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Gaussian learning‐based fuzzy predictive cruise control for improving safety and economy of connected vehicles

This study considers an adaptive cruise control problem of connected vehicles in the vehicular ad-hoc network and proposes a Gaussian learning-based fuzzy predictive cruise control approach to enhance the fuel efficiency and safety of the connected vehicles in a vehicle-following scenario. First, a Gaussian process regression model is introduced and trained with real data to estimate the future acceleration of the preceding vehicle over the prediction horizon. Moreover, with assessing traffic scenarios, the weights characterising the importance of individual performance are adjusted by a fuzzy decision method in real time. Then a fuzzy predictive cruise controller is obtained by online solving a constrained receding horizon optimal control problem with a changing cost function and acceleration prediction of the preceding vehicle. Finally, through CarSim/Simulink co-simulation, it is shown that the proposed approach has an improvement in fuel economy and safety compared with conventional predictive cruise control algorithms.