Autonomous Cars

Drones and rovers save the day at Raytheon Autonomous Vehicle Competition 2025

Jennifer Pocock — Thu, 01 May 2025 18:18:35 +0000

Drones and rovers save the day at Raytheon Autonomous Vehicle Competition 2025 Jennifer Pocock Thu, 05/01/2025 - 14:18

In This Story

As the saying goes, if you love something, set it free. If it maps an area, finds a target, delivers a package, and comes back, the trophy is yours forever. Or something like that.

The Raytheon Autonomous Vehicle Competition Team poses with their ground and air robots. Photo by Jenn Pocock

The black quadcopter drone surveyed the field in two quick swoops against a cloud-heavy sky. In just a matter of seconds, it picked up a signal and flew to the sidelines where it landed itself. A small ground rover took off, bumping over the grass to drop a first aid kit a few feet from the goal. Team members on the side of the field exploded with cheers and high fives.

The entire search and rescue took less than two minutes.

Programmer Qeon Edwards jumped with joy at just how well and how quickly their vehicles performed. “What, you doubted us?” he laughed, obviously relieved.

It was late in April when five teams gathered at the Xelevate drone ecosystem field in Leesburg, Virginia, to test their autonomous programming mettle via ground and air vehicles. This was ��’s second year in the competition, which they won last year. As the reigning champs, they had a lot to prove.

A quadcopter takes off from the starting line to search for its target, as the rover and judges stand by. Photo by Jenn Pocock

The challenge rules change every year, so the team can’t just copy their predecessors’ codes. “Last year was basically a game of tag,” said Xuesu Xiao, assistant professor of computer science and the team’s faculty lead. They had to fly a drone around to squirt water on their opponents’ ground vehicles while avoiding their own.

This year, instead of trying to shoot others down, they had to perform a rescue mission: Find a randomly assigned “survivor” in a field of battle and send first aid. The faster the find and the closer the kit, the higher the score.

“Our strategy was for a drone to scout the area first, find the target, and then drive another ground vehicle robot to deliver the first-aid kit,” said Xiao.

The team programmed six flying drones and a ground robot that communicated with one another—and nothing else. That means that the programmers had no immediate control over the vehicles; they set their autonomous creations free and hoped that they did a good job coding them.

Judges measure the distance from the target to the rover-delivered first aid kit. Photo by Jenn Pocock

Overall, their robots performed exceptionally, managing to cut their delivery time and distance to less than a minute and 57 inches, respectively. They were just nudged out, however, by first-place University of Southern Florida and second-place Virginia Tech.

“It was definitely very tight between the top three!” said Cameron Nowzari, associate professor in electrical and computer engineering. “There was only a two-point difference between first and second, and one point between second and third.”

Still, the team wasn’t disappointed with how they performed. Their robots were a labor of love, and they met the challenge in a big way.

Topics

Autonomous Cars

robotics and autonomous systems

competitions

Electrical and Computer Engineering

Students

Boom crash: Mason researcher receives half million NSF grant that could steer AI safely

Rena Malai — Fri, 24 Feb 2023 20:12:21 +0000

Boom crash: Mason researcher receives half million NSF grant that could steer AI safely Rena Malai Fri, 02/24/2023 - 15:12

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ThanhVu Nguyen

AI is everywhere. It emphatically wends its way and creeps into daily life, from parking cars to vending machines. In some cases, the use of AI needs to be closely monitored to ensure all safety and precautionary measures are taken into consideration.

Thanhvu H. Nguyen, one of the newest professors in the Department of Computer Science, arrived at �� in 2021 and knew of Mason’s diversity and proximity to one of the largest tech hubs in the nation.

Nguyen recently won the National Science Foundation (NSF) CAREER Award "NeuralSAT: A Constraint-Solving Framework for Verifying Deep Neural Networks.” Nguyen will work with his student Hai Duong, and a select team of students to develop technology that ensures AI machine learning is robust, safe, and unbiased.

“In certain scenarios, where AI is used for things like controlling airplanes or autonomous vehicles, any mistake could prove fatal,” says Nguyen. “This grant work will look at the deep neural networks (DNN) embedded in AI/machine learning technology and develop technology to prevent errors and ensure safety.”

According to the project’s abstract, the use of DNNs - a layered network that processes complex data - have emerged as an effective approach to tackling real-world problems. However, just like traditional software, DNNs can have ‘bugs’ and be attacked. 

This can be especially concerning, when it comes to the use of AI in driving cars, for example. Although it could eliminate some human error, Nguyen says any faulty DNNs could throw an AI program – and the car – off the road when it comes to something unexpected, like bad weather and icy roads.

“We all depend on AI, whether we realize it or not,” says Nguyen. “It’s a part of our lives. Whatever I can do to make it safer, is a benefit to my family and the society.”

Since coming to Mason, he has helped to bring in around $1.73M in external funding. Nguyen is excited to work with his students and the group of computer science faculty, noting how everyone brings a fresh perspective to the table.

The project runs for five years with a total of $510,509 in anticipated funding. Nguyen sees the project as helping society as a whole by improving the reliability of systems embedding DNNs. The research conducted throughout the project will allow AI and machine learning researchers and users to improve their DNNs and deploy them with confidence. 

Topics

Taking it to the streets: Commonwealth Cyber Initiative funds research for autonomous cars

Martha Bushong — Thu, 25 Mar 2021 20:34:26 +0000

Taking it to the streets: Commonwealth Cyber Initiative funds research for autonomous cars Martha Bushong Thu, 03/25/2021 - 16:34

Topics

Autonomous Cars

Commonwealth Cyber Initiative (CCI)

Cybersecurity

Artificial Intelligence

In This Story

Duminda Wijesekera

Professor Duminda Wijesekera (center) and his team have installed cameras and sensors on two cars to test their capabilities. Photo by Creative Services.

Driving three decked-out Toyota Corollas, researchers are taking to the busy streets of northern Virginia and Washington, D.C. to test sensors and cameras they’ve installed to simulate a self-driving car. “My number one goal is to take on Dupont Circle, but we’ll see if we can overcome that crazy obstacle,” says Duminda Wijesekera, a professor of computer science at ��’s Volgenau School of Engineering.

Wijesekera, the project lead, is using artificial intelligence to help create smarter car sensors. His research seeks to improve an autonomous cars’ ability to recognize potential hazards at night, in severe weather, and in construction conditions, making the roads safer for everyone.

He’s drawing upon the expertise of the CCI network by engaging several professors across Virginia, including Sachin Shetty, associate professor at Old Dominion University, Ying Wang, senior engineer at CCI’s Hub located at Virginia Tech’s Arlington Research Center, Vijay Shah, research assistant professor at Virginia Tech, and Jeffrey Reed, a Virginia Tech engineering professor and CCI’s chief technology officer. What the team learns could be applied to other 5G-connected technology in the future.

It’s this type of complex problem that CCI was designed to solve, says Luiz DaSilva, CCI executive director. “CCI brings together 320 researchers from 39 higher education institutions across Virginia to focus on research, innovation, and workforce development at the intersection of security, autonomous systems, and data.”

CCI’s funding allowed Wijesekera to purchase three Toyota Corollas as testbeds. Prior to receiving the grant and vehicles from CCI, Wijesekera was volunteering his own car to test the AI technology on the streets of Washington, D.C.

“The ability to test innovative solutions using real equipment is crucial,” DaSilva says. “It often reveals practical issues that the solution needs to deal with that may not be clear from simulations and theoretical models. It also adds a layer of credibility to results.”

How the technology works

Traditionally, autonomous vehicles create obstacle-free driving paths using image recognition software. Sometimes, the software can fail to recognize obstacles in the road in low-light conditions such as dusk and dawn, rain, and construction zones. This can result in the software mistakenly creating driving paths with dangerous obstacles in the way.

Wijesekera and his team are using AI to make sure that doesn’t happen. “To combat this issue, first, I use two cameras - a color camera and an Infrared camera,” Wijesekera explains. “Then I take data from both cameras and ‘fuse them’ using a deep learning process called AI.”

Future applications

Reed says findings from Wijesekera’s research can change the future of more than just autonomous vehicles. “This kind of research can apply to biomedical technologies, smart homes, and even how we teach in our classrooms,” he says.

Shetty is working on developing a privacy-preserving 5G network for tracking trucks in the Port of Virginia, which also includes some work of Wijesekera’s findings. “The privacy-preserving 5G network from Wijesekera’s project would ensure that when we track trucks, we do not track any sensitive information that could be weaponized by attackers. Even if attackers sniff the information, the driver’s privacy is protected.”

A safer road forward

Distracted driving claimed the lives of 1,730 drivers, 605 passengers, 400 pedestrians, and 77 bicyclists in 2018 alone, according to the National Highway Traffic Safety Administration (NHTSA).

“Just like automated-trains, an entirely automated-car world would be cleaner, safer, and greener in the long run,” says Wijesekera. “I’m hoping this is the first step of many towards making driving totally handled by machines.”