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Modelling the Intrusive Feelings of Advanced Driver Assistance Systems Based on Vehicle Activity Log Data: Case Study for the Lane Keeping Assistance System

  • Kyudong Park
  • Jiyoung KwahkEmail author
  • Sung H. Han
  • Minseok Song
  • Dong Gu Choi
  • Hyeji Jang
  • Dohyeon Kim
  • Young Deok Won
  • In Sub Jeong
Article
  • 37 Downloads

Abstract

Although the automotive industry has been among the sectors that best-understands the importance of drivers’ affect, the focus of design and research in the automotive field has long emphasized the visceral aspects of exterior and interior design. With the adoption of Advanced Driver Assistance Systems (ADAS), endowing ‘semi-autonomy’ to the vehicles, however, the scope of affective design should be expanded to include the behavioural aspects of the vehicle. In such a ‘shared-control’ system wherein the vehicle can intervene in the human driver’s operations, a certain degree of ‘intrusive feelings’ are unavoidable. For example, when the Lane Keeping Assistance System (LKAS), one of the most popular examples of ADAS, operates the steering wheel in a dangerous situation, the driver may feel interrupted or surprised because of the abrupt torque generated by LKAS. This kind of unpleasant experience can lead to prolonged negative feelings such as irritation, anxiety, and distrust of the system. Therefore, there are increasing needs of investigating the driver’s affective responses towards the vehicle’s dynamic behaviour. In this study, four types of intrusive feelings caused by LKAS were identified to be proposed as a quantitative performance indicator in designing the affectively satisfactory behaviour of LKAS. A metric as well as a statistical data analysis method to quantitatively measure the intrusive feelings through the vehicle sensor log data.

Key Words

ADAS LKAS Vehicle activity log data Intrusive feeling Affective design 

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Notes

Acknowledgement

This study has been supported by Hyundai NGV and Hyundai Motor Company.

References

  1. Bishop, R. (2005). Intelligent Vehicle Technology and Trends. Artech House. Norwood, Massachusetts, USA.Google Scholar
  2. Blaschke, C., Breyer, F., Färber, B., Freyer, J. and Limbacher, R. (2009). Driver distraction based lane-keeping assistance. Transportation Research Part F: Traffic Psychology and Behaviour 12, 4, 288–299.CrossRefGoogle Scholar
  3. Cha, S.-H. (2007). Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Mathematical Models and Methods in Applied Science 1, 4, 300–307.Google Scholar
  4. Chang, H.-C., Lai, H.-H. and Chang, Y.-M. (2006). Expression modes used by consumers in conveying desire for product form: A case study of a car. Int. J. Industrial Ergonomics 36, 1, 3–10.CrossRefGoogle Scholar
  5. Duda, R. O., Hart, P. E. and Stork, D. G. (2001). Pattern Classification. 2nd edn. John Wiley & Sons. Hoboken, New Jersey, USA.Google Scholar
  6. Eichelberger, A. H. and McCartt, A. T. (2016). Toyota drivers’ experiences with dynamic radar cruise control, pre-collision system, and lane-keeping assist. J. Safety Research, 56, 67–73.CrossRefGoogle Scholar
  7. Ersal, I., Papalambros, P., Gonzalez, R. and Aitken, T. J. (2011). Modelling perceptions of craftsmanship in vehicle interior design. J. Engineering Design 22, 2, 129–144.CrossRefGoogle Scholar
  8. Freedman, D. and Diaconis, P. (1981). On the histogram as a density estimator: L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 57, 4, 453–476.MathSciNetCrossRefGoogle Scholar
  9. Gkouskos, D. and Chen, F. (2012). The use of affective interaction design in car user interfaces. Work 41, Supplement 1, 5057–5061.Google Scholar
  10. Helander, M. G., Khalid, H. M., Lim, T. Y., Peng, H. and Yang, X. (2013). Emotional needs of car buyers and emotional intent of car designers. Theoretical Issues in Ergonomics Science 14, 5, 455–474.CrossRefGoogle Scholar
  11. Hidalgo, C., Gonçalves, B., Pedrosa, M. A., Castellano, J., Erents, K., Fraguas, A. L., Hron, M., Jiménez, J. A., Matthews, G. F. and van Milligen, B. (2002). Empirical similarity in the probability density function of turbulent transport in the edge plasma region in fusion plasmas. Plasma Physics and Controlled Fusion 44, 8, 1557–1564.CrossRefGoogle Scholar
  12. Hwang, J., Huh, K., Na, H., Jung, H., Kang, H. and Yoon, P. (2008). Development of a model based predictive controller for lane keeping assistance. SAE Paper No. 2008-01-1454.Google Scholar
  13. ISO 11270 (2014). Intelligent Transport Systems — Lane Keeping Assistance Systems (LKAS) — Performance Requirements and Test Procedures.Google Scholar
  14. Jones, W. D. (2002). Building safer cars. IEEE Spectrum 39, 1, 82–85.CrossRefGoogle Scholar
  15. Khalid, H. M., Opperud, A., Radha, J. K., Xu, Q. and Helander, M. G. (2012). Elicitation and analysis of affective needs in vehicle design. Theoretical Issues in Ergonomics Science 13, 3, 318–334.CrossRefGoogle Scholar
  16. Leder, H. and Carbon, C.-C. (2005). Dimensions in appreciation of car interior design. Applied Cognitive Psychology 19, 5, 603–618.CrossRefGoogle Scholar
  17. Marino, R., Scalzi, S. and Netto, M. (2012). Integrated driver and active steering control for vision-based lane keeping. European J. Control 18, 5, 473–484.MathSciNetCrossRefGoogle Scholar
  18. Mineta, K., Unoura, K. and Ikeda, T. (2003). Development of a lane mark recognition system for a lane keeping assist system. SAE Paper No. 2003-01-0281.Google Scholar
  19. National Highway Traffic Safety Administration (2013). Lane Departure Warning System Confirmation Test and Lane Keeping Support Performance Documentation. US Department of Transportation.Google Scholar
  20. Rajamani, R. (2011). Vehicle Dynamics and Control. Springer. New York, USA.Google Scholar
  21. Risack, R., Mohler, N. and Enkelmann, W. (2000). A video-based lane keeping assistant. Proc. IEEE Intelligent Vehicles Symp., Dearborn, Michigan, USA.Google Scholar
  22. Schütte, S. and Eklund, J. (2005). Design of rocker switches for work-vehicles — An application of kansei engineering. Applied Ergonomics 36, 5, 557–567.CrossRefGoogle Scholar
  23. Strand, N., Nilsson, J., Karlsson, I. C. M. and Nilsson, L. (2014). Semi-automated versus highly automated driving in critical situations caused by automation failures. Transportation Research Part F: Traffic Psychology and Behaviour 27, Part B, 218–228.CrossRefGoogle Scholar
  24. Suzianti, A., Apriliandary, S. and Poetri, N. P. (2016). Affective design with kansei mining: An empirical study from automotive industry in indonesia. Int. Conf. Design, User Experience, and Usability, 76–85.Google Scholar
  25. Tanaka, J., Ishida, S., Kawagoe, H. and Kondo, S. (2000). Workload of using a driver assistance system. Proc. IEEE Intelligent Transportation Systems, Dearborn, Michigan, USA.Google Scholar
  26. Vlacic, L., Parent, M. and Harashima, F. (2001). Intelligent Vehicle Technologies. Butterworth-Heinemann. Oxford, UK.Google Scholar

Copyright information

© KSAE 2019

Authors and Affiliations

  • Kyudong Park
    • 1
  • Jiyoung Kwahk
    • 2
    Email author
  • Sung H. Han
    • 2
  • Minseok Song
    • 2
  • Dong Gu Choi
    • 2
  • Hyeji Jang
    • 2
  • Dohyeon Kim
    • 2
  • Young Deok Won
    • 3
  • In Sub Jeong
    • 3
  1. 1.Department of Creative IT EngineeringPohang University of Science and Technology (POSTECH)GyeongbukKorea
  2. 2.Department of Industrial and Management EngineeringPohang University of Science and Technology (POSTECH)GyeongbukKorea
  3. 3.ADAS Performance Development TeamHyundai Motor CompanyHwaseong-si, GyeonggiKorea

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