Persuasive Strategies to Improve Driving Behaviour of Elderly Drivers by a Feedback Approach

  • Perrine Ruer
  • Charles Gouin-Vallerand
  • Evelyne F. Vallières
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9638)

Abstract

We are witnessing a growing aging population who wishes to live independently. In a driving context, the elderly want to maintain an active lifestyle, but they may suffer from impairments due to aging. New intelligent transportation systems can be beneficial for drivers to assist them with driving. Hence, new intelligent technologies have to be accepted and have to persuade the driver of adopting safer driving behaviours. In this paper, we present a persuasive driving feedback for elderly drivers. The feedback objectives are to evaluate the drivers’ perception of the driving fatigue and to compare it with their driving behaviour. Our first results from an exploratory field experiment with twenty elderly drivers support the use of the feedback with that category of drivers relative to fatigue perception. The originality of this paper is to enable progress in the area of persuasive technologies applied to road safety and for elder people.

Keywords

Elderly drivers Persuasive technology Feedback Contextual information Road safety 

Notes

Acknowledgments

We want to particularly thank the Canadian Automobile Association (CAA) Foundation, Section of the Quebec Province, for funding the research works behind this paper.

References

  1. 1.
    Sivak, M., Schoettle, B.: Recent changes in the age composition of drivers in 15 countries. Traffic Inj. Prev. 13(2), 126–132 (2012)CrossRefGoogle Scholar
  2. 2.
    Reimer, B.: Driver assistance systems and the transition to automated vehicles: A path to increase older adult safety and mobility? Publ. Policy Aging Rep. 24(1), 27–31 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Emery, L., Srinivasan, G., Bezzina, D., Leblanc, D., Sayer, J., Bogard, S., Pomerleau, D.: Status report on USDOT project – an intelligent vehicle initiative road departure crash warning field operational test. In: 19th International Technical Conference Enhanced Safety of Vehicles, Washington DC (2005)Google Scholar
  4. 4.
    Fogg, B.J.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  5. 5.
    Hamari, J., Koivisto, J., Pakkanen, T.: Do persuasive technologies persuade? - a review of empirical studies. In: Spagnolli, A., Chittaro, L., Gamberini, L. (eds.) PERSUASIVE 2014. LNCS, vol. 8462, pp. 118–136. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Oinas-Kukkonen, H., Harjumaa, M.: Persuasive systems design: key issues, process model, and system features. Commun. Assoc. Inf. Syst. 24(1), 28 (2009)Google Scholar
  7. 7.
    Schätzl, J.: How Effective are Persuasive Technologies in Automotive Context? Persuasive Technologies and Applications (2015)Google Scholar
  8. 8.
    Meschtscherjakov, A., Wilfinger, D., Scherndl, T., Tscheligi, M.: Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. In: Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 81–88. ACM (2009)Google Scholar
  9. 9.
    Vaezipour, A., Rakotonirainy, A., Haworth, N.: Reviewing in-vehicle systems to improve fuel efficiency and road safety. Procedia Manuf. 3, 3192–3199 (2015)CrossRefGoogle Scholar
  10. 10.
    Eby, D.W., Molnar, L.J.: Has the time come for an older driver vehicle? (2012)Google Scholar
  11. 11.
    Gruau, S., Pottier, A., Davenne, D., Denise, P.: Les facteurs d’accidents de la route par somnolence chez les conducteurs âgés: Prévention par l’activité physique. Recherche-Transports-Securite 79, 134–144 (2003)Google Scholar
  12. 12.
    Assailly, J.P., Bonin-Guillaume, S., Mohr, A., Parola, A., Grandjean, R., Frances, Y.M.: Les conducteurs âgés en bonne santé font plus d’erreurs et d’oublis que d’infractions: Enquête auprès de 904 volontaires. La Presse Médicale 35(6), 941–947 (2006)CrossRefGoogle Scholar
  13. 13.
    Oxley, J., Langford, J., Koppel, S., Charlton, J.: Senior Driving Longer, Smarter, Safer: Enhancement of an Innovative Educational and Training Package for the Safe Mobility of Seniors. Technical Report, Monash University Accident Research Centre, Monash Injury Research Institute (2013)Google Scholar
  14. 14.
    Anstey, K., Wood, J., Lord, S., Walker, J.G.: Cognitive, sensory and physical factors enabling driving safety in older adults. Clin. Psychol. Rev. 25(1), 45–65 (2005)CrossRefGoogle Scholar
  15. 15.
    Owsley, C., Mcgwin, G.: Vision and driving. Vision. Res. 50(23), 2348–2361 (2010)CrossRefGoogle Scholar
  16. 16.
    Langford, J., Koppel, S.: Licence restrictions as an under-used strategy in managing older driver safety. Accid. Anal. Prev. 43(1), 487–493 (2011)CrossRefGoogle Scholar
  17. 17.
    Rakotonirainy, A., Steinhardt, D.: In-vehicle technology functional requirements for older drivers. In: The 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 27–33. ACM, New York (2009)Google Scholar
  18. 18.
    Thiffault, P., Bergeron, J.: Monotony of road environment and driver fatigue: a simulator study. Accid. Anal. Prev. 35(3), 381–391 (2003)CrossRefGoogle Scholar
  19. 19.
    Beirness, D.J., Simpson, H.M., Desmond, K.: The Road Safety Monitor 2004: Drowsy Driving. Traffic Injury Research Foundation, Ottawa, Ontario (2005)Google Scholar
  20. 20.
    Gershon, P., Shinar, D., Oron-Gilad, T., Parmet, Y., Ronen, A.: Usage and perceived effectiveness of fatigue countermeasures for professional and nonprofessional drivers. Accid. Anal. Prev. 43, 797–803 (2011)CrossRefGoogle Scholar
  21. 21.
    Vanlaar, W., Simpson, H., Mayhew, D., Robertson, R.: Fatigued and drowsy driving: a survey of attitudes, opinions and behaviours. J. Saf. Res. 39, 303–309 (2008)CrossRefGoogle Scholar
  22. 22.
    Kedowide, C., Gouin-Vallerand, C., Vallieres, E.F.: Recognizing blind spot check activity with car drivers based on decision tree classifiers. In: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Québec (2014)Google Scholar
  23. 23.
    Ruer, P., Gouin-Vallerand, C., Zhang, L., Lemire, D., Vallières, E.F.: An analysis tool for the contextual information from field experiments on driving fatigue. In: Christiansen, H., Stojanovic, I., Papadopoulos, G.A. (eds.) Modeling and Using Context. LNCS, vol. 9405, pp. 172–185. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  24. 24.
    Lemire, D., Brooks, M., Yan, Y.: An optimal linear time algorithm for quasi-monotonic segmentation. Int. J. Comput. Math. 86(7), 1093–1104 (2009)CrossRefMATHGoogle Scholar
  25. 25.
    Clay, O.J., Wadley, V.G., Edwards, J.D., Roth, D.L., Roenker, D.L., Ball, K.K.: Cumulative meta-analysis of the relationship between useful field of view and driving performance in older adults: Current and future implications. Optom. Vis. Sci. 82(8), 724–731 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Perrine Ruer
    • 1
  • Charles Gouin-Vallerand
    • 1
  • Evelyne F. Vallières
    • 1
  1. 1.LICEF Research CenterTélé-Université du QuébecMontréalCanada

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