4D毫米波雷达在恶劣环境下仍能稳定感知，是智能交通的可靠选择。
4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges
- 综述4D毫米波雷达在雨雪雾等恶劣环境中的应用进展
- 涵盖多种气象与光照条件下的雷达数据集和学习方法
- 适合自动驾驶与智能交通领域研究者参考
智能交通系统需要精准可靠的感知能力。然而，雨、雪、雾等恶劣环境会显著降低激光雷达和摄像头的性能。相比之下，4D毫米波雷达不仅能提供三维点云和速度信息，还能在复杂条件下保持鲁棒性。近年来，针对恶劣环境中4D毫米波雷达的研究日益增多，但尚缺乏系统性综述。为此，本文首次全面回顾了该领域的最新进展：首先梳理了涵盖多样气象与光照场景的4D毫米波雷达数据集；其次分析了基于学习的方法在不同恶劣条件下的性能提升策略；最后讨论了当前挑战与未来发展方向。相关研究列表详见：https://github.com/XiangyPeng/4D-mmWave-Radar-in-Adverse-Environments。
原文摘要 · Abstract (English)
Intelligent transportation systems require accurate and reliable sensing. However, adverse environments, such as rain, snow, and fog, can significantly degrade the performance of LiDAR and cameras. In contrast, 4D mmWave radar not only provides 3D point clouds and velocity measurements but also maintains robustness in challenging conditions. Recently, research on 4D mmWave radar under adverse environments has been growing, but a comprehensive review is still lacking. To bridge this gap, this work reviews the current research on 4D mmWave radar under adverse environments. First, we present an overview of existing 4D mmWave radar datasets encompassing diverse weather and lighting scenarios. Subsequently, we analyze existing learning-based methods leveraging 4D mmWave radar to enhance performance according to different adverse conditions. Finally, the challenges and potential future directions are discussed for advancing 4D mmWave radar applications in harsh environments. To the best of our knowledge, this is the first review specifically concentrating on 4D mmWave radar in adverse environments. The related studies are listed at: https://github.com/XiangyPeng/4D-mmWave-Radar-in-Adverse-Environments.
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