The visual predictor of a drivable surface friction ahead of the vehicle is presented. The image recognition neural network is trained in self-supervised fashion, as an alternative to tedious, error-prone, and subjective human annotation. The training images are labelled automatically by surface friction estimates from vehicle response during ordinary driving. The Unscented Kalman Filter algorithm is used to estimate tire-to-road interface friction parameters, taking into account the highly nonlinear nature of tire dynamics. Finally, the overall toolchain was validated using an experimental subscale platform and real-world driving scenarios. The resulting visual predictor was trained using about 3 000 images and validated on an unseen set of 800 test images, achieving 0.98 crosscorrelation between the visually predicted and the estimated value of surface friction.


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    Titel :

    Self-Supervised Learning of Camera-based Drivable Surface Friction


    Beteiligte:
    Vosahlik, David (Autor:in) / Cech, Jan (Autor:in) / Hanis, Tomas (Autor:in) / Konopisky, Adam (Autor:in) / Rurtle, Tomas (Autor:in) / Svancar, Jan (Autor:in) / Twardzik, Tomas (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    1815173 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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