mlsanityData to 7 Oct 2026

Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review

arXiv 2312.04861 ↗First posted 8 Dec 2023cs.CV cs.AIcited by 22

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Abstract (arXiv)

With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligent transportation. Among these equipped sensors, the radar sensor plays a crucial role in providing robust perception information in diverse environmental conditions. This review focuses on exploring different radar data representations utilized in autonomous driving systems. Firstly, we introduce the capabilities and limitations of the radar sensor by examining the working principles of radar perception and signal processing of radar measurements. Then, we delve into the generation process of five radar representations, including the ADC signal, radar tensor, point cloud, grid map, and micro-Doppler signature. For each radar representation, we examine the related datasets, methods, advantages and limitations. Furthermore, we discuss the challenges faced in these data representations and propose potential research directions. Above all, this comprehensive review offers an in-depth insight into how these representations enhance autonomous system capabilities, providing guidance for radar perception researchers. To facilitate retrieval and comparison of different data representations, datasets and methods, we provide an interactive website at https://radar-camera-fusion.github.io/radar.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 102 of its 261 references that are in the map. First posted on arXiv 8 Dec 2023.

Builds on

102 of 261 references.

References · 102

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38 more
  1. 2023Tri-Perspective View for Vision-Based 3D Semantic Occupancy Predictioncited by 404
  2. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Drivingcited by 325
  3. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
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  8. 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
  9. 2022Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?cited by 128
  10. 2022K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditionscited by 107
  11. 2020RADIATE: A Radar Dataset for Automotive Perception in Bad Weathercited by 98
  12. 2023CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perceptioncited by 81
  13. 2022Boreas: A Multi-Season Autonomous Driving Datasetcited by 75
  14. 2022TJ4DRadSet: A 4D Radar Dataset for Autonomous Drivingcited by 74
  15. 2020A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detectioncited by 70
  16. 2021Raw High-Definition Radar for Multi-Task Learningcited by 69
  17. 2022CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformercited by 66
  18. 2021RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applicationscited by 61
  19. 2020CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotationscited by 60
  20. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  21. 2023LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusioncited by 57
  22. 2018SBNet: Sparse Blocks Network for Fast Inferencecited by 53
  23. 2021RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Userscited by 52
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  38. 2022aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perceptioncited by 29

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 12; 2026: 8.

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  1. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 54
  2. 2024WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radarcited by 13
  3. 2024PriorDrive: Enhancing Online HD Mapping with Unified Vector Priorscited by 9
  4. 2025USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterwayscited by 4
  5. 2025Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detectioncited by 4
  6. 2025Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systemscited by 4
  7. 2025RASMD: RGB And SWIR Multispectral Driving Dataset for Robust Perception in Adverse Conditionscited by 3
  8. 2025InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneckcited by 3
  9. 2025SFGFusion: Surface Fitting Guided 3D Object Detection with 4D Radar and Camera Fusioncited by 2
  10. 2025RAPTR: Radar-based 3D Pose Estimation using Transformercited by 2
  11. 2026RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weathercited by 2
  12. 20254DR P2T: 4D Radar Tensor Synthesis with Point Cloudscited by 1
10 more
  1. 2025Depth-aware Fusion Method based on Image and 4D Radar Spectrum for 3D Object Detectioncited by 1
  2. 2025All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehiclescited by 1
  3. 2025REXO: Indoor Multi-View Radar Object Detection via 3D Bounding Box Diffusioncited by 1
  4. 2026Radar-Informed 3D Multi-Object Tracking under Adverse Conditionscited by 1
  5. 20264D-CAAL: 4D Radar-Camera Calibration and Auto-Labeling for Autonomous Drivingcited by 0
  6. 2026On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurementscited by 0
  7. 2026DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weathercited by 0
  8. 2026REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar Viewcited by 0
  9. 20264D Radar Perception Algorithms for Autonomous Driving: A Reviewcited by 0
  10. 2026STAR: Scene- and Task-Aware 4D Radar Preprocessing Towards End-to-End Cognitive Radarcited by 0