Research on localization and perception for Autonomous Driving is mainly focused on camera and LiDAR datasets, rarely on radar data. Manually labeling sparse radar point clouds is challenging. For a dataset generation, we propose the cross sensor Radar Artifact Labeling Framework (RALF). Automatically generated labels for automotive radar data help to cure radar shortcomings like artifacts for the application of artificial intelligence. RALF provides plausibility labels for radar raw detections, distinguishing between artifacts and targets. The optical evaluation backbone consists of a generalized monocular depth image estimation of surround view cameras plus LiDAR scans. Modern car sensor sets of cameras and LiDAR allow to calibrate image-based relative depth information in overlapping sensing areas. K-Nearest Neighbors matching relates the optical perception point cloud with raw radar detections. In parallel, a temporal tracking evaluation part considers the radar detections' transient behavior. Based on the distance between matches, respecting both sensor and model uncertainties, we propose a plausibility rating of every radar detection. We validate the results by evaluating error metrics on semi-manually labeled ground truth dataset of $3.28\cdot10^6$ points. Besides generating plausible radar detections, the framework enables further labeled low-level radar signal datasets for applications of perception and Autonomous Driving learning tasks.
翻译:关于自动驾驶的本地化和认知的研究主要侧重于照相机和LiDAR数据集,很少见雷达数据。手工标注稀有的雷达点云层具有挑战性。对于数据集的生成,我们提出跨传感器雷达人工定位标签框架(RALF),自动生成的汽车雷达数据标签有助于纠正雷达缺陷,如人工智能应用的人工制品等。RALF为雷达原始探测提供了可视性标签,区分了文物和目标。光学评价主干网包括环景照相机和LIDAR扫描机的普通单眼深度图像估计。现代汽车传感器和LIDAR能够校准重叠感测区基于图像的相对深度信息。K-Nearest Nerighbors匹配将光学感测点云与原始雷达探测联系起来。同时,一个时间跟踪评估部分考虑雷达探测的瞬间行为。根据匹配的距离,尊重传感器和模型的不确定性,我们建议对每一项雷达探测结果进行可辨识度评级。我们通过评估半maniv的标度测量标值A-rxxxxlad dal dal dread dald dal deal slab slab slaved slaft slad slad slad slad slad slation slation slaved flaved slation slation slated flaved flaved slations flaved sq.