Determining the surface on which a vehicle is moving is vital information for improving active safety systems. Performing the surface classification or estimating adherence through tire slippage can lead to late action in possible risk situations. Currently, approaches based on image, sound, and vibration analysis are emerging as a viable alternatives, though sometimes complex. This work proposes a methodology based on the use of low-cost accelerometers combined with Deep Learning techniques to achieve continuous surface estimation. The performance of the proposed system is evaluated with real tests, where high percentages of accuracy are obtained in the classification task.


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    Titel :

    Low-Cost Surface Classification System Supported by Deep Neural Models


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Orlova, Anna (Herausgeber:in) / Cole, David (Herausgeber:in) / Sánchez, Ignacio (Autor:in) / Velasco, Juan M. (Autor:in) / Castillo, Juan J. (Autor:in) / Sánchez, Miguel (Autor:in) / Cabrera, Juan A. (Autor:in)

    Kongress:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2021 August 17, 2021 - August 19, 2021



    Erscheinungsdatum :

    2022-08-06


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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