Who Has Better Driving Style: Let Data Tell Us | SpringerLink
Skip to main content
Springer Nature Link
Log in

Who Has Better Driving Style: Let Data Tell Us

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

Included in the following conference series:

  • 2037 Accesses

Abstract

Recent researches have put a lot of effort on mining vehicle sensing data, in order to help improve the driving safety. However, due to the lack of continuously collected data, most work assumed that one particular driver would only show or hold one driving style on any trips he/she was going on. In this paper, we analyzed more than 3.5 million GPS data points and about 68,500 driving events obtained from on-board devices installed in cars running in big cities for nine months. Different than other methods, we establish the driving style vector for each driver based on the fact that the same driver performs different behaviors on different trips. We propose a two-step clustering algorithm and obtain four representative driver groups with different driving styles. In addition to “normal”, “aggressive” and “clam”, we also find out an “experienced” driving style that was never mentioned in other existing works.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

References

  1. Berthaume, A.L., Romoser, M.R.E., Collura, J., Ni, D.: Towards a social psychology-based microscopic model of driver behavior and decision-making: modifying lewin’s field theory. Procedia Comput. Sci. 32, 816–821 (2014)

    Article  Google Scholar 

  2. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)

    Article  MathSciNet  Google Scholar 

  3. Castignani, G., Derrmann, T., Frank, R., Engel, T.: Driver behavior profiling using smartphones: a low-cost platform for driver monitoring. IEEE Intell. Transp. Syst. Mag. 7(1), 91–102 (2015). https://doi.org/10.1109/MITS.2014.2328673

    Article  Google Scholar 

  4. Chakravarty, T., Ghose, A., Bhaumik, C., Chowdhury, A.: Mobidrivescore—a system for mobile sensor based driving analysis: a risk assessment model for improving one’s driving. In: 2013 Seventh International Conference on Sensing Technology (ICST), pp. 338–344. IEEE (2013)

    Google Scholar 

  5. Constantinescu, Z., Marinoiu, C., Vladoiu, M.: Driving style analysis using data mining techniques. Int. J. Comput. Commun. Control. 5(5), 654–663 (2010)

    Article  Google Scholar 

  6. Corti, A., Ongini, C., Tanelli, M., Savaresi, S.M.: Quantitative driving style estimation for energy-oriented applications in road vehicles. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3710–3715 (2013)

    Google Scholar 

  7. Dörr, D., Grabengiesser, D., Gauterin, F.: Online driving style recognition using fuzzy logic. In: 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 1021–1026. IEEE (2014)

    Google Scholar 

  8. Gilman, E., Keskinarkaus, A., Tamminen, S., Pirttikangas, S., Röning, J., Riekki, J.: Personalised assistance for fuel-efficient driving. Transp. Res. Part C Emerg. Technol. 58, 681–705 (2015)

    Article  Google Scholar 

  9. Higgs, B., Abbas, M.: A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles. In: International IEEE Conference on Intelligent Transportation Systems, pp. 857–862 (2013)

    Google Scholar 

  10. Higgs, B., Abbas, M.: Segmentation and clustering of car-following behavior: recognition of driving patterns. IEEE Trans. Intell. Transp. Syst. 16(1), 81–90 (2015)

    Article  Google Scholar 

  11. Ishibashi, M., Okuwa, M., Doi, S., Akamatsu, M.: Indices for characterizing driving style and their relevance to car following behavior. In: 2007 SICE Annual Conference, pp. 1132–1137 (2007)

    Google Scholar 

  12. Jolliffe, I.: Principal Component Analysis, pp. 1094–1096. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2_455

    Chapter  Google Scholar 

  13. Kim, J., Sim, H., Oh, J.: The flexible EV/HEV and SOC band control corresponding to driving mode, driver’s driving style and environmental circumstances. Oxford University Press 4(2), 151–152 (2012)

    Google Scholar 

  14. Leonhardt, V., Wanielik, G.: Neural network for lane change prediction assessing driving situation, driver behavior and vehicle movement. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6 (2017)

    Google Scholar 

  15. Ly, M.V., Martin, S., Trivedi, M.M.: Driver classification and driving style recognition using inertial sensors. In: Intelligent Vehicles Symposium, pp. 1040–1045 (2013)

    Google Scholar 

  16. Manzoni, V., Corti, A., De Luca, P., Savaresi, S.M.: Driving style estimation via inertial measurements. In: International IEEE Conference on Intelligent Transportation Systems, pp. 777–782 (2010)

    Google Scholar 

  17. Martinez, C.M., Heucke, M., Wang, F.Y., Gao, B., Cao, D.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transp. Syst. PP(99), 1–11 (2017)

    Google Scholar 

  18. Martinussen, L.M., Møller, M., Prato, C.G.: Assessing the relationship between the driver behavior questionnaire and the driver skill inventory: revealing sub-groups of drivers. Transp. Res. Part F Traffic Psychol. Behav. 26, 82–91 (2014)

    Article  Google Scholar 

  19. Meiring, G.A.M., Myburgh, H.C.: A review of intelligent driving style analysis systems and related artificial intelligence algorithms. Sensors 15(12), 30653–30682 (2015)

    Article  Google Scholar 

  20. Morton, J., Kochenderfer, M.J.: Simultaneous policy learning and latent state inference for imitating driver behavior. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6 (2017)

    Google Scholar 

  21. Mudgal, A., Hallmark, S., Carriquiry, A., Gkritza, K.: Driving behavior at a roundabout: a hierarchical bayesian regression analysis. Transp. Res. Part D 26, 20–26 (2014)

    Article  Google Scholar 

  22. Murphey, Y.L., Milton, R., Kiliaris, L.: Driver’s style classification using jerk analysis. In: 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp. 23–28 (2009). https://doi.org/10.1109/CIVVS.2009.4938719

  23. Romoser, M., Fisher, D.L.: Effects of cognitive and physical decline on older drivers’ side-to-side scanning for hazards while executing turns. In: Driving Assessment International Driving Symposium on Human Factors in Driving Assessment Training and Vehicle Design (2009)

    Google Scholar 

  24. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7. http://www.sciencedirect.com/science/article/pii/0377042787901257

    Article  MATH  Google Scholar 

  25. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)

    Article  Google Scholar 

  26. Vaitkus, V., Lengvenis, P., Žylius, G.: Driving style classification using long-term accelerometer information. In: 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 641–644 (2014)

    Google Scholar 

  27. Wang, W., Xi, J.: A rapid pattern-recognition method for driving styles using clustering-based support vector machines. In: American Control Conference (2016)

    Google Scholar 

  28. Wheeler, T.A., Robbel, P., Kochenderfer, M.J.: Analysis of microscopic behavior models for probabilistic modeling of driver behavior. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 1604–1609 (2016)

    Google Scholar 

  29. Xu, L., Hu, J., Jiang, H., Meng, W.: Establishing style-oriented driver models by imitating human driving behaviors. IEEE Trans. Intell. Transp. Syst. 16(5), 2522–2530 (2015)

    Article  Google Scholar 

  30. Zhao, N., Reimer, B., Mehler, B., D’Ambrosio, L.A., Coughlin, J.F.: Self-reported and observed risky driving behaviors among frequent and infrequent cell phone users. Accid. Anal. Prev. 61, 71–77 (2013)

    Article  Google Scholar 

  31. Zylius, G.: Investigation of route-independent aggressive and safe driving features obtained from accelerometer signals. IEEE Intell. Transp. Syst. Mag. 9(2), 103–113 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. China Academy of Information and Communications Technology, No.52, Hua Yuan Bei Road, Haidian District, Beijing, China

    Linna Wu

  2. AI Research Institute, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen, China

    Huan Li

  3. Beijing Bytedance Technology Co., Ltd, Building 4, Zijin Digital Park, Haidian District, Beijing, China

    Hengtian Ding

  4. Broadsense (Shenzhen) Information Technology Co. Ltd, Shenzhen, China

    Lizhuo Zhang

Authors
  1. Linna Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hengtian Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lizhuo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huan Li .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, L., Li, H., Ding, H., Zhang, L. (2020). Who Has Better Driving Style: Let Data Tell Us. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_37

Download citation

Publish with us

Policies and ethics