Skip to main content

Unsupervised Driving Profile Detection Using Cooperative Vehicles’ Data

  • Conference paper
  • First Online:
Communication Technologies for Vehicles (Nets4Cars/Nets4Trains/Nets4Aircraft 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 11461))

Included in the following conference series:

Abstract

C-ITS (Cooperative Intelligent Transport Systems) provide nowadays a very huge amounts of data either from vehicles, roadside units, operator servers or smart-phone applications. Data need to be exploited and analyzed. In this paper, we first study the communication logs containing network messages emitted by the vehicles and the infrastructures when they communicate. We used these logs to measure the latency and evaluate if it is consistent with data analysis. Then, we try to detect driving profile using unsupervised machine learning approaches. Results both in terms of latency and of driving profile detection reveal promising issues in this new area.

Supported by The InterCor project number INEA/CEF/TRAN/M2015/1143833.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.standards.its.dot.gov/Factsheets/Factsheet/80.

  2. 2.

    ETSI: http://www.etsi.org.

References

  1. Teixeira, F.A., et al.: Vehicular networks using the IEEE 802.11P standard. Veh. Commun. 1(2), 91–96 (2014). ISSN 2214–2096. https://doi.org/10.1016/j.vehcom.2014.04.001

    Article  Google Scholar 

  2. ETSI. Intelligent Transport Systems (ITS); Access layer specification for Intelligent Transport Systems operating in the 5 GHz frequency band. European Standard. ETSI, November 2012

    Google Scholar 

  3. ETSI. ETSI TS 102 941: Intelligent Transport Systems (ITS); Security; Trust and Privacy Management. European Standard. ETSI, May 2018

    Google Scholar 

  4. Akalu, R.: Privacy, consent and vehicular ad hoc networks (VANETs). Comput. Law Secur. Rev. 34(1), 37–46 (2018). ISSN 0267–3649. https://doi.org/10.1016/j.clsr.2017.06.006. http://www.sciencedirect.com/science/article/pii/S0267364917302170

    Article  Google Scholar 

  5. Saini, I., Saad, S., Jaekel, A.: Identifying vulnerabilities and attacking capabilities against pseudonym changing schemes in VANET. In: Traore, I., Woungang, I., Ahmed, S.S., Malik, Y. (eds.) ISDDC 2018. LNCS, vol. 11317, pp. 1–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03712-3_1. ISBN 978-3-030-03712-3

    Chapter  Google Scholar 

  6. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (1990)

    Google Scholar 

  7. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD 1996, Montreal, Quebec, Canada, pp. 103–114. ACM (1996). ISBN 0-89791-794-4. https://doi.org/10.1145/233269.233324

  8. Lorbeer, B., et al.: Variations on the clustering algorithm BIRCH. Big Data Res. 11, 44–53 (2018). Selected papers from the 2nd INNS Conference on Big Data: Big Data & Neural Networks, pp. 44–53. ISSN 2214–5796. https://doi.org/10.1016/j.bdr.2017.09.002. http://www.sciencedirect.com/science/article/pii/S2214579617300151

    Article  Google Scholar 

  9. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Statistics, vol. 1, pp. 281–297. University of California Press, Berkeley (1967). https://projecteuclid.org/euclid.bsmsp/1200512992

  10. Bahmani, B., et al.: Scalable K-means++. Proc. VLDB Endow. 5(7), 622–633 (2012). ISSN 2150–8097. https://doi.org/10.14778/2180912.2180915

    Article  Google Scholar 

  11. Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Statistical Data Analysis Based on the L1 Norm and Related Methods. North-Holland; Amsterdam, pp. 405–416 (1987). ISBN: 0444702733

    Google Scholar 

  12. Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering algorithm for large databases. Inf. Syst. 26(1), 35–58 (2001). ISSN 0306–4379. https://doi.org/10.1016/S0306-4379(01)00008-4

    Article  Google Scholar 

  13. Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)

    Google Scholar 

  14. Ankerst, M., et al.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28(2), 49–60 (1999)

    Article  Google Scholar 

  15. Agrawal, R., et al.: Automatic subspace clustering of high dimensional data for data mining applications, vol. 27, no. 2. ACM (1998)

    Google Scholar 

  16. Wang, W., Yang, J., Muntz, R., et al.: STING: a statistical information grid approach to spatial data mining. In: VLDB, vol. 97, pp. 186–195 (1997)

    Google Scholar 

  17. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982). ISSN 1432–0770. https://doi.org/10.1007/BF00337288

    Article  Google Scholar 

  18. Martinetz, T., Schulten, K., et al.: A “neural-gas” network learns topologies (1991)

    Google Scholar 

  19. Canales, F., Chacón, M.: Modification of the growing neural gas algorithm for cluster analysis. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 684–693. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76725-1_71

    Chapter  Google Scholar 

  20. Bourdy, E., Piamrat, K., Herbin, M., Fouchal, H.: New method for selecting exemplars application to roadway experimentation. In: Hodoň, M., Eichler, G., Erfurth, C., Fahrnberger, G. (eds.) I4CS 2018. CCIS, vol. 863, pp. 75–84. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93408-2_6

    Chapter  Google Scholar 

  21. ETSI EN 302 637–2; Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Part 2: Specification of Cooperative Awareness Basic Service. European Standard. ETSI, November 2014

    Google Scholar 

  22. ETSI EN 302 637–3; Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Application; Part 3: Specifications of Decentralized Environmental Notification Basic Service. European Standard. ETSI, November 2014

    Google Scholar 

  23. ETSI EN 302 636-4-1; Intelligent Transport Systems (ITS); Vehicular Communications; GeoNetworking; Part 4: Geographical Addressing and forwarding for point-to-point and point-to-multipoint communications; Subpart 1: Media-Independent Functionality. European Standard. ETSI, July 2014

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cyril de Runz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leblanc, B., Bourdy, E., Fouchal, H., de Runz, C., Ercan, S. (2019). Unsupervised Driving Profile Detection Using Cooperative Vehicles’ Data. In: Hilt, B., Berbineau, M., Vinel, A., Jonsson, M., Pirovano, A. (eds) Communication Technologies for Vehicles. Nets4Cars/Nets4Trains/Nets4Aircraft 2019. Lecture Notes in Computer Science(), vol 11461. Springer, Cham. https://doi.org/10.1007/978-3-030-25529-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-25529-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25528-2

  • Online ISBN: 978-3-030-25529-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics