Curve speed model for driver assistance based on driving style classification

Curve speed model for driver assistance based on driving style classification

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle–road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K-means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors’ model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.


    1. 1)
      • 1. Statistics Annals of Road Traffic Accident of People's Republic of China (2014) (Traffic Administration Bureau, Chinese Ministry of Public Security, 2015).
    2. 2)
      • 2. Jiménez, F., Liang, Y., Aparicio, F.: ‘Adapting ISA system warnings to enhance user acceptance’, Accid. Anal. Prev., 2012, 48, pp. 3748.
    3. 3)
      • 3. Pratt, M.P., Geedipally, S.R.: ‘Developing a framework for evaluating and selecting curve safety treatments’. Transportation Research Board 95th Annual Meeting, Washington, the USA, 2016.
    4. 4)
      • 4. Funk, J., Wirth, J., Bonugli, E.,, et al: ‘An integrated model of rolling and sliding in rollover crashes’ (SAE, 2012).
    5. 5)
      • 5. Yang, X.: ‘Balance of static and dynamic rollover thresholds for a three-axle vehicle’, SAE Int. J. Commer. Veh., 2011, 4, (1), pp. 2230.
    6. 6)
      • 6. MacAdam, C.C.: ‘Understanding and modeling the human driver’, Veh. Syst. Dyn., 2003, 40, (1-3), pp. 101134.
    7. 7)
      • 7. Salvucci, D.D.: ‘Modeling driver behavior in a cognitive architecture’, Hum. Factors, J. Hum. Factors Ergon. Soc., 2006, 48, (2), pp. 362380.
    8. 8)
      • 8. Glaser, S., Mammar, S., Sentouh, C.: ‘Integrated driver–vehicle–infrastructure road departure warning unit’, IEEE Trans. Veh. Technol., 2010, 59, (6), pp. 27572771.
    9. 9)
      • 9. Sentouh, C., Glaser, S., Mammar, S.: ‘Advanced vehicle–infrastructure–driver speed profile for road departure accident prevention’, Veh. Syst. Dyn., 2006, 44, (Sup1), pp. 612623.
    10. 10)
      • 10. Chen, X.L., Lu, M., Zhang, D.Z.,, et al: ‘Integrated safety speed model for curved roads’. Proc. of the 2010 World Automotive Congress, London, the UK, 2010, pp. 17.
    11. 11)
      • 11. Bosetti, P., Da Lio, M., Saroldi, A.: ‘On curve negotiation: from driver support to automation’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 20822093.
    12. 12)
      • 12. Zhang, D., Xiao, Q., Wang, J., et al: ‘Driver curve speed model and its application to ACC speed control in curved roads’, Int. J. Autom. Technol., 2013, 14, (2), pp. 241247.
    13. 13)
      • 13. Lee, Y.H., Deng, W.: ‘Speed control method for vehicle approaching and traveling on a curve’. U.S. Patent 7,400,963, July 2008.
    14. 14)
      • 14. Lee, Y.H.: ‘Automatic speed control system for vehicle approaching and driving on a curve’. ASME 2008 Int. Mechanical Engineering Congress and Exposition, January 2008, pp. 345353.
    15. 15)
      • 15. Sun, C., Wu, C., Chu, D., et al: ‘Risk prediction for curve speed warning by considering human, vehicle, and road factors’, Transp. Res. Rec., J. Transp. Res. Board, 2016, 2581, pp. 1826.
    16. 16)
      • 16. Dukic, T., Ahlstrom, C., Patten, C., et al: ‘Effects of electronic billboards on driver distraction’, Traffic Inj. Prev., 2013, 14, (5), pp. 469476.
    17. 17)
      • 17. Son, J., Park, M., Park, B.B.: ‘The effect of age, gender and roadway environment on the acceptance and effectiveness of advanced driver assistance systems’, Transp. Res. F, Traffic Psychol. Behav., 2015, 31, pp. 1224.
    18. 18)
      • 18. Vangi, D., Virga, A.: ‘Evaluation of energy-saving driving styles for bus drivers’, Proc. Inst. Mech. Eng. D, J. Autom. Eng., 2003, 217, (4), pp. 299305.
    19. 19)
      • 19. Castignani, G., Frank, R., Engel, T.: ‘An evaluation study of driver profiling fuzzy algorithms using smartphones’. 21st IEEE Int. Conf. Network Protocols (ICNP), October 2013, pp. 16.
    20. 20)
      • 20. Constantinescu, Z., Marinoiu, C., Vladoiu, M.: ‘Driving style analysis using data mining techniques’, Int. J. Comput. Commun. Control, 2010, 5, (5), pp. 654663.
    21. 21)
      • 21. Enev, M., Takakuwa, A., Koscher, K., et al: ‘Automobile driver fingerprinting’, Proc. Privacy Enhancing Technol., 2016, 2016, (1), pp. 3450.
    22. 22)
      • 22. Miyajima, C., Nishiwaki, Y., Ozawa, K., et al: ‘Driver modeling based on driving behavior and its evaluation in driver identification’, Proc. IEEE, 2007, 95, (2), pp. 427437.
    23. 23)
      • 23. Zhang, T., Chen, H.: ‘Report about the development and preliminary trial of the temperament test scale’, J. Shanxi Univ. (Philos. Soc. Sci. Ed.) (in Chinese), 1985, 1985, (4), pp. 7377.
    24. 24)
      • 24. Murphey, Y.L., Milton, R., Kiliaris, L.: ‘Driver's style classification using jerk analysis’. IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, March 2009, pp. 2328.
    25. 25)
      • 25. Li, G., Li, S.E., Cheng, B., et al: ‘Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities’, Transp. Res. C, Emerg. Technol., 2017, 74, pp. 113125.
    26. 26)
      • 26. Larish, C., Piyabongkarn, D., Tsourapas, V., et al: ‘A new predictive lateral load transfer ratio for rollover prevention systems’, IEEE Trans. Veh. Technol., 2013, 62, (7), pp. 29282936.
    27. 27)
      • 27. Wong, J.Y.: ‘Theory of ground vehicles’ (John Wiley & Sons, 2001, 3rd edn.).
    28. 28)
      • 28. SAE J3016: ‘Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems’, 2014.
    29. 29)
      • 29. Russell, H.E., Harbott, L.K., Nisky, I., et al: ‘Motor learning affects car-to-driver handover in automated vehicles’, Sci. Robot., 2016, 1, (1), p. eaah5682.

Related content

This is a required field
Please enter a valid email address