Predictive cruise control based on short traffic forecast in an urban simulation environment

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.