Chinese Journal of Mechanical Engineering

, Volume 30, Issue 5, pp 1133–1149 | Cite as

Exploring Challenges in Developing a Smart and Effective Assistive System for Improving the Experience of the Elderly Drivers

Original Article


As the overall population ages, driving-related accidents and injuries, associated with elderly drivers, have risen. Existing research about elderly drivers mainly focuses on factual data collection and analysis, indicating the elderly’s growing fatal accident rates and their different behaviours compared to younger drivers. However, few research has focused on design-led practical solutions to mitigate the elderly’s growing fatal accidents, by considering their usability and body conditions, afflicting the elderly, such as decreased vision, hearing, and reaction times. In this paper, first, current worldwide situations on growing fatal accident rates for elderly drivers is reviewed and the key impact factors are identified and discussed with regarding to usability and design trend in the automotive technology for elderly. Second, existing smart vehicle technology-based solutions to promote safe driving are explored and their pros and cons are discussed and analysed. Most of solutions are not created by people with driving difficulties, which are caused by health problems most commonly afflicting the elderly. Thirdly, diverse design-led research activities are taken, such as a survey, observation, and interviews to gain new understanding of what kinds of driving problems elderly drivers have and demonstrate how new system concepts could be developed for the elderly’s benefits. Finally, it is found that the elderly’s low vision and late reaction are main factors causing their driving difficulties. Based on this finding, usable vehicle system design ideas have been proposed, by utilising facial expression sensing technology as a solution. The proposed solutions would ensure reducing both the elderly’s driving problems and high fatal accident rates and provide a more enjoyable driving environment for the elderly population.


Elderly drivers Aging health conditions Driving behaviours Vision impairment Cognitive impairment Hearing loss Reflexes Fatal collision 


  1. 1.
    World Population United Nations Ageing. Economic and Social Affairs, 2013, 7: 66−71.Google Scholar
  2. 2.
    National Highway Traffic Safety Administration. Traffic safety facts: older population. Washington (DC): NHTSA, 2014.Google Scholar
  3. 3.
    Institute for Traffic Accident Research and Data Analysis. Annual report of statistics on traffic accidents 2011. Institute for Traffic Accident Research and Data Analysis, 2012.Google Scholar
  4. 4.
    National Highway Traffic Safety ADMINISTRATION. Traffic Safety Facts: Older Population. Washington (DC): NHTSA, 2015.Google Scholar
  5. 5.
    NATIONAL HIGHWAY TRAFFIC SAFETY ADMINISTRATION. Traffic Safety Facts: Older Population. Washington (DC): NHTSA, 2010.Google Scholar
  6. 6.
    Transport Canada. Canadian motor vehicle traffic collision statistics. [Retrieved 2005-02].
  7. 7.
    M Johnson, E Howard. Road safety vision 2010 mid-term review. Canadian Traffic Safety Institute, 2007.Google Scholar
  8. 8.
    Department for Transport. Strategic Framework for Road Safety. [Retrieved 2011].
  9. 9.
    D Otte, B Wiese. Injury rates for older and younger belted drivers in traffic accidents. SAE International, 2012, 5(1): 506−516.Google Scholar
  10. 10.
    S W Hong, W Park, S Hong. Thoracic injury characteristics of elderly drivers in real world car accidents. ESV Conference, 2013, 13: 337−347.Google Scholar
  11. 11.
    K Morita, M Sekine. Characteristics of accidents and violations caused by elderly drivers in Japan. SAE International, 2013, 2: 14−20.Google Scholar
  12. 12.
    M Jie, M Horie. Self-driving car demand seen boosted by Japan’s aging population. [Retrieved 2013]
  13. 13.
    J Saisan, M White, L Robinson. Older driver safety. [Retrieved 2013].
  14. 14.
    S J Park, H K Lim. Characteristics of elderly driver’s driving behaviour and cognition under unexpected event using driving simulator. SAE International, 2011, 1: 552−556. doi: 10.4271/2011-01-0552.
  15. 15.
    A Iwase, Y Suzuki, M Araie, et al. The prevalence of primary open-angle glaucoma in Japanese TheTajimi Study. Ophthalmology, 2004, 111(9): 1641−1648.Google Scholar
  16. 16.
    Roaddriver. Driving with a hearing impairment. [Retrieved 2010].
  17. 17.
    F Alvarez, I Fierro. Older drivers, medical condition, medical impairment and crash risk. Accident Analysis and Prevention, 2008, 40: 55−60.Google Scholar
  18. 18.
    A Hiroko, M Hiroyuki. Usability research for the elderly people. Oki Technical Review, 2004, 71(3): 54−57.Google Scholar
  19. 19.
    A W Guo, J F Brake, S Edwards, et al. The application of in-vehicle systems for elderly drivers. European Transport Research Review, 2010, 2(3): 165−174.Google Scholar
  20. 20.
    B Prazak, A Hochgatterer, T Holthe, et al. User requirements as crucial determinants for the development of new technological solutions in elderly care – Exemplified in an European Project. Assistive Technology Research Series, 2007, 20: 826−830.Google Scholar
  21. 21.
    M Hermann, T Pentek, B Otto. Design principles for Industrie 4.0 scenarios. System Sciences (HICSS), 2016 49th Hawaii International Conference.Google Scholar
  22. 22.
    S Matsuoka, H Ogawa, H Maki. A new safety support system for wandering elderly persons. 33rd Annual International Conference of the IEEE EMBS. Boston, Massachusetts USA, 2011.Google Scholar
  23. 23.
    V Charisis, G Vlachos, S Arafat. On the impact of elderly warning collision avoidance information through prototype head-up display for older drivers. SAE International, 2011-01-1033.Google Scholar
  24. 24.
    R G Fairchild, J F Brake, N Thorpe, et al. Using on-board driver feedback systems to encourage safe, ecological and efficient driving: The Foot-LITE Project. AISB 2009 Convention. Edinburgh, UK, 2009.Google Scholar
  25. 25.
    L Mitchell, N Stamatiadis. Traffic maneuvers of elderly: Their viewpoint and perspective. SAE International, 2002, 1: 81−89.Google Scholar
  26. 26.
    S Koppel, M Bohensky, J Langford, et al. Older drivers, crashes and injuries. Traffic International Journal Prev., 2011, 12(5): 459−467.Google Scholar
  27. 27.
    J M Lyman, G McGwin, R V Sims. Factors related to driving difficulty and habits in older drivers. Accident Analysis and Prevention, 2001, 33(3): 413−421.Google Scholar
  28. 28.
    H Middleton, D Westwood. Specification of older driver requirements for technologies to enhance capability. SAE International, 2001-01-3350.Google Scholar
  29. 29.
    D Parker, L McDonald, P Rabbitt, et al. Elderly drivers and their accidents: The Aging Driver Questionnaire. Elsevier, 2000, 32(6), 751−759.Google Scholar
  30. 30.
    J A Healey, R W Picard. Detecting stress during real world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(2): 156−166.Google Scholar
  31. 31.
    A Lott, M Scheneck, J A Brabyn. Face recognition in the elderly. Optometry and Vision Science, 2005, 82: 10−18.Google Scholar
  32. 32.
    E Schmidt, R Decke, R Rasshofer. Correlation between subjective driver state measures and psychophysiological and vehicular data in simulated driving. 2016 IEEE Intelligent Vehicles Symposium, Gothernburg, Sweden.Google Scholar
  33. 33.
    Y Linwang, J Hong, C Hong. EEG-based emotion recognition in music listening. IEEE Transactions on Biomedical Engineering, 2010, 57: 1798−1806.Google Scholar
  34. 34.
    R Khosrowabadi. Affective computation on EEG correlates of emotion from musical and voal stimuli. Proceedings of International Joint Conference on Neural Networks, Atlanta, 2009.Google Scholar
  35. 35.
    C Owsley, R Sekuler, C Boldt. Aging and low-contrast vision: face perception. Invest Ophthalmol Vis. Sci., 1981, 21: 362−365.Google Scholar
  36. 36.
    T Lassa. The beginning of the end of driving. [Retrieved 2013-10].
  37. 37.
    W Zhu, J Miao, B Jiang, et al. Vehicle detection in driving simulation using extreme learning machine. Neurocomputing, 2014, 128: 160-165.Google Scholar
  38. 38.
    B Howard. Tesla records its first Autopilot fatal crash; NHTSA opens investigation. [Retrieved 2016-05].
  39. 39.
    A Hern. Car hacking is the future – and sooner or later you’ll be hit, [Retrieved 2016-03].
  40. 40.
    H Sharp, Y Rogers, J Preece. Interaction design: beyond human computer interaction. 2nd ed. Chichester: John Wiley, 2007.Google Scholar

Copyright information

© Chinese Mechanical Engineering Society and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Electronic and Computer EngineeringBrunel University LondonUxbridgeUK
  2. 2.Electrical QRD, PM, UX, SBT DivisionGeneral Motors KoreaIncheonSouth Korea
  3. 3.Northumbria School of DesignNorthumbria UniversityNewcastle upon TyneUK

Personalised recommendations