[Submitted on 16 Sep 2026]
Title:4D Radar Perception Algorithms for Autonomous Driving: A Review
View a PDF of the paper titled 4D Radar Perception Algorithms for Autonomous Driving: A Review, by Xumin Wu and 4 other authors
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Abstract:Research on 4D millimeter-wave radar perception algorithms has flourished in recent years, extending from signal processing and object detection to semantic segmentation, motion estimation, occupancy prediction, and dynamic scene reconstruction. This review organizes the field according to the evolution of perception tasks and algorithms. It first introduces radar fundamentals, data representations, and quality-enhancement methods, and then reviews object-level perception, motion and localization, local and dense spatial perception, and dynamic scene understanding. Across these directions, we compare radar-only learning, multimodal fusion, and cross-modal supervision and knowledge distillation. Particular attention is paid to how elevation, Doppler measurements, and radar physical priors are exploited across tasks. We further summarize the task coverage, input data, annotations, and evaluation protocols of existing datasets, clarifying the empirical support for different research directions. Finally, we discuss the common challenges and future directions of 4D radar perception for autonomous driving. This review provides a task-oriented perspective on the transition from sparse object perception to dynamic spatial understanding.
Comments: 12 pages, 9 figures, 5 tables. Submitted to IEEE Sensors Journal
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
Cite as: arXiv:2609.19216 [cs.RO]
(or arXiv:2609.19216v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.19216
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xumin Wu [view email] [v1] Wed, 16 Sep 2026 12:46:07 UTC (5,355 KB)
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