NDSU Drives Itself Forward

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Three North Dakota State professors, Ying Huang, Pan Lu and Raj Bridgelall, recently presented their paper, “Road Sensor Network for Smart City Applications,” at a conference in Denver, Colorado. The paper follows preliminary findings on road sensors and how it could be used to aid self-driving cars.

Raj Bridgelall, assistant professor of transportation and logistics, said that for autonomous vehicles to work it “will require that we have some smart infrastructure.”

The artificial intelligence (AI) in the driver’s seat is “not going to be able to necessarily perceive the environment as well as we humans can,” according to Bridgelall.

Bridgelall likened today’s AI-driven vehicles to babies that haven’t learned to walk and can barely see yet.

“These vehicles and smart cities will coexist with human drivers and pedestrians … other human factors will come into play,” Brigdelall said. “What we’re trying to do is enhance the environment for both the human drivers as well as the artificial intelligence drivers.”

The team recently revealed the beginning stages of their research at the Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation conference.

Bridgelall said the project was received well at the conference and that he saw “a positive movement to help to mitigate future problems.”

The initial idea for the project was to collaborate with the civil engineering “to deploy some of the sensors they have devolved,” according to Brigdelall.

The sensors the professors are collecting data from were embedded in a part of I-94 where there is a testing loop, Pan Lu, associate professor of transportation and logistics, said.

Lu said the “sensor is under actual traffic and also in the loop.”

“We had to design a sensor network to actually collect the data we need.” Lu said the team uses multiple sensors to collect proper data.

Lu said she is excited by both the possibility of helping self-driving cars become a reality and by the traditional application because it could help solve congestion by providing information to drivers.

“It’s basically information flow,” Lu said. “By improving the information flow, we can assist drivers, driver-less vehicles, autonomous vehicles, drive better, and also we can provide better information to human drivers to make better decisions.”

Another aspect of this project that Lu finds exciting are the sensors.

According to Lu, the sensors they put on the roads are widely used but “strangely very few of them apply to the transportation area.”

Lu said the professors are still in the process of collecting data from the sensors and that she is in charge of processing the data once it is all in.

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