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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    Title :

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


    Additional title:

    Lect.Notes Mechanical Engineering



    Conference:

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



    Publication date :

    2020-02-13


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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