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Road Condition Estimation Based on Spatio-Temporal Reflection Models

  • Manuel Amthor
  • Bernd Hartmann
  • Joachim Denzler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

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.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Manuel Amthor
    • 1
  • Bernd Hartmann
    • 2
  • Joachim Denzler
    • 1
  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany
  2. 2.Advanced EngineeringContinental Teves AG & Co. oHGFrankfurt a.M.Germany

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