Trying to avoid traffic congestion to get home more quickly and save on gas? One day your car will do it for you.

Mechanical engineering professor Ardalan Vahidi, Ph.D., is designing vehicle-control systems that factor in terrain, traffic updates and more to give you unprecedented fuel efficiency and driving ease.

“Connected vehicles that communicate wirelessly to roadside infrastructure and data servers receive upcoming road information to increase their energy efficiency,” explains Vahidi. “For example, we have shown in city driving tests that fuel and time spent at traffic stops is saved by communicating traffic-signal timing to approaching vehicles that adjust their speeds accordingly.”

In the future, your car’s dashboard could actually countdown to a traffic-light change and house another indicator that shows your ideal speed. Vahidi’s team is the first to demonstrate real-time use of such information.

Beyond city driving, your car could manage its fuel system and speed by knowing the immediate terrain and the locations of other vehicles. Imagine you are driving a hybrid in the Colorado Rockies. Your GPS and onboard system know the terrain. Designed to save fuel, the car’s battery is completely discharged as you crest each peak, recharging as you brake on the way down, “We expect additional hybrid fuel efficiencies of up to 10 percent with terrain preview information,” says Vahidi.

Last year, Vahidi worked with the BMW Group Technology office in California on a pilot connected vehicle project using GPS data broadcast by San Francisco metro buses. The data analysis and predictions helped estimate traffic flow on the city streets. Vahidi’s team is working on predictive use of traffic flow information for planning more fuel-efficient trips and reducing traffic congestion. Going beyond existing traffic-prediction techniques, they seek methods that predict future state-of-traffic and traffic signals using probability and statistical models.

This research could enable the prediction of future congestion or measure the impact of a traffic-light-change plan. It should help further reduce energy use and optimally coordinate traffic control infrastructure, thus improving traffic flow. The research relies on real-time information from the vehicles and infrastructure, historical data and computational power of a back-end computing cluster. This work provides an alternative for managing congestion by relying mostly on information.

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