Autonomous driving simulators still lack high-fidelity radar, even though radar is critical for robust perception in adverse weather. A key obstacle is that raw radar point clouds are extremely sparse and stochastic, making it difficult to model; we argue that simulating the full range-azimuth-Doppler cube is a more principled target. Existing radar cube simulators either rely purely on neural generators, which are opaque and offer little control over sensor attributes, or on detailed electromagnetic pipelines, which are slow, require proprietary hardware specifications, and still struggle to capture real-world complexity. We introduce Ctrl-RS, a controllable radar cube simulation framework that combines the strengths of both worlds. First, we build an environment reflection tensor from diverse sensor sources (including LiDAR, monocular cameras, and existing radar). Second, we abstract radar physics into a compact set of waveform parameters that characterize the 3D point spread function, yielding an intuitive embedding of radar attributes such as range resolution, Doppler broadening, and azimuth beam shape. Third, we train a WARP-Net on a large mixed dataset that fuses real, analytically synthesized, and simulator-generated radar cubes to cover a wide distribution of radar attributes. Ctrl-RS supports viewpoint changes, actor removal, and attribute editing. Experiments on RADDet, Carrada, and nuScenes show that our simulated data can match or surpass real radar in 2D detection and semantic segmentation, and consistently boosts performance in 3D detection when combined with real data. The Project is available at https://github.com/zhuxing0/Ctrl-RS.

翻译：自动驾驶仿真器仍缺乏高保真雷达模型，尽管雷达对于恶劣天气下的稳健感知至关重要。主要障碍在于原始雷达点云极度稀疏且具有随机性，难以建模；我们认为，对完整的距离-方位-多普勒立方体进行仿真才是更具原则性的目标。现有雷达立方体仿真器要么依赖纯神经生成器（不透明且对传感器属性控制力弱），要么依赖详细电磁管道（速度慢、需要专有硬件规格且难以捕捉真实世界的复杂性）。我们提出Ctrl-RS——一种结合两者优势的可控雷达立方体仿真框架。首先，我们从多种传感器源（包括激光雷达、单目相机和现有雷达）构建环境反射张量；其次，将雷达物理抽象为一组紧凑的波形参数，用于表征三维点扩散函数，从而形成对雷达属性（如距离分辨率、多普勒展宽和方位波束形状）的直观嵌入；第三，在大规模混合数据集上训练WARP-Net，融合真实雷达、解析合成雷达和仿真生成雷达立方体，以覆盖广泛的雷达属性分布。Ctrl-RS支持视点变换、参与者删除和属性编辑。在RADDet、Carrada和nuScenes上的实验表明，我们的仿真数据在二维检测和语义分割任务中可达到甚至超越真实雷达性能，在三维检测任务中与真实数据结合时能持续提升性能。项目地址：https://github.com/zhuxing0/Ctrl-RS。