Advertisement

Road Condition Estimation Based on Spatio-Temporal Reflection Models

  • Manuel AmthorEmail author
  • 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.

References

  1. 1.
    Alonso, J., López, J.M., Pavón, I., Recuero, M., Asensio, C., Arcas, G., Bravo, A.: On-board wet road surface identification using tyre/road noise and support vector machines. Appl. Acoust. 76, 407–415 (2014)CrossRefGoogle Scholar
  2. 2.
    Brust, C.A., Sickert, S., Simon, M., Rodner, E., Denzler, J.: Convolutional patch networks with spatial prior for road detection and urban scene understanding. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 510–517 (2015)Google Scholar
  3. 3.
    Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. (ML) 36, 3–42 (2006)CrossRefGoogle Scholar
  4. 4.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. Syst. Man Cybern. (SMC) 3, 610–621 (1973)CrossRefGoogle Scholar
  5. 5.
    Irschik, D., Stork, W.: Road surface classification for extended floating car data. In: International Conference on Vehicular Electronics and Safety (ICVES), pp. 78–83 (2014)Google Scholar
  6. 6.
    Johansson, R.: Vision zero-implementing a policy for traffic safety. Saf. Sci. 47, 826–831 (2009)CrossRefGoogle Scholar
  7. 7.
    Jokela, M., Kutila, M., Le, L.: Road condition monitoring system based on a stereo camera. In: International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 423–428 (2009)Google Scholar
  8. 8.
    Jonsson, P.: Classification of road conditions: From camera images and weather data. In: International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 1–6 (2011)Google Scholar
  9. 9.
    Kawai, S., Takeuchi, K., Shibata, K., Horita, Y.: A method to distinguish road surface conditions for car-mounted camera images at night-time. In: International Conference on ITS Telecommunications (ITST), pp. 668–672 (2012)Google Scholar
  10. 10.
    Lüke, S., Rieth, P., Darms, M.: From brake assist to autonomous collision avoidance. In: FISITA World Automotive Congress (FISITA) (2008)Google Scholar
  11. 11.
    Malvar, H.S., He, L.-W., Cutler, R.: High-quality linear interpolation for demosaicing of bayer-patterned color images. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 482–485 (2004)Google Scholar
  12. 12.
    McFall, K., Niittula, T.: Results of av winter road condition sensor prototype. In: International Road Weather Congress (SIRWEC) (2002)Google Scholar
  13. 13.
    Mondal, A., Sharma, A., Yadav, K., Tripathi, A., Singh, A., Piratla, N.: Roadeye: A system for personalized retrieval of dynamic road conditions. In: International Conference on Mobile Data Management (MDM), pp. 297–304 (2014)Google Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal. Mach. Intell. (PAMI) 24, 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    Omer, R., Fu, L.: An automatic image recognition system for winter road surface condition classification. In: International Conference on Intelligent Transportation Systems (ITSC), pp. 1375–1379 (2010)Google Scholar
  16. 16.
    Pyykonen, P., Laitinen, J., Viitanen, J., Eloranta, P., Korhonen, T.: Iot for intelligent traffic system. In: International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 175–179 (2013)Google Scholar
  17. 17.
    Rankin, A.L., Matthies, L.H., Huertas, A.: Daytime water detection by fusing multiple cues for autonomous off-road navigation (2004)Google Scholar
  18. 18.
    Rühle, J., Rodner, E., Denzler, J.: Beyond thinking in common categories: Predicting obstacle vulnerability using large random codebooks. In: Machine Vision Applications (MVA), pp. 198–201 (2015)Google Scholar
  19. 19.
    Shibata, K., Furukane, T., Kawai, S., Horita, Y.: Distinction of wet road surface condition at night using texture features. Electron. Commun. Jpn. (ECJ) 97, 51–57 (2014)CrossRefGoogle Scholar
  20. 20.
    Shibata, K., Takeuch, K., Kawai, S., Horita, Y.: Detection of road surface conditions in winter using road surveillance cameras at daytime, night-time and twilight. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 14, 21–24 (2014)Google Scholar
  21. 21.
    Silion, S., Fosalau, C.: Wet road surfaces detection by measuring the air humidity in two points. In: International Conference and Exposition on Electrical and Power Engineering (EPE), pp. 744–747 (2014)Google Scholar
  22. 22.
    Sun, Z., Jia, K.: Road surface condition classification based on color and texture information. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), pp. 137–140 (2013)Google Scholar
  23. 23.
    Teshima, T., Saito, H., Shimizu, M., Taguchi, A.: Classification of wet/dry area based on the mahalanobis distance of feature from time space image analysis. In: International Conference on Machine Vision Applications (MVA), pp. 467–470 (2009)Google Scholar
  24. 24.
    Yamada, M., Ueda, K., Horiba, I., Sugie, N.: Discrimination of the road condition toward understanding of vehicle driving environments. Intell. Transp. Syst. (ITS) 2, 26–31 (2001)CrossRefGoogle Scholar
  25. 25.
    Yang, H.J., Jang, H., Kang, J.W., Jeong, D.S.: Classification algorithm for road surface condition. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 14, 1–5 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Manuel Amthor
    • 1
    Email author
  • 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

Personalised recommendations