SAN MARCOS, Texas (KXAN) — For the next two years, these students will use machine learning, a type of artificial intelligence, to identify and measure any cracking on roads across the country.

“We probably will travel to states like Oklahoma, Arkansas and Alabama, just states close to Texas,” explained Texas State associate civil engineering professor Feng Wang.

The National Science Foundation awarded the university a $250,000 grant this month, and Wang said the Texas Department of Transportation donated the van. Now, students will add sensors to the van that will collect data as they travel.

“We are going to mount 3D cameras and laser cameras,” explained Haitao Gong, a PhD student at the university working on the project.

Wang said they’re hoping by using this form of AI they can improve the accuracy of automated data collection on pavement conditions. But first, they have to tweak the technology to make sure it gets the data right.

“For example, somebody you know, pours a cup of coffee on the surface road, and the machine’s picture could be translated by the robots or the machine as a pothole underground, but for human being eyes it can recognize that someone dropped a cup of coffee, so that’s the difference between human intelligence and also the machine,” he said.

Right now, Wang said it’s a manual task that requires a lot of human effort and time. This technology will allow local, state and federal agencies to step in to make improvements.

“We really rely on accurate data collection for our road conditions, then the budget of the state agency will be optimally and cost-effectively used,” he said.