The knowledge of vehicle dynamical states and parameters plays a crucial role in vehicle stability control systems and, specifically, Vehicle Sideslip Angle (VSA) is an essential factor for active safety control systems. However, the demand for real-time knowledge of this parameter is not practical, due to technical and economic reasons. This paper proposes a novel Interacting Multiple Model Unscented Kalman Filter (IMMUF) to estimate VSA, without tire-road friction coefficient information, and integrating three Unscented Kalman Filters (UKF) to estimate vehicle system models in three different driving conditions (dry, wet, and damp asphalt), characterized by a specific coefficient and modeled through a 2-DOFs single-track vehicle model with a Dugoff tire model. A Monte Carlo analysis has been performed on a wide range of non-trivial driving scenarios and vehicle maneuvers, implemented on a 7-DOFs vehicle model. The results of the estimation have been compared to those of a single UKF, in order to validate the effectiveness of the proposed solution and to highlight the worst performances of a single filter solution in hard driving conditions, justifying the specific Multiple Model solution adopted.


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    Titel :

    Enhancing ADS and ADAS Under Critical Road Conditions Through Vehicle Sideslip Angle Estimation via Unscented Kalman Filter-Based Interacting Multiple Model Approach


    Weitere Titelangaben:

    Mechan. Machine Science


    Beteiligte:
    Niola, Vincenzo (Herausgeber:in) / Gasparetto, Alessandro (Herausgeber:in) / Quaglia, Giuseppe (Herausgeber:in) / Carbone, Giuseppe (Herausgeber:in) / Battistini, Simone (Autor:in) / Brancati, Renato (Autor:in) / Lui, Dario Giuseppe (Autor:in) / Tufano, Francesco (Autor:in)

    Kongress:

    The International Conference of IFToMM ITALY ; 2022 ; Naples, Italy September 07, 2022 - September 09, 2022



    Erscheinungsdatum :

    06.08.2022


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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