# 4D Radar Perception Algorithms for Autonomous Driving: A Review

arXiv: https://arxiv.org/abs/2609.19216 · HTML: https://mlsanity.com/paper/2609.19216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 55 of its 88 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 30 of 55)

- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving](https://mlsanity.com/paper/2204.13483/) (2022)
- [LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion](https://mlsanity.com/paper/2307.00724/) (2023)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision](https://mlsanity.com/paper/2303.00462/) (2023)
- [L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection](https://mlsanity.com/paper/2408.03677/) (2024)
- [RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://mlsanity.com/paper/2405.14014/) (2024)
- [Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving](https://mlsanity.com/paper/2310.07602/) (2023)
- [Self-Supervised Scene Flow Estimation with 4-D Automotive Radar](https://mlsanity.com/paper/2203.01137/) (2022)
- [4D Millimeter-Wave Radar in Autonomous Driving: A Survey](https://mlsanity.com/paper/2306.04242/) (2023)
- [OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving](https://mlsanity.com/paper/2412.10734/) (2024)
- [MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review](https://mlsanity.com/paper/2108.03004/) (2021)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception](https://mlsanity.com/paper/2411.10962/) (2024)
- [Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception](https://mlsanity.com/paper/2501.15394/) (2025)
- [RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud](https://mlsanity.com/paper/2309.09737/) (2023)
- [MVFAN: Multi-View Feature Assisted Network for 4D Radar Object Detection](https://mlsanity.com/paper/2310.16389/) (2023)
- [NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping](https://mlsanity.com/paper/2309.00962/) (2023)
- [4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping](https://mlsanity.com/paper/2303.13962/) (2023)
- [MSSF: A 4D Radar and Camera Fusion Framework With Multi-Stage Sampling for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2411.15016/) (2024)
- [Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?](https://mlsanity.com/paper/2309.06036/) (2023)
- [A Deep Automotive Radar Detector using the RaDelft Dataset](https://mlsanity.com/paper/2406.04723/) (2024)
- [RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network](https://mlsanity.com/paper/2409.04979/) (2024)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking](https://mlsanity.com/paper/2403.15313/) (2024)
- [Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds](https://mlsanity.com/paper/2309.16435/) (2023)
- [CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar](https://mlsanity.com/paper/2311.01423/) (2023)
- [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning](https://mlsanity.com/paper/2312.04519/) (2023)
- [DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds](https://mlsanity.com/paper/2405.05131/) (2024)
- [RadarMOSEVE: A Spatial-Temporal Transformer Network for Radar-Only Moving Object Segmentation and Ego-Velocity Estimation](https://mlsanity.com/paper/2402.14380/) (2024)

## Abstract (arXiv)

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.