UA Radar Lab's Multimodal Traffic Monitoring Project Releases Dataset
Cao's lab publishes dataset and platform for multimodal intersection traffic monitoring using mmWave radar.
In 2021, Dr. Siyang Cao and collaborators published a dataset entitled Development of Intelligent Multimodal Traffic Monitoring using Radar Sensor at Intersections.
Project Overview
The project integrates mmWave radar with machine learning to monitor multimodal traffic (vehicles, pedestrians, bicycles) at signalized intersections, robust under varying light and weather conditions.
Key Deliverables
- A radar-based prototype monitoring platform
- Real-world experimental data collected at intersections
- Live demonstration showing counts, speed, and direction estimation
This work demonstrates the lab's interest in deploying radar sensing beyond indoor and health applications into smart city infrastructure, contributing to safer and more efficient urban transportation systems.