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

Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


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.


Computer vision Road condition Classification 



The authors would like to thank the German Research Foundation (DFG) for founding this project.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Mechatronic SystemsGottfried Wilhelm Leibniz UniversitätHanoverGermany

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