Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weatherrobust 4D radar provides Doppler and additional geometric information, raising the possibility of addressing this challenge. To this end, we present V2X-R, the first simulated V2X dataset incorporating LiDAR, camera, and 4D radar. V2X-R contains 12,079 scenarios with 37,727 frames of LiDAR and 4D radar point clouds, 150,908 images, and 170,859 annotated 3D vehicle bounding boxes. Subsequently, we propose a novel cooperative LiDAR-4D radar fusion pipeline for 3D object detection and implement it with various fusion strategies. To achieve weather-robust detection, we additionally propose a Multi-modal Denoising Diffusion (MDD) module in our fusion pipeline. MDD utilizes weather-robust 4D radar feature as a condition to prompt the diffusion model to denoise noisy LiDAR features. Experiments show that our LiDAR-4D radar fusion pipeline demonstrates superior performance in the V2X-R dataset. Over and above this, our MDD module further improved the performance of basic fusion model by up to 5.73%/6.70% in foggy/snowy conditions with barely disrupting normal performance. The dataset and code will be publicly available at: https://github.com/ylwhxht/V2X-R.

翻译：当前的车路协同系统利用激光雷达与相机数据显著提升了三维目标检测性能。然而，这些方法在恶劣天气条件下性能会下降。具备天气鲁棒性的4D雷达可提供多普勒信息与额外的几何信息，为解决这一挑战提供了可能。为此，我们提出了V2X-R，这是首个融合激光雷达、相机与4D雷达的模拟车路协同数据集。V2X-R包含12,079个场景，共计37,727帧激光雷达与4D雷达点云、150,908张图像以及170,859个标注的三维车辆边界框。基于此，我们提出了一种新颖的激光雷达-4D雷达协同融合流程用于三维目标检测，并实现了多种融合策略。为实现天气鲁棒性检测，我们进一步在融合流程中提出了一个多模态去噪扩散模块。该模块利用天气鲁棒性的4D雷达特征作为条件，引导扩散模型对受噪声污染的激光雷达特征进行去噪。实验表明，我们的激光雷达-4D雷达融合流程在V2X-R数据集上表现出优越性能。此外，我们的多模态去噪扩散模块在雾天/雪天条件下将基础融合模型的性能进一步提升达5.73%/6.70%，且几乎不影响正常天气下的性能。数据集与代码将公开于：https://github.com/ylwhxht/V2X-R。