# Experiments with mmWave Automotive Radar Test-bed

arXiv: https://arxiv.org/abs/1912.12566 · HTML: https://mlsanity.com/paper/1912.12566/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 14 of 14)

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2310.02260/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Scene-aware Learning Network for Radar Object Detection](https://mlsanity.com/paper/2107.01469/) (2021)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [A Systematic Study on Object Recognition Using Millimeter-wave Radar](https://mlsanity.com/paper/2305.02085/) (2023)
- [mmIR: Frequency-Space Inverse Rendering for 3D Millimeter-Wave Radar ADC Synthesis](https://mlsanity.com/paper/2608.28913/) (2026)
- [Learning to Detect Open Carry and Concealed Object with 77GHz Radar](https://mlsanity.com/paper/2111.00551/) (2021)
- [Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels](https://mlsanity.com/paper/2311.18316/) (2023)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)

## Abstract (arXiv)

Millimeter-wave (mmW) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability to provide high accuracy location, velocity, and angle estimates of objects, largely independent of environmental conditions. Such radar sensors not only perform basic functions such as detection and ranging/angular localization, but also provide critical inputs for environmental perception via object recognition and classification. To explore radar-based ADAS applications, we have assembled a lab-scale frequency modulated continuous wave (FMCW) radar test-bed (https://depts.washington.edu/funlab/research) based on Texas Instrument's (TI) automotive chipset family. In this work, we describe the test-bed components and provide a summary of FMCW radar operational principles. To date, we have created a large raw radar dataset for various objects under controlled scenarios. Thereafter, we apply some radar imaging algorithms to the collected dataset, and present some preliminary results that validate its capabilities in terms of object recognition. Our code is available at https://github.com/Xiangyu-Gao/mmWave-radar-signal-processing-and-microDoppler-classification.