Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct numerous experiments on the single modality of radar and camera, as well as the fused modalities. Results demonstrate that 4D radar-camera fusion can considerably enhance the robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io.

翻译：水面自动驾驶在执行危险及耗时任务（如海上监视、幸存者救援、环境监测、水文测绘与垃圾清理）中发挥着关键作用。本文提出WaterScenes——首个面向水面自动驾驶的多任务4D雷达-相机融合数据集。我们的无人水面艇（USV）配备4D雷达与单目相机，能够提供全天候解决方案以获取物体相关信息，包括颜色、形状、纹理、距离、速度、方位角与仰角。针对水面典型静态与动态物体，我们分别对相机图像和雷达点云进行像素级与点级标注。除基础感知任务（如目标检测、实例分割与语义分割）外，本数据集还提供自由空间分割与水线分割的标注。基于多任务与多模态数据，我们开展了雷达单模态、相机单模态以及融合模态的大量实验。结果表明，4D雷达-相机融合可显著增强水面感知的鲁棒性，尤其在恶劣光照与天气条件下。WaterScenes数据集已公开发布于https://waterscenes.github.io。