# 4D Millimeter-Wave Radar in Autonomous Driving: A Survey

arXiv: https://arxiv.org/abs/2306.04242 · HTML: https://mlsanity.com/paper/2306.04242/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2023.

## Built on by (most cited first, 29 of 29)

- [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)
- [L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection](https://mlsanity.com/paper/2408.03677/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception](https://mlsanity.com/paper/2501.15394/) (2025)
- [Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension](https://mlsanity.com/paper/2405.12821/) (2024)
- [V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection](https://mlsanity.com/paper/2411.08402/) (2024)
- [ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving](https://mlsanity.com/paper/2504.03438/) (2025)
- [Radar-Guided Polynomial Fitting for Metric Depth Estimation](https://mlsanity.com/paper/2503.17182/) (2025)
- [Structure-Aware Radar-Camera Depth Estimation](https://mlsanity.com/paper/2506.05008/) (2025)
- [4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges](https://mlsanity.com/paper/2503.24091/) (2025)
- [OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation](https://mlsanity.com/paper/2407.13137/) (2024)
- [RadarGaussianDet3D: Gaussian Representation-based Real-time 3D Object Detection with 4D Automotive Radars](https://mlsanity.com/paper/2509.16119/) (2025)
- [Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks](https://mlsanity.com/paper/2509.20674/) (2025)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [4DR P2T: 4D Radar Tensor Synthesis with Point Clouds](https://mlsanity.com/paper/2502.05550/) (2025)
- [A Multi-Sensor Fusion Approach for Rapid Orthoimage Generation in Large-Scale UAV Mapping](https://mlsanity.com/paper/2503.01202/) (2025)
- [Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation](https://mlsanity.com/paper/2601.18569/) (2026)
- [Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation](https://mlsanity.com/paper/2605.11840/) (2026)
- [ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness](https://mlsanity.com/paper/2607.23537/) (2026)
- [Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?](https://mlsanity.com/paper/2503.02687/) (2025)
- [The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radar](https://mlsanity.com/paper/2509.19644/) (2025)
- [A Lightweight Model-Driven 4D Radar Framework for Pervasive Human Detection in Harsh Conditions](https://mlsanity.com/paper/2601.13373/) (2026)
- [4D-CAAL: 4D Radar-Camera Calibration and Auto-Labeling for Autonomous Driving](https://mlsanity.com/paper/2601.21454/) (2026)
- [RAVEN: Radar Adaptive Vision Encoders for Efficient Chirp-wise Object Detection and Segmentation](https://mlsanity.com/paper/2604.04490/) (2026)
- [ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity](https://mlsanity.com/paper/2606.09634/) (2026)
- [Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation](https://mlsanity.com/paper/2606.26743/) (2026)
- [Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment](https://mlsanity.com/paper/2607.01983/) (2026)
- [4D Radar Perception Algorithms for Autonomous Driving: A Review](https://mlsanity.com/paper/2609.19216/) (2026)

## Abstract (arXiv)

The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community. This is attributed to its robustness in extreme environments and the velocity and elevation measurement capabilities. However, despite the rapid advancement in research related to its sensing theory and application, there is a conspicuous absence of comprehensive surveys on the subject of 4D mmWave radar. In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving. Initially, the paper provides reviews on the theoretical background and progress of 4D mmWave radars, encompassing aspects such as the signal processing workflow, resolution improvement approaches, and extrinsic calibration process. Learning-based radar data quality improvement methods are present following. Then, this paper introduces relevant datasets and application algorithms in autonomous driving perception, localization and mapping tasks. Finally, this paper concludes by forecasting future trends in the realm of 4D mmWave radar in autonomous driving. To the best of our knowledge, this is the first survey specifically dedicated to the 4D mmWave radar in autonomous driving.