  This paper experimentally demonstrates the effectiveness of an anticipative
car-following algorithm in reducing energy use of gasoline engine and electric
Connected and Automated Vehicles (CAV), without sacrificing safety and traffic
flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which
experimental CAVs driven on a track interact with surrounding virtual traffic
in real-time. We explore the energy savings when following city and highway
drive cycles, as well as in emergent highway traffic created from
microsimulations. Model predictive control handles high level velocity planning
and benefits from communicated intentions of a preceding CAV or estimated
probable motion of a preceding human driven vehicle. A combination of classical
feedback control and data-driven nonlinear feedforward control of pedals
achieve acceleration tracking at the low level. The controllers are implemented
in ROS and energy is measured via calibrated OBD-II readings. We report up to
30% improved energy economy compared to realistically calibrated human driver
car-following without sacrificing following headway.
