Vehicle state estimation represents a prerequisite for ADAS (Advanced Driver-Assistant Systems) and, more in general, for autonomous driving. In particular, algorithms designed for path or trajectory planning require the continuous knowledge of some data such as the lateral velocity and heading angle of the vehicle, together with its lateral position with respect to the road boundaries. Vehicle state estimation can be assessed by means of extended and unscented Kalman filters (EKF and UKF, respectively), that have been well treated in the literature. Referring to an experimental case study, the presented work deals with the design and the real time implementation of two different adaptive Kalman filters for vehicle sideslip and positioning estimation. Accuracy have been assessed by means of an automotive optical sensor.


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    Title :

    Vehicle state estimation based on Kalman filters


    Contributors:
    Bersani, M. (author) / Vignati, M. (author) / Mentasti, S. (author) / Arrigoni, S. (author) / Cheli, F. (author)


    Publication date :

    2019-07-01


    Size :

    13046133 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Dynamics Estimation Using Kalman Filters

    Venhovens, P. / Naab, K. / Society of Automotive Engineers of Japan | British Library Conference Proceedings | 1998


    Vehicle dynamics estimation using Kalman filters

    Venhovens, P.J.T. / Naab, K. | Tema Archive | 1999


    Vehicle Dynamics Estimation Using Kalman Filters

    Venhovens, P.J.Th | Online Contents | 1999


    Vehicle dynamics estimation using Kalman filters

    Vennhovens, P. / Naab, K. | Tema Archive | 1998


    Vehicle dynamics estimation using Kalman filters

    Venhovens,P.J. / Naab,N. / Bayerische Motorenwerke,BMW,Muenchen,DE | Automotive engineering | 1999