DriveZero：超越人類示範的端到端駕駛

摘要

大多數端到端自動駕駛系統透過模仿人類駕駛日誌來學習，使其學到的行為受制於所記錄軌跡的品質與行為覆蓋範圍。本報告提出 DriveZero，一套能學習超越人類示範之駕駛行為的端到端系統。它將駕駛分解為感知模型與動作模型，分別在最適合各自的機制中預訓練，再結合成單一端到端規劃器。這兩個模型需要不同的學習配方：感知必須理解世界，並受益於大量且多樣的視覺資料；動作必須與世界互動，且需要閉環回饋。在動作方面，我們提出 DriveRL，一個混合代理閉環強化學習框架。它將真實駕駛日誌轉換為互動式世界，在其中透過閉環 rollout 以 PPO 訓練具特權的教師策略。對於感知模型，DriveVFM 將多個凍結的視覺基礎模型，包括 DINOv3、SigLIP2、SAM 與 Depth Anything V2，僅從原始影像整合為單一骨幹，無需任務特定標註。DriveZero 接著統一兩者：一個僅使用相機的規劃器，透過其 rollout 軌跡蒸餾凍結的 DriveRL 教師。此目標條件教師還可在增強的駕駛意圖下被查詢，產生記錄資料無法提供的多樣且目標一致的監督。在 nuPlan 上，DriveRL 搭配價值引導的測試時動作搜尋，在 Val14、Test14-hard 與 Test14-random 社群分割中，於非反應式與反應式模式下均達到平均分數 93.57，並在所有三個分割上超越 Log-Replay 專家。DriveZero 在 NAVSIMv1、NAVSIMv2 與閉環 HUGSIM 基準上，在無任何人類軌跡監督下達到最先進效能。

English

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.