Skip to main content
SpringerLink
Log in

Estimating Road Surface Condition Using Crowdsourcing

  • Conference paper
  • First Online:
Information Search, Integration, and Personlization (ISIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 760))

Abstract

Road surface conditions have a significant impact on transport safety and driving comfort, particularly in snowy areas. This paper proposes a new method for estimating road surface conditions by using a motion sensor embedded in a smartphone. The method is based on a mobile sensing framework that can collect sensor data using crowdsourcing. In this study, we have defined new road surface conditions as the estimation target, which takes into account both the substance that covers the road surface, and the shape of the road surface itself. The paper also describes a method of feature selection, comprising two steps: First, an initial feature set is directly calculated using various features published in previous studies, using raw sensor data. Second, three feature selection algorithms are compared: Principal Component Analysis (PCA), Relief-F, and Sequential Forward Floating Search (SFFS), and the most effective of the three chosen. In this study, the SFFS algorithm showed higher accuracy than the others. The road surface condition classification was performed across different speed ranges using the Random Forest Classifier, and results show that the best accuracy, of about 91%, was obtained in the 50 km/h–80 km/h range.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    https://itunes.apple.com/app/drive-around-the-corner./id1053216595.

References

  1. Aihara, K., Bin, P., Imura, H., Takasu, A., Tanaka, Y.: On feasibility of crowdsourced mobile sensing for smarter city life. In: Streitz, N., Markopoulos, P. (eds.) DAPI 2016. LNCS, vol. 9749, pp. 395–404. Springer, Cham (2016). doi:10.1007/978-3-319-39862-4_36

    Chapter  Google Scholar 

  2. Breiman, L.: Random for. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  3. Chen, K., Lu, M., Tan, G., Wu, J.: CRSM: crowdsourcing based road surface monitoring. In: 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), pp. 2151–2158. IEEE (2013)

    Google Scholar 

  4. Fujino, Y., Kitagawa, K., Furukawa, T., Ishii, H.: Development of Vehicle Intelligent Monitoring System (VIMS), part 1st edn., vol. 5765, pp. 148–157. SPIE (2005). doi:10.1117/12.601727

  5. Gupta, P., Dallas, T.: Feature selection and activity recognition system using a single triaxial accelerometer. IEEE Trans. Biomed. Eng. 61(6), 1780–1786 (2014)

    Article  Google Scholar 

  6. He, Z., Jin, L.: Activity recognition from acceleration data based on Discrete Consine Transform and SVM. In: The 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp. 5041–5044 (2009). http://ieeexplore.ieee.org/xpl/downloadCitations

  7. Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intel. 19(2), 153–158 (1997)

    Article  Google Scholar 

  8. Jolliffe, I.: Principal Component Analysis. Springer, New York (1986). doi:10.1007/b98835

    Book  MATH  Google Scholar 

  9. Jonsson, P.: Classification of road conditions: from camera images and weather data. In: 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), pp. 1–6. IEEE (2011)

    Google Scholar 

  10. Kawai, S., Takeuchi, K., Shibata, K., Horita, Y.: A method to distinguish road surface conditions for car-mounted camera images at night-time. In: 012 12th International Conference on ITS Telecommunications (ITST), vol. 2, pp. 668–672. IEEE (2012)

    Google Scholar 

  11. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the ninth international workshop on Machine learning, pp. 249–256 (1992)

    Chapter  Google Scholar 

  12. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994). doi:10.1007/3-540-57868-4_57

    Chapter  Google Scholar 

  13. Korpela, J., Maekawa, T., Eberle, J., Chakraborty, D., Aberer, K.: Tree-structured classifier for acceleration-based activity and gesture recognition on smartwatches. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–4. IEEE (2016)

    Google Scholar 

  14. Nomura, T., Shiraishi, Y.: A method for estimating road surface conditions with a smartphone. Int. J. Inform. Soc. 7(1), 29–36 (2015)

    Google Scholar 

  15. Omer, R., Fu, L.: An automatic image recognition system for winter road surface condition classification. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1375–1379. IEEE (2010)

    Google Scholar 

  16. Qian, Y., Almazan, E.J., Elder, J.H.: Evaluating features and classifiers for road weather condition analysis. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 4403–4407. IEEE (2016)

    Google Scholar 

  17. Sayers, M.: On the calculation of international roughness index from longitudinal road profile. Transp. Res. Rec. 1501, 1–12 (1995)

    Google Scholar 

  18. Wu, J., Pan, G., Zhang, D., Qi, G., Li, S.: Gesture recognition with a 3-D accelerometer. In: Zhang, D., Portmann, M., Tan, A.-H., Indulska, J. (eds.) UIC 2009. LNCS, vol. 5585, pp. 25–38. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02830-4_4

    Chapter  Google Scholar 

  19. Yagi, K.: A measuring method of road surface longitudinal profile from sprung acceleration, and verification with road profiler. J. Japan Soc. Civ. Eng. Ser. E1 (Pavement Eng.) 69(3) (2013)

    Article  Google Scholar 

  20. Yamada, M., Ueda, K., Horiba, I., Tsugawa, S., Yamamoto, S.: A study of the road surface condition detection technique based on the image information for deployment on a vehicle. IEEE Trans. Electr. Inform. Syst. 124(3), 753–760 (2004). doi:10.1541/ieejeiss.124.753

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” of the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan. And also it was partly supported by the CPS-IIP Project in the research promotion program “Research and Development for the Realization of Next-Generation IT Platforms” of the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).

Author information

Authors and Affiliations

  1. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan

    Bin Piao, Kenro Aihara, Atsuhiro Takasu & Jun Adachi

  2. The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan

    Akira Kinoshita

Authors
  1. Bin Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kenro Aihara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akira Kinoshita
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atsuhiro Takasu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun Adachi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Piao .

Editor information

Editors and Affiliations

  1. Saint Lab ETIS, Université de Cergy-Pontoise, Pontoise, France

    Dimitris Kotzinos

  2. Laboratoire ETIS, Université de Cergy Pontoise, Cergy Pontoise, France

    Dominique Laurent

  3. Dept Informatique INSA Lyon, Université de Lyon, Villeurbanne Cedex, France

    Jean-Marc Petit

  4. LRI, University of Paris, Orsay, France

    Nicolas Spyratos

  5. Graduate School of Information Science Knowledge Media Laboratory, Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Piao, B., Aihara, K., Kinoshita, A., Takasu, A., Adachi, J. (2017). Estimating Road Surface Condition Using Crowdsourcing. In: Kotzinos, D., Laurent, D., Petit, JM., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personlization. ISIP 2016. Communications in Computer and Information Science, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-68282-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68282-2_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68281-5

  • Online ISBN: 978-3-319-68282-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation