Hao Xu

Dr. Hao Xu’s recent research areas include roadside LiDAR sensing networks, algorithms for processing high-density city cloud points, edge- and cloud-based data processing, connected vehicle communication, all-traffic trajectory generation from roadside LiDAR data, and GIS-based traffic information extraction from LiDAR trajectory data. His research group is a worldwide leader in roadside LiDAR sensing and applications in traffic. Dr. Xu and his collaborators are applyitng the roadside LiDAR technologies and all-traffic trajectory data for connected-autonomous vehicle applications, real-time traffic signal control systems, and performance evaluation of multimodal traffic safety and mobility. He has published 100 research papers and his research team has received more than $6 million in funding and multiple research and paper awards.
Dr. Xu led the implementation of the worldwide first LiDAR-equipped smart and connected intersection in Reno, Nevada, in 2017. Since then, he has been performing innovative research in roadside LiDAR hardware, algorithms, software implementation, data applications, real-time signal systems taking LiDAR data input, and LiDAR data service to CAVs. His research team implemented the worldwide first LiDAR-controlled pedestrian crossing signal, which is the first real-time traffic signal system controlled by cloud point sensing data. Based on Dr. Xu’s research and projects, UNR and Velodyne published a white paper that demonstrates the ability of LiDAR sensors to make transportation infrastructure more efficient, sustainable, and safe. Dr. Xu’s team collected multi-year roadside LiDAR data from various traffic scenarios and now maintains a large roadside LiDAR database as an invaluable data asset for smart traffic research.
Dr. Xu also led several projects on data-driven safety analysis, including street light data collection and safety analysis; safety benefit-cost analysis of roundabouts; before-and-after complete streets data collection; correlation analysis of Nevada crash data and ITS sensor data; automatic horizontal curve identification and estimation; assessment of the influence of driver, vehicle, roadway, and environmental factors on pedestrian and turning-traffic crashes at intersections; and development of a comprehensive crash database for Nevada that can be used with AASHTOWare Safety Analyst.
Dr. Xu’s research has attracted collaboration interest from multiple companies such as Velodyne LiDAR, Intel, Dell, Qualcomm, and Switch. His research and projects have been noted by multiple media publications, such as BBC, USA Today, Yahoo News, Business Wire, AASHTO Journal, and Nevada Today. Multiple traffic agencies have adopted the portable roadside LiDAR platform to collect extensive traffic information that is not available via traditional traffic sensors.