMv2 and the closed-loop HUGSIM benchmark without any human trajectory supervision.