International Journal of Automotive Technology

, Volume 19, Issue 1, pp 179–190 | Cite as

Investigation of objective parameters for acceptance evaluation of automatic lane change system

  • Chulwoo Moon
  • Youngseok Lee
  • Chang-Hyun Jeong
  • Seibum Choi


Recently, with increased interest in high levels of automated driving systems such as automatic lane change system, the need for reliable assessment methods of driver acceptance has arisen. Because the acceptance depends on the individual, the assessment of the acceptance can only be based on an individual’s personal attitude, expectations, and experiences. Accordingly, subjective evaluation methods have mostly been utilized to assess the acceptance of newly developed advanced driver assistance systems. In this study, an investigation of the effects of vehicle dynamic behavior and the traffic environment on driver acceptance is conducted to provide an objective evaluation method of driver acceptance for an automatic lane change system. In order to conduct the investigation, a specific experimental program is designed and a massive database, including information on interaction behaviors between drivers, a vehicle and the traffic environment is constructed with a selected group of 19 drivers. Then, 21 parameters and their descriptive statistics for an objective evaluation index are presented to illustrate the analysis results. The results of this research can be important not only for an objective evaluation of the acceptance, but can also be expanded to suggest design criteria for control of advanced and automated driving assistance systems.


Driver/Passenger acceptance Acceptance evaluation Objectification ADAS (Advanced Driver Assistant System) Automatic lane change system 



accelerations, m/s2


distance to target vehicles, m


derivative of accelerations, m/s3


velocity, m/s


quartile value of a ranked set


throttle positions sensor value, -


time to collision, s


relative velocity between vehicles, m/s


steering wheel angle, deg

\(\dot \delta \)

steering wheel angular velocity, deg/s


vehicle yaw angle, deg


vehicle roll angle, deg



n th quartiles of a ranked set


identifications of target vehicles


set of minimum values


set of maximum values


set of median values


set of mean values


set of standard deviation values


element in longitudinal vector


element in lateral vector


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adell, E. and Varhelyi, A. (2008). Driver comprehension and acceptance of the active accelerator pedal after longterm use. Transportation Research Part F: Traffic Psychology and Behaviour 11, 1, 37–51.CrossRefGoogle Scholar
  2. Adell, E. (2010). Acceptance of driver support systems. Proc. European Conf. Human Centred Design for Intelligent Transport Systems, 475–486.Google Scholar
  3. Butakov, V. A. and Ioannou, P. (2015). Personalized driver/vehicle lane change models for ADAS. IEEE Trans. Vehicular Technology 64, 10, 4422–4431.CrossRefGoogle Scholar
  4. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science 35, 8, 982–1003.CrossRefGoogle Scholar
  5. Gindele, T., Brechtel, S. and Dillmann, R. (2015). Learning driver behavior models from traffic observations for decision making and planning. IEEE Intelligent Transportation Systems Magazine 7, 1, 69–79.CrossRefGoogle Scholar
  6. Kim, J. (2008). Analysis of handling performance based on simplified lateral vehicle dynamics. Int. J. Automotive Technology 9, 6, 687–693.CrossRefGoogle Scholar
  7. Kim, J. (2011). Objectification of on-center handling characteristics based on spring-mass-damper system. Int. J. Automotive Technology 12, 6, 857–864.CrossRefGoogle Scholar
  8. Neale, V. L., Dingus, T. A., Klauer, S. G., Sudweeks, J. and Goodman, M. (2005). An overview of the 100-car naturalistic study and findings. National Highway Traffic Safety Administration, Paper No. 05-0400.Google Scholar
  9. Nobukawa, K., Bao, S., LeBlanc, D. J., Zhao, D., Peng, H. and Pan, C. S. (2016). Gap acceptance during lane changes by large-truck drivers–An image-based analysis. IEEE Trans. Intelligent Transportation Systems 17, 3, 772–781.CrossRefGoogle Scholar
  10. Nishiwaki, Y., Miyajima, C., Kitaoka, N., Terashima, R., Wakita, T. and Takeda, K. (2008). Generating lanechange trajectories of individual drivers. IEEE Int. Conf. Vehicular Electronics and Safety, 271–275.Google Scholar
  11. Peden, M., Scurfield, R., Sleet, D., Mohan, A., Hyder, A., Jrawan, E. and Mathers, C. (2004). World Report on Road Traffic Injury Prevention. World Health Organization. World Bank. Geneva.Google Scholar
  12. Regan, M., Mitsopoulos, E., Haworth, N. and Young, K. (2002). Acceptability of In-vehicle Intelligence Transport Systems to Victorian Car Drivers. Monash University Accident Research Centre.Google Scholar
  13. Schade, J. and Baum, M. (2007). Reactance or acceptance? Reactions towards the introduction of road pricing. Transportation Research Part A: Policy and Practice 41, 1, 41–48.Google Scholar
  14. Van Der Laan, J. D., Heino, A. and De Waard, D. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C: Emerging Technologies 5, 1, 1–10.CrossRefGoogle Scholar
  15. Venkatesh, V. and Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly 24, 1, 115–139.CrossRefGoogle Scholar
  16. Yao, W., Zhao, H., Bonnifait, P. and Zha, H. (2013). Lane change trajectory prediction by using recorded human driving data. IEEE Intelligent Vehicles Symp. (IV), 430–436.Google Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  • Chulwoo Moon
    • 1
    • 2
  • Youngseok Lee
    • 2
  • Chang-Hyun Jeong
    • 2
  • Seibum Choi
    • 1
  1. 1.School of Mechanical, Aerospace & System EngineeringKAISTDaejeonKorea
  2. 2.Korea Automotive Technology InstituteDriving & Safety System R&D CenterChungnamKorea

Personalised recommendations