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Road Condition Estimation Based on Spatio-Temporal Reflection Models

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

Automated road condition estimation is a crucial basis for Advanced Driver Assistance Systems (ADAS) and even more for highly and fully automated driving functions in future. In order to improve vehicle safety relevant vehicle dynamics parameters, e.g. last-point-to-brake (LPB), last-point-to-steer (LPS), or vehicle curve speed should be adapted depending on the current weather-related road surface conditions. As vision-based systems are already integrated in many of today’s vehicles they constitute a beneficial resource for such a task. As a first contribution, we present a novel approach for reflection modeling which is a reliable and robust indicator for wet road surface conditions. We then extend our method by texture description features since local structures enable for the distinction of snow-covered and bare road surfaces. Based on a large real-life dataset we evaluate the performance of our approach and achieve results which clearly outperform other established vision-based methods while ensuring real-time capability.

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Acknowledgements

The proposed method in this paper was developed in a joint research project funded by Continental Teves AG & Co. oHG.

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Authors and Affiliations

  1. Computer Vision Group, Friedrich Schiller University Jena, Jena, Germany

    Manuel Amthor & Joachim Denzler

  2. Advanced Engineering, Continental Teves AG & Co. oHG, Frankfurt a.M., Germany

    Bernd Hartmann

Authors
  1. Manuel Amthor
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  2. Bernd Hartmann
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  3. Joachim Denzler
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Corresponding author

Correspondence to Manuel Amthor .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Amthor, M., Hartmann, B., Denzler, J. (2015). Road Condition Estimation Based on Spatio-Temporal Reflection Models. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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