4D radar has higher point cloud density and precise vertical resolution than conventional 3D radar, making it promising for adverse scenarios in the environmental perception of autonomous driving. However, 4D radar is more noisy than LiDAR and requires different filtering strategies that affect the point cloud density and noise level. Comparative analyses of different point cloud densities and noise levels are still lacking, mainly because the available datasets use only one type of 4D radar, making it difficult to compare different 4D radars in the same scenario. We introduce a novel large-scale multi-modal dataset that captures both types of 4D radar, consisting of 151 sequences, most of which are 20 seconds long and contain 10,007 synchronized and annotated frames. Our dataset captures a variety of challenging driving scenarios, including multiple road conditions, weather conditions, different lighting intensities and periods. It supports 3D object detection and tracking as well as multi-modal tasks. We experimentally validate the dataset, providing valuable insights for studying different types of 4D radar.

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time.