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A curated list of radar datasets, detection, tracking and fusion
| Date | Stars |
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| 2026-07-24 | 1867 |
| 2026-07-25 | 1868 |
| 2026-07-28 | 1868 |
| 2026-07-30 | 1868 |
| 2026-08-06 | 1868 |
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A curated list of radar datasets, detection, tracking and fusion. <br>Keep updating.<br>Author: Yi Zhou<br>Contact: [email protected]
🚩I have published a review paper on radar perception. Please see the link below. It is open access. If you find the contents are useful, please cite this paper in your work. I will keep updating this repository for the latest works in the radar perception field.
## [Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges](https://www.mdpi.com/1424-8220/22/11/4208)
## The 41-page slides associated with this paper: [Link](https://www.slideshare.net/YiZhou66/slidesdeepradarperceptionforautonomousdrivingpdf) ; [Link for China Mainland](https://www.aliyundrive.com/s/CZ3SKqY3U4w)
---
## Contents
Overview
- [Review](#Review-Papers)
- [Seminars and Workshops](#Seminars-and-Workshops)
Data Perspective:
- [Radar Datasets](#Radar-Datasets)
- [Radar Signature](#Radar-Signature)
- [Calibration](#Calibration)
- [Labelling](#Labelling)
- [Augmentation](#Data-Augmentation)
- [Simulation](#Simulator)
- [Generative Model](#Generative-Model)
- [Testing](#Testing)
Signal Processing:
- [Radar Toolbox](#Radar-Toolbox)
- [MIMO Calibration](#MIMO-Calibration)
- [Detector](#Detector)
- [Super Resolution](#Super-Resolution)
- [Clustering](#Clustering)
- [Denoising](#Denoising)
Applications:
- [TI Reference Designs](#TI-Reference-Designs)
- [Ego-Motion Estimation](#Ego-Motion-Estimation)
- [Velocity Estimation](#Velocity-Estimation)
- [Depth Estimation](#Depth-Estimation)
- [Object Detection](#Object-Detection)
- [Sensor Fusion](#Sensor-Fusion)
- [Weakly Supervised](#Weakly-Supervised)
- [Tracking](#Tracking)
- [Prediction](#Prediction)
- [Occupancy Grid Map](#Occupancy-Grid-Map)
- [Open Space Segmentation](#Open-Space-Segmentation)
- [Scene Understanding (Static Segmentation)](#Scene-Understanding)
- [Place Recognition](#Place-Recognition)
- [Odometry and SLAM](#Odometry-and-SLAM)
- [Automotive SAR](#Automotive-SAR)
- [Human Activity](#Human-Activity-Recognition)
- [Radar-Audio](#Radar-Audio)
Challenges:
- [Weather Effect](#Weather-Effects)
- [Multi Path Effect](#Multi-Path-Effect)
- [Mutual Interference](#Mutual-Interference)
- [Cell Migration](#Range-and-Doppler-Cell-Migration)
- [Tx-Rx Leakage](#Tx-Rx-Leakage)
- [Imperfect Waveform Separation](#Imperfect-Waveform-Separation)
<br>
---
## Radar Datasets
In my [review paper](https://www.mdpi.com/1424-8220/22/11/4208), there is a table with more detials.
### Conventional Radar Datasets for Autonomous Driving
| Dataset | Radar Type | Data Type| Annotation | Link |
| ---- |----| ---- | ---- | ---- |
| nuScenes | Continental ARS408 x5 | Sparse PC | 3D bbox, TrackID | [Website](https://www.nuscenes.org/) |
| DENSE| 77Ghz Long-Range Radar | Sparse PC | 3D bbox |[Website](https://www.uni-ulm.de/en/in/driveu/projects/dense-datasets) |
| PixSet| TI AWR1843| Sparse PC | 3D bbox, TrackID| [Website](https://leddartech.com/solutions/leddar-pixset-dataset/)|
| Radar Scenes | 77GHz Middle-Range Radar x4 | Dense PC |2D point-wise, TrackID| [Website](https://radar-scenes.com/)|
| Pointillism | 2 TI AWR 1443 | PC | 3D bbox | [Github](https://github.com/Kshitizbansal/pointillism-multi-radar-data) |
| Zendar SAR | SAR | ADC, RD, PC| Pointwise Mask of Moving Vehicle |[Github](https://github.com/ZendarInc/ZendarSDK) |
| Cooperative Radars | 77GHz Radar x 3 | PC | Trajctory from GNSS-RTK | [Website](https://ieee-dataport.org/documents/radar-measurements-two-vehicles-three-cooperative-imaging-sensors) |
| aiMotive| 77GHz LRR Radar x2(Front and Back) | PC | 3D bbox, TrackID | [Website](https://github.com/aimotive/aimotive_dataset)|
<br>Comments: nuScenes, DENSE and Pixset are for sensor fusion, but not particularly address the role of radar. Radar scenes provides point-wise annotations for radar point cloud, but has no other modalities. Pointillism uses 2 rExcerpt of 97,442 characters
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Shanliang Yao · Yancheng Institute of Technology · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8e38fd9487b62b86, topic:autonomous-driving, topic:slam, readme:autonomous driving
matched fp:8e38fd9487b62b86, topic:deep-learning
matched fp:8e38fd9487b62b86, topic:dataset, readme:dataset, desc:datasets