Han, Zeyu (Tsinghua University), Jiang, Junkai (Tsinghua University), Ding, Xiaokang (Beihang university), Wang, Jiahao (Tsinghua University), Qingwen, Meng (Tsinghua university), Xu, Shaobing (Tsinghua University), He, Lei (Tsinghua University), Wang, Jianqiang (Tsinghua University)

DenserRadar: A 4D Millimeter-Wave Radar Point Cloud Detector Based on Dense LiDAR Point Clouds

Scheduled for presentation during the Invited Session "Driving the Edge: Addressing Corner Cases in Self-driving Vehicles" (WeBT5), Wednesday, September 25, 2024, 15:30−15:50, Salon 13

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of autonomous driving systems in corner-case scenarios, such as rainy, snowy or foggy weathers. Nevertheless, the inherent sparsity and noise of 4D mmWave radar point clouds restrict its further deployment and practical application. In this paper, we introduce a novel 4D mmWave radar point cloud detector, which leverages high-resolution dense LiDAR point clouds. Our approach constructs dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a specially designed network named DenserRadar. The proposed method surpasses existing probability-based and learning-based radar point cloud detectors in terms of both point cloud density and accuracy on the K-Radar dataset.