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This paper experimentally demonstrates the effectiveness of an anticipativecar-following algorithm in reducing energy use of gasoline engine and electricConnected and Automated Vehicles (CAV), without sacrificing safety and trafficflow. We propose a Vehicle-in-the-Loop (VIL) testing environment in whichexperimental CAVs driven on a track interact with surrounding virtual trafficin real-time. We explore the energy savings when following city and highwaydrive cycles, as well as in emergent highway traffic created frommicrosimulations. Model predictive control handles high level velocity planningand benefits from communicated intentions of a preceding CAV or estimatedprobable motion of a preceding human driven vehicle. A combination of classicalfeedback control and data-driven nonlinear feedforward control of pedalsachieve acceleration tracking at the low level. The controllers are implementedin ROS and energy is measured via calibrated OBD-II readings. We report up to30% improved energy economy compared to realistically calibrated human drivercar-following without sacrificing following headway.

Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results