4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913929171566592 |
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| author | Peng, Xiangyuan Tang, Miao Sun, Huawei Bierzynski, Kay Servadei, Lorenzo Wille, Robert |
| author_facet | Peng, Xiangyuan Tang, Miao Sun, Huawei Bierzynski, Kay Servadei, Lorenzo Wille, Robert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_24091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges Peng, Xiangyuan Tang, Miao Sun, Huawei Bierzynski, Kay Servadei, Lorenzo Wille, Robert Computer Vision and Pattern Recognition 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. |
| title | 4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.24091 |