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 benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and 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且耗时的任务中发挥着关键作用，例如海上监视、幸存者救援、环境监测、水文测绘及垃圾清理。本文提出了WaterScenes，这是首个面向水面自动驾驶的多任务四维雷达-相机融合数据集。我们的无人水面艇（USV）配备四维雷达和单目相机，提供全天候解决方案以识别物体相关信息，包括颜色、形状、纹理、距离、速度、方位和高度。针对水面上的典型静态和动态物体，我们分别对相机图像和雷达点云进行了像素级和点级标注。除基础感知任务（如目标检测、实例分割和语义分割）外，我们还提供了自由空间分割和水线分割的标注。利用多任务和多模态数据，我们在雷达与相机的单模态及其融合模态上开展了基准实验。实验结果表明，四维雷达-相机融合可显著提升水面感知的精度与鲁棒性，尤其在恶劣光照和天气条件下。WaterScenes数据集公开于https://waterscenes.github.io。