发表机构
Rochester Institute of Technology; City University of Hong Kong（罗切斯特理工学院; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
muSync-GS是一种物理同步驾驶视频合成框架，通过耦合天气、道路几何编辑与车辆动力学，在12个CarSim案例上实现了车辆状态的精准预测与场景同步。
AI 中文摘要
高质量驾驶数据对于自动驾驶系统和生成式世界模型至关重要。然而，涉及恶劣天气、低轮胎-路面摩擦力下制动、不规则道路几何结构等罕见且关乎安全的场景，大规模采集成本高昂且存在风险。现有视频生成与3D高斯编辑方法可修改天气外观或道路几何，但通常未将这些编辑与轮胎-路面交互及车辆动力学耦合，导致编辑后的视频可能保留原始轨迹，而修改后的道路条件本应改变制动、车轮滑移、载荷转移及自车相机运动。本文提出muSync-GS，一种面向恶劣天气与道路高程危险场景的物理同步驾驶视频合成框架。由降水导出的路面条件共同控制道路外观与轮胎摩擦力，共享的道路高程轮廓同时驱动可见道路几何编辑与车轴激励。经校准的车辆模型预测速度、滑移率、法向载荷及俯仰角，用于构建自车相机轨迹与同步物理标注。在涵盖降水水平、制动输入及道路轮廓参数的12个预留CarSim案例上，该模型实现速度的平均案例级RMSE为0.0273 m/s，俯仰角为0.0590度，滑移率为0.0101，单轮法向载荷为26.61 N。结合重建场景实验，这些结果表明muSync-GS能在重现预留控制下车辆响应的同时，将其与可控场景编辑及自车相机运动同步。
英文摘要
High-quality driving data are essential for autonomous-driving systems and generative world models. However, rare and safety-critical scenarios involving adverse weather, braking under low tire--road friction, and uneven road geometry are costly and risky to collect at scale. Existing video-generation and 3D Gaussian editing methods can modify weather appearance or road geometry, but typically do not couple these edits with tire--road interaction and vehicle dynamics. As a result, an edited video may retain its original trajectory even when the modified road condition should alter braking, wheel slip, load transfer, and ego-camera motion. We present muSync-GS, a physics-synchronized framework for driving video synthesis under adverse-weather and road-elevation hazards. A precipitation-derived road-surface condition jointly controls road appearance and tire friction, while a shared road-elevation profile drives both visible road-geometry editing and axle excitation. A calibrated vehicle model predicts speed, slip ratio, normal loads, and pitch for constructing the ego-camera trajectory and synchronized physical annotations. On 12 held-out CarSim cases spanning precipitation levels, brake inputs, and road-profile parameters, the model achieves mean case-wise RMSEs of 0.0273 m/s for speed, 0.0590 degrees for pitch, 0.0101 for slip ratio, and 26.61 N for per-wheel normal load. Together with the reconstructed-scene experiments, these results show that muSync-GS accurately reproduces vehicle responses under held-out controls while synchronizing them with controllable scene edits and ego-camera motion.
Comments42 pages, 14 figures; includes an appendix
