Hashemi, EhsanPirani, MohammadFidan, BarisKhajepour, AmirChen, Shih-KenLitkouhi, Baktiar2017-12-152017-12-152017-06https://doi.org/10.1109/IVS.2017.7995798http://hdl.handle.net/10012/12731© IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners’ velocity estimates, which are obtained by an unscented Kalman filter. The corners’ estimates from a Kalman observer, which is formed by integrating the model-based and kinematic-based velocity estimation approaches, are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Road tests confirm the robustness of the methods independent of the powertrain configuration on surfaces with various friction conditions in pure and combined-slip maneuvers, which are arduous for the current vehicle state estimators.enOpinion dynamicsDistributed estimationUnscented Kalman filterRobust observer designConference Paper