In this paper, we present a novel fog visibility range estimation algorithm for autonomous driving application. The proposed method is based on a hybrid neural network for which localized image entropy and image-based features are given as an input. While entropy used for visibility estimation, image-based features act as a basis for distance calibration. The proposed network is tested on real data collected and calibrated on-road and performs very well with respect to accuracy. The proposed algorithm is also of low complexity and provides the result on a near real-time basis.


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    Titel :

    Real-time fog visibility range estimation for autonomous driving applications


    Beteiligte:


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    2949227 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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