Open AccessGaussian learning‐based fuzzy predictive cruise control for improving safety and economy of connected vehicles
Author(s) -
He Defeng,
Peng Binbin
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0452
Subject(s) - carsim , cruise control , model predictive control , acceleration , cruise , gaussian process , fuzzy logic , controller (irrigation) , control theory (sociology) , computer science , engineering , gaussian , vehicle dynamics , automotive engineering , control (management) , artificial intelligence , aerospace engineering , agronomy , physics , classical mechanics , quantum mechanics , biology
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.