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Paper FR-LM-T43.2

Peng, Xiangyuan (Infineon Technologies AG, Technical University of Munich), Tang, Miao (China University of Geosciences), Sun, Huawei (Technical University of Munich; Infineon Technologies AG), Bierzynski, Kay (Infineon Technologies AG), Servadei, Lorenzo (Technical University of Munich), Wille, Robert (Technical University of Munich)

4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges

Scheduled for presentation during the Regular Session "S43a-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (FR-LM-T43), Friday, November 21, 2025, 10:50−11:10, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Advanced Sensor Fusion for Robust Autonomous Vehicle Perception, Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles

Abstract

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 current 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 in: https://github.com/XiangyPeng/4D-mmWave-Radar-in-Adverse-En vironments.

 

 

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