# RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users

arXiv: https://arxiv.org/abs/2105.00363 · HTML: https://mlsanity.com/paper/2105.00363/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [CNN based Road User Detection using the 3D Radar Cube](https://mlsanity.com/paper/2004.12165/) (2020)
- [Probabilistic Oriented Object Detection in Automotive Radar](https://mlsanity.com/paper/2004.05310/) (2020)
- [Deep Radar Detector](https://mlsanity.com/paper/1906.12187/) (2019)

## Built on by (most cited first, 30 of 52)

- [K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions](https://mlsanity.com/paper/2206.08171/) (2022)
- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://mlsanity.com/paper/2405.14014/) (2024)
- [Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion](https://mlsanity.com/paper/2307.16532/) (2023)
- [T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals](https://mlsanity.com/paper/2303.16940/) (2023)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation](https://mlsanity.com/paper/2404.18150/) (2024)
- [Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation](https://mlsanity.com/paper/2309.17336/) (2023)
- [A recurrent CNN for online object detection on raw radar frames](https://mlsanity.com/paper/2212.11172/) (2022)
- [TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2310.02260/) (2023)
- [Bootstrapping Autonomous Driving Radars with Self-Supervised Learning](https://mlsanity.com/paper/2312.04519/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Radar Spectra-Language Model for Automotive Scene Parsing](https://mlsanity.com/paper/2406.02158/) (2024)
- [DoppDrive: Doppler-Driven Temporal Aggregation for Improved Radar Object Detection](https://mlsanity.com/paper/2508.12330/) (2025)
- [Towards Foundational Models for Single-Chip Radar](https://mlsanity.com/paper/2509.12482/) (2025)
- [SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data](https://mlsanity.com/paper/2406.10600/) (2024)
- [Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving](https://mlsanity.com/paper/2311.10261/) (2023)
- [4D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networks](https://mlsanity.com/paper/2404.05307/) (2024)
- [SIRA: Scalable Inter-frame Relation and Association for Radar Perception](https://mlsanity.com/paper/2411.02220/) (2024)
- [Wavelet-based Multi-View Fusion of 4D Radar Tensor and Camera for Robust 3D Object Detection](https://mlsanity.com/paper/2512.22972/) (2025)
- [Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence](https://mlsanity.com/paper/2206.06424/) (2022)
- [RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model](https://mlsanity.com/paper/2304.08447/) (2023)
- [AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection](https://mlsanity.com/paper/2504.00559/) (2025)
- [DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDAR](https://mlsanity.com/paper/2604.03685/) (2026)
- [ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals](https://mlsanity.com/paper/2209.12940/) (2022)
- [BoostRad: Enhancing Object Detection by Boosting Radar Reflections](https://mlsanity.com/paper/2404.17861/) (2024)
- [Controllable Radar Simulation with Waveform Parameter Embedding](https://mlsanity.com/paper/2506.03134/) (2025)

## Abstract (arXiv)

Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-Azimuth-Doppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.