This paper aims to extend the potential of Adaptive Cruise Control (ACC) technology by forecasting the behaviour of the traffic using physical models. A preceding vehicle prediction algorithm is proposed estimating the future kinematics of the vehicles with an extended car-following model. The prediction is tested with two cruise control frameworks: An anticipative car-following (ACF) control where the prediction is used to anticipate the drivers reaction and an economical predictive cruise control algorithm, named as eco-PCC, where the estimation of the preceding vehicle is added as a constraint to an optimal control problem (OCP) to minimise the fuel consumption over a receding horizon. The algorithms are tested in SUMO with a validated simulation environment of the city of Bologna. Results compare the benefits of the ACF approach and the eco-PCC algorithm with a baseline ACC without predictions. Several prediction horizons have been tested, highlighting the trade-off between prediction accuracy and the energy improvements. Experiments in an engine testbed presented benefits up to 8.03% in fuel consumption for the ACF control and up to 24.14% for the eco-PCC algorithm when compared to the baseline ACC without prediction.
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