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Road Weather Condition Estimation Using Fixed and Mobile Based Cameras

  • Koray OzcanEmail author
  • Anuj SharmaEmail author
  • Skylar KnickerbockerEmail author
  • Jennifer MerickelEmail author
  • Neal HawkinsEmail author
  • Matthew RizzoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.

Keywords

Road weather classification Scene classification VGG16 Neural networks CCTV Mobile camera 

References

  1. 1.
    U.S. DOT Federal Highway Administration: How do weather events impact roads? https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm. Accessed 01 Aug 2018
  2. 2.
    Rakha, H., Arafeh, M., Park, S.: Modeling inclement weather impacts on traffic stream behavior. Int. J. Transp. Sci. Technol. 1(1), 25–47 (2012)CrossRefGoogle Scholar
  3. 3.
    Haug, A., Grosanic, S.: Usage of road weather sensors for automatic traffic control on motorways. Transp. Res. Procedia 15, 537–547 (2016)CrossRefGoogle Scholar
  4. 4.
    Ogura, T., Kageyama, I., Nasukawa, K., Miyashita, Y., Kitagawa, H., Imada, Y.: Study on a road surface sensing system for snow and ice road. JSAE Rev. 23(3), 333–339 (2002)CrossRefGoogle Scholar
  5. 5.
    Hirt, B.: Installing snowplow cameras and integrating images into MnDOT’s traveler information system. National Transportation Library (2017)Google Scholar
  6. 6.
    Son, S., Baek, Y.: Design and implementation of real-time vehicular camera for driver assistance and traffic congestion estimation. Sensors 15(8), 20204–20231 (2015)CrossRefGoogle Scholar
  7. 7.
    Rajamohan, D., Gannu, B., Rajan, K.S.: MAARGHA: a prototype system for road condition and surface type estimation by fusing multi-sensor data. ISPRS Int. J. Geo- Inf. 4(3), 1225–1245 (2015)CrossRefGoogle Scholar
  8. 8.
    Kutila, M., Pyykönen, P., Ritter, W., Sawade, O., Schäufele, B.: Automotive LIDAR sensor development scenarios for harsh weather conditions. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro (2016)Google Scholar
  9. 9.
    Nguyen, C.V., Milford, M., Mahony, R.: 3D tracking of water hazards with polarized stereo cameras. In: IEEE International Conference on Robotics and Automation (ICRA) (2017)Google Scholar
  10. 10.
    Abdic, I., Fridman, L., Brown, D.E., Angell, W., Reimer, B., Marchi, E., Schuller, B.: Detecting road surface wetness from audio: a deep learning approach. In: 23rd International Conference on Pattern Recognition (ICPR) (2016)Google Scholar
  11. 11.
    Kuehnle, A., Burghout, W.: Winter road condition recognition using video image classification. Transp. Res. Rec. J. Transp. Res. Board 1627, 29–33 (1998)CrossRefGoogle Scholar
  12. 12.
    Pan, G., Fu, L., Yu, R., Muresan, M.I.: Winter road surface condition recognition using a pre-trained deep convolutional neural network. In: Transportation Research Board 97th Annual Meeting, Washington DC, United States (2018)Google Scholar
  13. 13.
    Qian, Y., Almazan, E.J., Elder, J.H.: Evaluating features and classifiers for road weather condition analysis. In: IEEE International Conference on Image Processing (ICIP), September 2016Google Scholar
  14. 14.
    Jonsson, P.: Road condition discrimination using weather data and camera images. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) (2011)Google Scholar
  15. 15.
    Jonsson, P.: Classification of road conditions: from camera images and weather data. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings (2011)Google Scholar
  16. 16.
    Sun, Z., Jia, K.: Road surface condition classification based on color and texture information. In: Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2013)Google Scholar
  17. 17.
    Lee, J., Hong, B., Shin, Y., Jang, Y.-J.: Extraction of weather information on road using CCTV video. In: International Conference on Big Data and Smart Computing (BigComp), Hong Kong (2016)Google Scholar
  18. 18.
    Kawarabuki, H., Onoguchi, K.: Snowfall detection under bad visibility scene. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), October 2014Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  20. 20.
    Wang, L., Lee, C.-Y., Tu, Z., Lazebnik, S.: Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:1505.02496 (2015)
  21. 21.
    Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1452–1464 (2018)CrossRefGoogle Scholar
  22. 22.
    Kalliatakis, G.: Keras-places. https://github.com/GKalliatakis/Keras-VGG16-places365. Accessed 15 Nov 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute for TransportationIowa State UniversityAmesUSA
  2. 2.Center for Transportation Research and EducationAmesUSA
  3. 3.University of Nebraska Medical CenterOmahaUSA

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