发表机构
Institute of Computing Technology, Chinese Academy of Sciences; Beijing Jingwei Hirain Technologies Co., Inc.（中国科学院计算技术研究所; 北京经纬恒润科技股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述系统梳理4D毫米波雷达感知算法，涵盖从信号处理到动态场景理解的任务演变，比较不同学习范式，总结数据集并展望未来方向。
AI 中文摘要
近年来，关于4D毫米波雷达感知算法的研究蓬勃发展，其范围从信号处理和物体检测扩展到语义分割、运动估计、占用预测和动态场景重建。本综述根据感知任务和算法的演变对该领域进行组织。首先介绍雷达基础、数据表示和质量增强方法，然后回顾物体级感知、运动与定位、局部和密集空间感知以及动态场景理解。在这些方向中，我们比较了仅雷达学习、多模态融合以及跨模态监督和知识蒸馏。特别关注了仰角、多普勒测量和雷达物理先验如何在各项任务中被利用。我们进一步总结了现有数据集的任务覆盖范围、输入数据、标注和评估协议，阐明了不同研究方向的经验支持。最后，我们讨论了4D雷达感知在自动驾驶中的常见挑战和未来方向。本综述提供了从稀疏物体感知向动态空间理解转变的任务导向视角。
英文摘要
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.
Comments12 pages, 9 figures, 5 tables. Submitted to IEEE Sensors Journal
