RAP: 3D Rasterization Augmented End-to-End Planning

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 (R2R) feature-space alignment that bridges the sim-to-real gap at the representation level. Together, these components form the Rasterization Augmented Planning (RAP) pipeline, a scalable data augmentation framework for planning. RAP achieves state-of-the-art closed-loop robustness and long-tail generalization, ranking 1st on four major benchmarks: NAVSIM v1/v2, Waymo Open Dataset Vision-based E2E Driving, and Bench2Drive. Our results demonstrate that lightweight rasterization with feature alignment suffices to scale end-to-end training, offering a practical alternative to photorealistic rendering. Project page: https://alan-lanfeng.github.io/RAP/.