Control of Energy Efficient Powertrain for Autonomous and Connected Vehicles in a Mixed Autonomous and Human Driving Environment
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In the near future, human drivers will share the roadways with fleets of autonomous vehicles. Significant energy savings might be achieved in autonomous vehicles if vehicle control systems integrate models of human driver behavior. Such savings would beneficially impact the economics of transportation, thereby increasing national prosperity. This project considers control of autonomous and connected vehicles in an on-road environment where both autonomous and human driving vehicles exist. The project will integrate novel models of human driver behavior into autonomous vehicle motion and powertrain control, thereby enabling simultaneous optimization of energy efficiency and driver safety. Experiments also examine the co-adaptive interaction between human drivers and autonomous vehicles on a closed-course proving ground, wherein system performance can be tested and verified safely. The project will involve an educational component that provides training to graduate, undergraduate, and high school students in conducting research. The project also develops animation software to help students understand the working principle of connected and autonomous vehicles.This research investigates new control methodologies that promise improved autonomous vehicle powertrain efficiency by integrating a novel model of human driving behavior, which is capable to predict the movement of human-driven vehicles and autonomous vehicles over long time periods. Methods include: a hazard-based modeling framework and rolling horizon mechanism that ensures accurate modeling of vehicle movements while considering driver and vehicle heterogeneity; experimental evaluations of how human driving behavior will impact the energy efficiency of autonomous vehicle control, and how changes in autonomous vehicle control strategy will impact human driving.This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.
