Designing the Loss Function of Vehicle Speed Predictors to Enhance Ecological Adaptive Cruise Control Performance

This paper presents a novel strategy for designing the loss function of data-driven vehicle speed predictors to increase the energy efficiency of an ecological adaptive cruise control (eco-ACC) system. A unique eco-ACC simulation is designed to investigate the influence of step-wise accuracy on the performance of eco-ACC. In these simulations, uncertainty is added at only one prediction step of a prediction horizon during an entire trip, and the eco-ACC performance is evaluated in terms of acceleration minimization. A weighted-mean-squared error is chosen as the loss function of vehicle speed predictors, where its weights are tuned based on the quantified influence of uncertainty. The shape of the weight function is formed as a χ
²
distribution with respect to prediction time. The proposed loss function is used for training an example data-driven speed predictor using polynomial regression. Simulation results show that using the proposed loss function allows energy efficiency to be enhanced by up to 5.4% more than using the conventional loss function, namely, mean-squared errors.