Wednesday, November 12, 2025
DENTON (夜色视频), Texas 鈥� As Texas鈥� population grows, researchers at the are developing artificial intelligence tools to help keep the state鈥檚 busy highways safer and more efficient. The projects that North Texas will surpass 12 million residents in the next 25 years 鈥� bringing more vehicles, more traffic and greater demand for faster response times when issues arise on the road.
One common cause of traffic is debris on the road. Whether it鈥檚 large pieces of furniture or fallen trash bags, debris can cause dangerous situations for drivers that often lead to crashes. The Texas Department of Transportation (TxDOT) relies on reports to know when debris is on the state鈥檚 more than 3,200 miles of interstate highways. Two researchers at 夜色视频 hope to make their job easier.
夜色视频's Yan Huang and Heng Fan will develop an AI geared towards clearing road debris sooner
鈥淭he biggest obstacle to timely removal is quick detection,鈥� said Regents Professor of Yan Huang. 鈥淭here鈥檚 a lot of crossover data of reports, but they鈥檙e not being leveraged and utilized to their full extent yet. We want to change that.鈥�
, who specializes in smart transportation and spatial databases and networks, is working alongside Assistant Professor from the same department in the .
鈥淥ne of the applications of my work is detecting objects from images or videos,鈥� Fan said. 鈥淲hen Dr. Huang talked to me about this project, I thought it was a great application of what I鈥檝e studied in smart transportation. It鈥檚 a very interesting project that could benefit TxDOT and potentially other state departments of transportation.鈥�
Together, the researchers will develop an AI system that analyzes data from three sources 鈥� WAZE, electric vehicle dash cameras and TxDOT鈥檚 closed-circuit television (CCTV) traffic cameras 鈥� to track and assess road conditions. WAZE provides real-time, crowdsourced reports from drivers, while CCTV cameras supply continuous video monitoring for TxDOT鈥檚 highway operations.
鈥淲e鈥檝e seen that roughly 72% of debris reports come from WAZE, and those reports tend to be received about 16 minutes earlier than traditional methods,鈥� Huang said. 鈥淏y combining WAZE with other crowdsourced data, we hope to detect debris even faster and improve response times.鈥�
The two will work alongside TxDOT and the Texas A&M Transportation Institute (TTI)
for the project. TTI is a Texas state agency and member of the Texas A&M University
System that focuses on transportation research and innovation. Huang says TTI reached
out to her to collaborate on the project after she wrote her project proposal.
鈥淭hey have experience with the operational side of TxDOT and with WAZE. They鈥檒l also work with transportation management centers to develop communications protocols and learn what their removal processes are so this should be a mutually beneficial collaboration.鈥�
On their end, Huang and Fan will develop a prototype algorithm that will be able to detect debris in real time and label the types of the debris. They plan to test it in three different districts which will be chosen later in the project.
鈥淲e need to be able to accurately classify the debris because the process is different if it鈥檚 small debris, large debris or an animal,鈥� Huang said. 鈥淭he goal is to do this as quickly as possible to make removal faster and the roads safer.鈥�
When ready, TTI and TxDOT will test pilot the algorithms. The project will last two years, and the professors hope to bring on a graduate student as well. Huang and Fan both feel this project is only the beginning of leveraging AI for transportation.
鈥淭here鈥檚 a lot of innovation that can be done after this,鈥� Huang said. 鈥淭he advancement of AI is so fast and furious, it鈥檚 only natural that transportation is going to benefit from it.鈥�
From 鈥� Research