A Review of 4D Millimeter-Wave Radar-Based Automotive Perception Technology Research
DOI:
https://doi.org/10.63313/FE.9011Keywords:
4D millimeter-wave radar, raw tensor, point cloud, 3D object detection, semantic occupancy, BEV, voxelAbstract
4D millimeter-wave radar, with its four-dimensional measurement capabilities encompassing range, azimuth, elevation, and Doppler velocity, has overcome the elevation perception limitations of traditional 3D millimeter-wave radar. Owing to its all-weather operation, high robustness, and cost advantages, it has emerged as a core sensor in the field of automotive intelligent perception. This paper systematically reviews the latest research progress in 4D millimeter-wave radar automotive perception technology, introduces common detection and semantic occupancy methods from two perspectives—raw tensor data and point cloud data—and discusses future development trends.
References
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[2] Felix Fent, Andras Palffy, Holger Caesar.DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection. In Proceedings of the IEEE Transactions on Intelligent Vehicles.2025,10(11):4929 - 4941
[3] Fangqiang Ding, Xiangyu Wen, Yunzhou Zhu, Yiming Li, Chris Xiaoxuan Lu. RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar.Advances in Neural Information Processing Systems 37: NeurIPS 2024.doi: 10.52202/079017-3222.
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