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Smart Intersections for Connected Vehicles

Zhenbo WangIn 2009, the Google Self-Driving Car Project made its debut, ushering in an age of interest and research in automated vehicles. Connected and automated vehicles, CAVs, have continued to capture the attention of researchers as they attempt to address some of the fundamental problems with connective vehicle technology. Zhenbo Wang, assistant professor of mechanical, aerospace and biomedical engineering is tackling one of these very problems: intersections. 

CAVs use a variety of technologies to communicate with other connected devices around them. This may include other cars, roadside assistance services, or even traffic signals. These communications could provide information that allow drivers and vehicles to adjust for improved efficiency, such as changes in acceleration to adapt to an approaching intersection. 

“What I’m doing with this project is trying to better control ground vehicles based on traffic signal changes,” said Wang. “The traffic signal will broadcast information to oncoming vehicles and we want to know how we can optimize the motion of the vehicle to, for example, minimize fuel consumption.” 

In additional to improved fuel efficiency, CAVs have major implications for road safety as well. According to the Tennessee Department of Safety & Homeland Security, there were more than 1,100 traffic fatalities in the state, a little more than ten percent of which occurred in the greater Knoxville area.  

Some of the primary causes of these accidents include distracted driving, driving under the influence, and speeding. In other words, human error is a major cause of fatal traffic accidents. CAVs may decrease fatal traffic accidents by using connections with other devices to reduce risk such as by communicating to oncoming vehicles that a car is about to run a red light.  

Wang’s StART team hopes to develop a control strategy for vehicles using traffic signal phase and timing data to make real time speed adjustments in response to information received. These adjustments will contribute to better fuel efficiency and a host of other benefits. 

Wang is working in collaboration with Tim LaClair, research and development engineer at ORNL, whose team has developed expertise in modeling, simulation, and control of CAVs. Their long-term goal is to develop a framework for controlling CAVs that works effectively with the large amounts of data generated by the traffic network. 

“Connected vehicles have the potential to revolutionize transportation,” said Wang. “To realize that potential we need to develop algorithms to control these vehicles in real time to reduce congestion, maximize fuel economy, and increase safety.”