Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be corrected and quickly compound into failures. A promising direction is to generate alternative viewpoints and trajectories beyond the logged path. Prior work explores photorealistic digital twins via neural rendering or game engines, but these methods are prohibitively slow and costly, and thus mainly used for evaluation. In this work, we argue that photorealism is unnecessary for training end-to-end planners. What matters is semantic fidelity and scalability: driving depends on geometry and dynamics, not textures or lighting. Motivated by this, we propose 3D Rasterization, which replaces costly rendering with lightweight rasterization of annotated primitives, enabling augmentations such as counterfactual recovery maneuvers and cross-agent view synthesis. To transfer these synthetic views effectively to real-world deployment, we introduce a Raster-to-Real feature-space alignment that bridges the sim-to-real gap. Together, these components form Rasterization Augmented Planning (RAP), a scalable data augmentation pipeline for planning. RAP achieves state-of-the-art closed-loop robustness and long-tail generalization, ranking first on four major benchmarks: NAVSIM v1/v2, Waymo Open Dataset Vision-based E2E Driving, and Bench2Drive. Our results show that lightweight rasterization with feature alignment suffices to scale E2E training, offering a practical alternative to photorealistic rendering. Project page: https://alan-lanfeng.github.io/RAP/.

翻译：端到端驾驶的模仿学习仅依赖于专家演示数据进行策略训练。一旦部署于闭环系统中，此类策略缺乏恢复数据：微小的错误无法被纠正，并迅速累积导致失败。一个具有前景的方向是生成超出记录路径的替代视角与轨迹。先前工作通过神经渲染或游戏引擎探索了照片级逼真的数字孪生，但这些方法速度极慢且成本高昂，因此主要用于评估。在本研究中，我们论证了照片级真实感对于训练端到端规划器并非必需。关键在于语义保真度与可扩展性：驾驶行为依赖于几何与动力学特性，而非纹理或光照。基于此，我们提出三维栅格化方法，以轻量级标注图元栅格化替代昂贵的渲染过程，从而支持反事实恢复机动与跨智能体视角合成等增强操作。为有效将这些合成视角迁移至现实世界部署，我们引入了栅格-现实特征空间对齐方法，以弥合仿真与现实的差距。这些组件共同构成了栅格化增强规划框架，这是一种可扩展的规划数据增强流程。RAP在闭环鲁棒性与长尾泛化能力上达到最先进水平，在四大基准测试中均位列第一：NAVSIM v1/v2、Waymo开放数据集基于视觉的端到端驾驶以及Bench2Drive。我们的结果表明，结合特征对齐的轻量级栅格化足以扩展端到端训练规模，为照片级渲染提供了实用替代方案。项目页面：https://alan-lanfeng.github.io/RAP/。