让自动驾驶场景生成更可控，能模拟危险驾驶事故。
ReinDriveGen: Reinforcement Post-Training for Out-of-Distribution Driving Scene Generation
- 用强化学习优化生成效果，提升异常场景表现。
- 可自由编辑车辆轨迹，生成真实感强的驾驶视频。
- 适合自动驾驶安全测试与仿真系统开发者。
我们提出ReinDriveGen，一种可完全控制动态驾驶场景的框架，支持用户自由编辑车辆轨迹，以模拟前车碰撞、车辆漂移、失控旋转、行人乱穿马路、自行车横穿车道等安全关键边缘情况。该方法从多帧激光雷达数据构建动态3D点云场景，引入车辆补全模块，从部分观测重建360°完整几何结构，并将编辑后的场景渲染为2D条件图像，引导视频扩散模型生成逼真的驾驶视频。由于这些编辑场景必然超出训练分布，我们进一步提出基于强化学习的后训练策略，结合成对偏好模型与成对奖励机制，在无真实标签监督下实现对分布外条件的鲁棒质量提升。大量实验表明，ReinDriveGen在编辑驾驶场景上优于现有方法，并在新视角合成任务中达到当前最佳性能。
原文摘要 · Abstract (English)
We present ReinDriveGen, a framework that enables full controllability over dynamic driving scenes, allowing users to freely edit actor trajectories to simulate safety-critical corner cases such as front-vehicle collisions, drifting cars, vehicles spinning out of control, pedestrians jaywalking, and cyclists cutting across lanes. Our approach constructs a dynamic 3D point cloud scene from multi-frame LiDAR data, introduces a vehicle completion module to reconstruct full 360° geometry from partial observations, and renders the edited scene into 2D condition images that guide a video diffusion model to synthesize realistic driving videos. Since such edited scenarios inevitably fall outside the training distribution, we further propose an RL-based post-training strategy with a pairwise preference model and a pairwise reward mechanism, enabling robust quality improvement under out-of-distribution conditions without ground-truth supervision. Extensive experiments demonstrate that ReinDriveGen outperforms existing approaches on edited driving scenarios and achieves state-of-the-art results on novel ego viewpoint synthesis.
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