But cars communicating vehicle-to-vehicle can calculate possible obstacles in the road at increasing distances -- and their synchronous reactions can prevent traffic jams and car accidents.
"On the freeway, one bad decision propagates other bad decisions," Zhang said. "If we can consider what's happening 300 meters in front of us, it can really improve road safety. It reduces congestion and accidents."
Zhang's research asks how vehicles connect to other vehicles, how those vehicles make decisions together based on data from the driving environment and how to integrate disparate observations into a network.
Zhang and Zhao created a data-driven, optimization-based control model for a "platoon" of automated vehicles driving cooperatively under uncertain traffic conditions. Their model, based on the concept of forecasting the forecasts of others, uses streaming data from the modeled vehicles to predict the driving states (accelerating, decelerating or stopped) of preceding platoon vehicles. The predictions are integrated into real-time, machine-learning controllers that provide onboard sensed data. For these automated vehicles, data from controllers across the platoon become resources for cooperative decision-making.
The model indicates controllers could help vehicles maintain constant time gaps between themselves to reduce congestion and traffic accidents and could also conserve energy by reducing the need to accelerate and decelerate.
The next phase of Zhang's CAREER Award-supported research is to test the model's simulations using actual connected, autonomous vehicles. Among the locations well-suited to this kind of testing is Michigan Tech's Keweenaw Research Center, a proving ground for autonomous vehicles, with expertise in unpredictable environments.
Ground truthing the model will enable data-driven, predictive controllers to consider all kinds of hazards vehicles might encounter while driving and create a safer, more certain future for everyone sharing the road.