Technology in autonomous cars is taking another step forward with the help of a Clemson University team that is wrapping up three years of research into how the vehicles can save energy.
Ardalan Vahidi, a mechanical engineering professor, said he and his team created algorithms that help autonomous, wirelessly-connected vehicles anticipate the behavior of other vehicles to reduce braking. The less a vehicle brakes, the less energy it wastes through heat and the more energy efficient it becomes.
The team found its algorithms resulted in energy savings ranging from 8-23%, depending on the scenario.
“The big picture is that we’ll have more opportunities to save energy when autonomous cars that are connected to the internet and other wireless networks start talking to each other,” Vahidi said. “There are a lot of groups focusing on autonomous vehicles, but the focus on how they can be energy efficient is not as mainstream. That’s our niche.”
The research underscores the role Clemson engineers are playing in shaping the future of transportation as vehicles become more automated and a growing number run on electricity instead of gas or diesel.
The team involved in the recent research tested its algorithms on two separate autonomous cars, a gas-powered Mazda and an electric Nissan, both connected to the same wireless network, allowing them to send and receive data, such as speed and heading. The cars took turns traveling a closed track in southern Greenville County so that only one real car was on the track at any given time.
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Researchers used computer simulations to create “ghost” vehicles in front of and behind the Mazda and Nissan, making them think they were in traffic. It allowed researchers to be more aggressive and try difficult scenarios because any collision would be with a ghost car that caused no damage or injury.
Some of the ghost vehicles were autonomous, and some were driven by computer-simulated human drivers. Each test consisted of seven laps around the track with U-turns at both ends that caused slow-downs and often traffic jams.
The Mazda and Nissan saved more energy when following autonomous ghost vehicles than the ghost vehicles driven by simulated human drivers.
The autonomous ghost vehicles shared their intentions with the Mazda and Nissan several seconds ahead of time, giving the ghost vehicle and the real-life vehicle a chance to coordinate braking. The simulated human drivers, like real-life human drivers, were less predictable, giving the vehicles less time to work together.
“The experimental vehicles– the Mazda and the Nissan–saved 20-23% energy when following a simulated vehicle that was automated and connected to a wireless network,” Vahidi said. “When following a simulated vehicle driven by a simulated human, we measured 8-12% energy savings compared to human driver baselines.”
While it wasn’t part of the research, the team observed something that bodes well for commuters who would rather not sit in traffic. Braking often caused phantom traffic jams. But having the Mazda and Nissan anticipate what the preceding ghost vehicles were going to do smoothed out traffic flow, helping alleviate stop-and-go congestion.
Vahidi said he and his team are now writing a paper that will detail more of the results. The paper brings to an end three years of research funded with $1.16 million by the Department of Energy.
It’s been a big year for Vahidi. Not only did he and his team finish their research, he was also recently named Fellow of The American Society of Mechanical Engineers.
“Dr. Vahidi’s recognition is a high honor that serves as a testament to his significant engineering achievements,” Kelkar said. “The autonomous vehicle research that he and his team have recently completed adds to his impressive list of credentials. I congratulate Dr. Vahidi on all his success.”
Vahidi served as principal investigator on the research. The co-principal investigators were Yunyi Jia, the McQueen Quattlebaum Assistant Professor of automotive engineering, and Beshah Ayalew, a professor of automotive engineering.
Alireza Fayazi and Nawaz Ali, both postdoctoral researchers, contributed to the research, along with Ph.D. students Austin Dollar, Tyler Ard, Longxiang Guo and Nathan Goulet. The team worked with Joachim Taiber at the International Transportation Center and with Dominik Karbowski from Argonne National Laboratory.
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