In order to achieve the goal of autonomous driving, a precise perception of the vehicle’s environment is required. In particular, the weather-related road condition has a major influence on vehicle dynamics and thus on driving safety.

    In this paper, we compare Deep Convolutional Neural Networks of different computational effort, namely Inception-v3, GoogLeNet and the much smaller SqueezeNet, for classification of road surface and its weather-related condition. Previously, different regions of interest were compared in order to provide the networks with optimal input data.


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    Titel :

    Classification of Road Surface and Weather-Related Condition Using Deep Convolutional Neural Networks


    Weitere Titelangaben:

    Lect.Notes Mechanical Engineering


    Beteiligte:
    Klomp, Matthijs (Herausgeber:in) / Bruzelius, Fredrik (Herausgeber:in) / Nielsen, Jens (Herausgeber:in) / Hillemyr, Angela (Herausgeber:in) / Busch, Alexander (Autor:in) / Fink, Daniel (Autor:in) / Laves, Max-Heinrich (Autor:in) / Ziaukas, Zygimantas (Autor:in) / Wielitzka, Mark (Autor:in) / Ortmaier, Tobias (Autor:in)

    Kongress:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2019 ; Gothenburg, Sweden August 12, 2019 - August 16, 2019



    Erscheinungsdatum :

    2020-02-13


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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