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access icon free Investigation of older driver's takeover performance in highly automated vehicles in adverse weather conditions

Driving is important for older people to maintain mobility. To reduce age-related functional decline, older drivers may adjust their driving by avoiding difficult situations. One of these situations is driving in adverse weather conditions such as in the rain, snow and fog which reduce the visual clarity of the road ahead. The upcoming highly automated vehicle (HAV) has the potential of supporting older people. However, only limited work has been done to study older drivers’ interaction with HAV, especially in adverse weather conditions. This study investigates the effect of age and weather on takeover control performance among drivers from HAV. A driving simulation study with 76 drivers has been implemented. The participants took over the vehicle control from HAV under four weather conditions clear weather, rain, snow and fog, where the time and quality of the takeover control are quantified and measured. Results show age did affect the takeover time (TOT) and quality. Moreover, adverse weather conditions, especially snow and fog, lead to a longer TOT and worst takeover quality. The results highlighted that a user-centred design of human–machine interaction would have the potential to facilitate a safe interaction with HAV under the adverse weather for older drivers.

References

    1. 1)
      • 4. Musselwhite, C.B., Haddad, H.: ‘Prolonging the safe driving of older people through technology’ (University of the West of England, England, 2007).
    2. 2)
      • 33. Fotios, S., Cheal, C., Fox, S., et al: ‘The transition between lit and unlit sections of road and detection of driving hazards after dark’, Light. Res. Technol., 2017, 0, pp. 119.
    3. 3)
      • 35. Brébion, G.: ‘Working memory, language comprehension, and aging: four experiments to understand the deficit’, Exper. Aging Res., 2003, 29, (3), pp. 269301.
    4. 4)
      • 1. ‘National Population Projections: 2014-based Statistical Bulletin-ONS’. Available at http://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2015-10-29, accessed 4 October 2016.
    5. 5)
      • 31. Ferreira, I.S., Simões, M.R., Marôco, J.: ‘Cognitive and psychomotor tests as predictors of on-road driving ability in older primary care patients’, Transp. Res. F, Traffic Psychol. Behav., 2013, 21, pp. 146158.
    6. 6)
      • 23. Mok, B., Johns, M., Lee, K.J., et al: ‘Timing of unstructured transitions of control in automated driving’. Proc. Int. Conf. IEEE Intelligent Vehicles Symp. IV, Seoul, South Korea, 2015 pp. 11671172.
    7. 7)
      • 28. Brouwer, W.H., Waterink, W., Van Wolffelaar, P.C., et al: ‘Divided attention in experienced young and older drivers: lane tracking and visual analysis in a dynamic driving simulator’, Hum. Factors, J. Hum. Factors Ergon. Soc., 1991, 33, (5), pp. 573582.
    8. 8)
      • 6. McGwin, G.Jr., Brown, D.B.: ‘Characteristics of traffic crashes among young, middle-aged, and older drivers’, Accid. Anal. Prev., 1999, 31, (3), pp. 181198.
    9. 9)
      • 41. Bellet, T., Paris, J.C., Marin-Lamellet, C.: ‘Difficulties experienced by older drivers during their regular driving and their expectations towards advanced driving aid systems and vehicle automation’, Transp. Res. F, Traffic Psychol. Behav., 2018, 52, pp. 138163.
    10. 10)
      • 18. Körber, M., Gold, C., Lechner, D., et al: ‘The influence of age on the takeover of vehicle control in highly automated driving’, Transp. Res. F, Traffic Psychol. Behav., 2016, 39, pp. 1932.
    11. 11)
      • 7. Karthaus, M., Falkenstein, M.: ‘Functional changes and driving performance in older drivers: assessment and interventions’, Geriatrics, 2016, 1, (2), p. 12.
    12. 12)
      • 40. Mueller, A.S., Trick, L.M.: ‘Driving in fog: the effects of driving experience and visibility on speed compensation and hazard avoidance’, Accid. Anal. Prev., 2012, 48, pp. 472479.
    13. 13)
      • 8. Clarke, D.D., Ward, P., Bartle, C., et al: ‘Older drivers’ road traffic crashes in the UK’, Accid. Anal. Prev., 2010, 42, (4), pp. 10181024.
    14. 14)
      • 5. Department for Transport: ‘National travel survey: England 2015’, September 2016.
    15. 15)
      • 26. Attebo, K., Mitchell, P., Smith, W.: ‘Visual acuity and the causes of visual loss in Australia: the blue mountains eye study’, Ophthalmology, 1996, 103, (3), pp. 357364.
    16. 16)
      • 29. Pollatsek, A., Romoser, M.R., Fisher, D.L.: ‘Identifying and remediating failures of selective attention in older drivers’, Curr. Dir. Psychol. Sci., 2012, 21, (1), pp. 37.
    17. 17)
      • 21. Guo, W., Blythe, P.T., Edwards, S., et al: ‘Effect of intelligent speed adaptation technology on older drivers’ driving performance’, IET Intell. Transp. Syst., 2013, 9, (3), pp. 343350.
    18. 18)
      • 9. Ball, K., Owsley, C., Stalvey, B., et al: ‘Driving avoidance and functional impairment in older drivers’, Accid. Anal. Prev., 1998, 30, (3), pp. 313322.
    19. 19)
      • 22. Edwards, S.J., Emmerson, C., Namdeo, A., et al: ‘Optimising landmark-based route guidance for older drivers’, Transp. Res. F, Traffic Psychol. Behav., 2016, 43, pp. 225237.
    20. 20)
      • 30. Myerson, J., Robertson, S., Hale, S.: ‘Aging and intraindividual variability in performance: analyses of response time distributions’, J. Exper. Anal. Behav., 2007, 88, (3), pp. 319337.
    21. 21)
      • 36. Shumway-Cook, A., Woollacott, M.: ‘Attentional demands and postural control: the effect of sensory context’, J. Gerontol. A, Biol. Med. Sci., 2000, 55, (1), p. 10.
    22. 22)
      • 13. Gasser, T.M., Westhoff, D.: ‘BAST-study: definitions of automation and legal issues in Germany’. 2012 Road Vehicle Automation Workshop, Irvine, CA, USA, 2012.
    23. 23)
      • 27. Helzner, E.P., Cauley, J.A., Pratt, S.R., et al: ‘Race and sex differences in age-related hearing loss: the health, aging and body composition study’, J. Am. Geriatr. Soc., 2005, 53, (12), pp. 21192127.
    24. 24)
      • 32. Stelmach, G.E., Goggin, N.L.: ‘Psychomotor decline with age’, Choice, 1988, 247, (307), p. 24.
    25. 25)
      • 24. Horst, R.V.D.: ‘Time-to-collision as a cue for decision-making in braking’, in Gale, A.G.E.A. (Ed.): ‘Vision in vehicles III’ (Elsevier, Amsterdam, Netherlands, 1991), pp. 1926.
    26. 26)
      • 20. Molnar, L.J.: ‘Age-related differences in driver behavior associated with automated vehicles and the transfer of control between automated and manual control: a simulator evaluation’, University of Michigan, A.A., Transportation Research Institute.
    27. 27)
      • 14. ‘NHTSA U.S. Department of Transportation Releases Policy on Automated Vehicle Development’. Available at http://www.nhtsa.gov/About-NHTSA/Press-Releases/U.S.-Department-of-Transportation-Releases-Policy-on-Automated-Vehicle-Development, accessed 25 October 2016.
    28. 28)
      • 12. Marottoli, R.A., Leon, C.F.M., Glass, T.A., et al: ‘Driving cessation and increased depressive symptoms: prospective evidence from the New Haven EPESE’, J. Am. Geriatr. Soc., 1997, 45, (2), pp. 202206.
    29. 29)
      • 3. Musselwhite, C.B.: ‘The importance of driving for older people and how the pain of driving cessation can be reduced’, J. Dement. Ment. Health, 2011, 15, (3), pp. 2226.
    30. 30)
      • 15. ‘SAE Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems’. Available at http://standards.sae.org/j3016_201401/, accessed 25 October 2016.
    31. 31)
      • 25. Radlmayr, J., Gold, C., Lorenz, L., et al: ‘How traffic situations and non-driving related tasks affect the takeover quality in highly automated driving’. Proc. Int. Conf. Human Factors and Ergonomics Society Annual Meeting, 2014, pp. 20632067.
    32. 32)
      • 16. Department for Transport: ‘Department for transport the pathway to driverless car: a code of practice for testing’, July 2015.
    33. 33)
      • 38. Pangbourne, K., Aditjandra, P.T., Nelson, J.D.: ‘New technology and quality of life for older people: exploring health and transport dimensions in the UK context’, IET Intell. Transp. Syst., 2010, 4, (4), pp. 318327.
    34. 34)
      • 10. Emmerson, C., Guo, W., Blythe, P., et al: ‘Fork in the road: in-vehicle navigation systems and older drivers’, Transp. Res. F, Traffic Psychol. Behav., 2013, 21, pp. 173180.
    35. 35)
      • 37. Vaportzis, E., Georgiou-Karistianis, N., Stout, J.C.: ‘Dual task performance in normal aging: a comparison of choice reaction time tasks’, PLOS One, 2013, 8, (3), p. e60265.
    36. 36)
      • 17. Miller, D., Johns, M., Ive, H., et al: ‘Exploring transitional automation with new and old drivers’, SAE Technical Paper, 2016-01-1442.
    37. 37)
      • 2. Metz, D.H.: ‘Mobility of older people and their quality of life’, Transp. Policy, 2000, 7, (2), pp. 149152.
    38. 38)
      • 19. Clark, H., Feng, J.: ‘Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation’, Accid. Anal. Prev., 2016, 106, pp. 468479.
    39. 39)
      • 39. Ashley, W.S., Strader, S., Dziubla, D.C., et al: ‘Driving blind: weather-related vision hazards and fatal motor vehicle crashes’, Bull. Am. Meteorol. Soc., 2015, 96, (5), pp. 755778.
    40. 40)
      • 11. Davidse, R.J.: ‘Older drivers and ADAS: which systems improve road safety?’, IATSS Res., 2006, 30, (1), pp. 620.
    41. 41)
      • 34. Brookhuis, K.A., de Waard, D.: ‘Monitoring drivers’ mental workload in driving simulators using physiological measures’, Accid. Anal. Prev., 2010, 42, (3), pp. 898903.
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