Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly contributed to various autonomous driving tasks, its potential for data generation and augmentation in V2X scenarios remains underexplored. In this paper, we introduce CRUISE, a comprehensive reconstruction-and-synthesis framework designed for V2X driving environments. CRUISE employs decomposed Gaussian Splatting to accurately reconstruct real-world scenes while supporting flexible editing. By decomposing dynamic traffic participants into editable Gaussian representations, CRUISE allows for seamless modification and augmentation of driving scenes. Furthermore, the framework renders images from both ego-vehicle and infrastructure views, enabling large-scale V2X dataset augmentation for training and evaluation. Our experimental results demonstrate that: 1) CRUISE reconstructs real-world V2X driving scenes with high fidelity; 2) using CRUISE improves 3D detection across ego-vehicle, infrastructure, and cooperative views, as well as cooperative 3D tracking on the V2X-Seq benchmark; and 3) CRUISE effectively generates challenging corner cases.

翻译：车联万物（V2X）通信在自动驾驶中发挥着关键作用，实现了车辆与基础设施间的协同合作。尽管仿真技术已为多种自动驾驶任务做出重要贡献，但其在V2X场景中用于数据生成与增强的潜力仍未得到充分探索。本文提出CRUISE——一个专为V2X驾驶环境设计的综合重建与合成框架。CRUISE采用分解式高斯溅射技术，在精确重建真实场景的同时支持灵活编辑。通过将动态交通参与者分解为可编辑的高斯表征，CRUISE实现了驾驶场景的无缝修改与增强。此外，该框架能够从自车视角和基础设施视角渲染图像，从而为训练与评估提供大规模V2X数据集增强。实验结果表明：1）CRUISE能够以高保真度重建真实V2X驾驶场景；2）使用CRUISE可提升自车视角、基础设施视角及协同视角下的3D检测性能，并在V2X-Seq基准上改善协同3D跟踪效果；3）CRUISE能有效生成具有挑战性的极端案例。