How do autonomous vehicles affect the flow of traffic?
According to a new research study, adding just a few self-driving vehicles to the road could eliminate stop-and-go driving conditions.
Some of the world’s major cities are plagued with stop-and-go driving conditions; and sitting in traffic can be the worst.
According to a new research study, adding just a few autonomous vehicles to the road will eliminate stop-and-go driving as well as limit the risk of accidents and increase fuel efficiency. By examining the effects of autonomous vehicles on overall traffic patters, researchers are indicating that self-driving cars may have more of a positive impact that previously thought.
“Our experiments show that with as few as 5 percent of vehicles being automated and carefully controlled, we can eliminate stop-and-go waves caused by human driving behavior,” says Daniel B. Work, assistant professor at the University of Illinois at Urbana-Champaign, a lead researcher in the study.
The use of autonomous vehicles to regulate traffic flow is the next innovation in the rapidly evolving science of traffic monitoring and control, Work said. Just as fixed traffic sensors have been replaced by crowd-sourced GPS data in many navigation systems, the use of self-driving cars is poised to replace classical freeway traffic control concepts like variable speed limits. Critical to the success of this innovation is a deeper understanding of the dynamic between these autonomous vehicles and the human drivers on the road.
The team took to the streets in Tucson, Arizona, where a single self-driving vehicle circled a track continuously with at least 20 other human-driven cars. Under normal circumstances, human drivers naturally create stop-and-go traffic, even in the absence of bottlenecks, lane changes, merges or other disruptions, Work explains.
This phenomenon is called the “phantom traffic jam.”
Researchers found that by controlling the pace of the autonomous car in the study, they were able to smooth out the traffic flow for all the cars. The researchers were able to determine that by adding a small percentage of self-driving cars to the road, driving conditions were improved, eliminating waves and reducing the total fuel consumption by up to 40 per cent.
“Before we carried out these experiments, I did not know how straightforward it could be to positively affect the flow of traffic,” says Jonathan Sprinkle, the Litton Industries John M. Leonis Distinguished Associate Professor in Electrical and Computer Engineering at the University of Arizona. “I assumed we would need sophisticated control techniques, but what we showed was that controllers which are staples of undergraduate control theory will do the trick.”
The research indicates that new advancements in driving tech has the potential to change traffic patterns. Related technologies, like adaptive cruise control, could be another factor in effecting change.
“Fully autonomous vehicles in common traffic may be still far away in the future due to many technological, market and policy constraints,” explains Benedetto Piccoli, the Joseph and Loretta Lopez Chair Professor of Mathematics at Rutgers University. “However, increased communication among vehicles and increased levels of autonomy in human-driven vehicles is in the near future.”
The near future with only a few autonomous vehicles on the road is more challenging than the far future in which all vehicles are connected, says Benjamin Seibold, associate professor of Mathematics at Temple University.
“The proper design of autonomous vehicles requires a profound understanding of the reaction of humans to them,” Seibold adds, “and traffic experiments play a crucial role in understanding this interplay of human and robotic agents.”
The researchers say the next step will be to study the impact of autonomous vehicles in denser traffic with more freedom granted to the human drivers, such as the ability to change lanes.
The study was conducted by a multi-disciplinary team of researchers with expertise in traffic flow theory, control theory, robotics, cyber-physical systems, and transportation engineering. Principal investigators (PIs) were: Benedetto Piccoli, the Joseph and Loretta Lopez Chair Professor of Mathematics at Rutgers University, Camden; Benjamin Seibold, associate professor of Mathematics at Temple University; Jonathan Sprinkle, the Litton Industries John M. Leonis Distinguished Associate Professor in Electrical and Computer Engineering at the University of Arizona, Tucson; and Daniel B. Work, assistant professor in Civil and Environmental Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign.