A team of UNT researchers working in the field of connected and autonomous vehicles (CAV) have earned $1 million in grant funding from the National Science Foundation this year for various CAV projects.
The first grant is $500,000 intended to help create workforce development training for researchers in CAV.
“We are training the research workforce,” says Qing Yang, an assistant professor in the Department of Computer Science and Engineering. “The training will be project-based, giving participants the opportunity to work on practical developments in the field and apply knowledge they’ve gained from the course and outside study.”
Yang and Song Fu plan to offer the training annually as well as in their summer research experiences for undergraduates and at international conferences. The team also plans to host a CAV workshop at UNT that will be open to the public, and hopes to offer undergraduate courses on autonomous vehicles in the future. A graduate-level course already exists.
“We are probably the first in the U.S. to propose to develop an autonomous vehicle cyber infrastructure,” says Fu, an associate professor at the college. “It’s very new. It’s combing all the new technologies, like self-driving cars, Internet of Things and also edge computing – all new technologies the industry is very interested in.”
The two researchers intend to create an industry advisory board that includes researchers from companies such as Fujitsu Communication Network, Texas Instruments and Microsoft Research, as well as government agencies like NSF, Texas Department of Transportation and researchers from other universities to ensure the training stays up to date with current trends and developments within the field.
The second grant is a $100,000 NSF EAGER award that will fund research into finding solutions for the security and privacy challenges faced in achieving cooperative perception among autonomous vehicles.
Yang and Fu’s project will explore how cooperative perception enables vehicles to exchange data to better see and detect objects in a vehicle’s pathway. The method is a useful technique for advancing autonomous vehicle safety, but with the current data protection measures in place, very few vehicles are likely to share the sensor data due to privacy concerns.
“The proposed technique is a generic solution that can be extended to offer data privacy protection for other machine learning methods,” says Yang. “It will enable vehicles to securely share useful sensor data between each other, which can be extremely beneficial for extending the line of sight and field of view of autonomous vehicles, helping secure public safety and advance smart transportation.”
Earlier this year, Yang and Fu were awarded $400,000 from the NSF to help make data collection and processing by autonomous vehicles more efficient.