With age-related changes in cognitive functioning including attention, older drivers experience increased crash risks and are particularly overrepresented in vehicle crashes in driving situations such as making a left turn and merging with traffic. Effective remedies via assessment, training, and interface design require understanding of individual performance in specific scenarios. In this study, we investigated older driver performance characteristics in eight distinct hazard scenarios. Participants completed a task measuring their attentional processing of information and decision at intersections. The findings revealed sizable heteroge-neity in performance across scenarios, with older drivers being more conservative or liberal in certain situa-tions. Some group differences were also observed. These findings suggest the importance of examining individual performance in unique driving scenarios in addition to an aggregated accuracy across all scenar-ios.
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References
1.
BirrellS. A.FowkesM.JenningsP. A. (2014). Effect of using an in-vehicle smart driving aid on real-world driver performance. IEEE Transactions on Intelligent Transportation Systems, 15(4), 1801–1810. https://doi.org/10.1109/TITS.2014.2328357
2.
BallK. K.BeardB. L.RoenkerD. L.MillerR. L.GriggsD. S. (1988). Age and visual search: Expanding the useful field of view. JOSA A, 5(12), 2210-2219.
3.
BédardM.ParkkariM.WeaverB.RiendeauJ.DahlquistM. (2010). Assessment of driving performance using a simulator protocol: Validi-ty and reproducibility. American Journal of Occupational Therapy, 64(2), 336-340.
4.
BraverE. R.TrempelR. E. (2004). Are older drivers actually at higher risk of involvement in collisions resulting in deaths or non-fatal inju-ries among their passengers and other road users?. Injury Preven-tion, 10(1), 27-32.
ChoiH.FengJ. (2016). Differential association of distinct attentional functions with specific driving errors and crash types. In the Proceed-ings of the 95th Transportation Research Board Annual Meeting, Washington, DC.
7.
ChoiH.GrühnD.FengJ. (2015). Self-reported Attentional Failures during Driving Relates to On-road Crashes and Simulated Driving Per-formance of Older Drivers. In Transportation Research Board 94th Annual Meeting (No. 15-5079).
8.
ChoiH.KaskoJ.FengJ. (2019). An attention assessment for informing older drivers’ crash risks in various hazardous situations. The Geron-tologist, 59(1), 112-123.
9.
CicchinoJ. B.McCarttA. T. (2014). Trends in older driver crash in-volvement rates and survivability in the United States: An up-date. Accident Analysis & Prevention, 72, 44-54.
10.
CicchinoJ. B.McCarttA. T. (2015). Critical older driver errors in a national sample of serious US crashes. Accident Analysis & Preven-tion, 80, 211-219.
11.
CockrellJ. R.FolsteinM. F. (1988). Mini-Mental State Examination (MMSE). Psychopharmacology bulletin, 24(4), 689-692.
12.
DaigneaultG.JolyP.FrigonJ. Y. (2002). Executive functions in the evaluation of accident risk of older drivers. Journal of Clinical and Experimental Neuropsychology, 24, 221-238.
FengJ.ChoiH.CraikF. I.LevineB.MorenoS.NaglieG.ZhuM. (2018). Adaptive response criteria in road hazard detection among old-er drivers. Traffic Injury Prevention, 19(2), 141-146.
15.
FengJ.CraikF. I.LevineB.MorenoS.NaglieG.ChoiH.MedinaA. (2015, January). Drive aware task: measuring target detection in a visual clutter in the driving context. In Transportation Research Board 94th Annual Meeting (No. 15-4960).
16.
FolsteinM. F.FolsteinS. E.McHughP. R. (1975). Minin-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189-198.
17.
HorswillM. S.HillA.WettonM. (2015). Can a video-based hazard perception test used for driver licensing predict crash involvement?. Accident Analysis & Prevention, 82, 213-219.
18.
LiG.BraverE. R.ChenL. H. (2003). Fragility versus excessive crash involvement as determinants of high death rates per vehicle-mile of travel among older drivers. Accident Analysis & Prevention, 35(2), 227-235.
19.
López-RamónM. E.CastroC.RocaJ.LedesmaR.LupiañezJ. (2011). Attentional networks functioning, age, and attentional lapses while driving. Traffic Injury Prevention, 8, 116-120.
20.
OwsleyC.BallK.McGwinG.JrSloaneM. E.RoenkerD. L.WhiteM. F.OverleyE. T. (1998). Visual processing impairment and risk of motor vehicle crash among older adults. JAMA, 279(14), 1083-1088.
21.
PerryD. (2009). In the balance: silver tsunami or longevity dividend?Quality in Ageing and Older Adults, 10(2), 15-22.
22.
ReasonJ.MansteadA.StradlingS.BaxterJ.CampbellK. (1990). Errors and violations on the roads: a real distinction?Ergonom-ics, 33(10-11), 1315-1332.
23.
RocaJ.CrundallD.Morenon-RíosS.CastroC.LupiañezJ. (2013). The influence of differences in the functioning of the neurocognitive attentional networks on drivers’ performance. Accident Analysis and Prevention, 50, 1193-1206.
24.
SchwanenT.ZieglerF. (2011). Wellbeing, independence and mobility: an introduction. Ageing & Society, 31(5), 719-733.
25.
StaplinL.LococoK. H.MartellC.StuttsJ.. Taxonomy of older driver behaviors and crash risk. Publication No. DOT HS 811 468A, U.S. Department of Transportation, 2012.
26.
WeaverB.BédardM.McAuliffeJ.ParkkariM. (2009). Using the Attention Network Test to predict driving test scores. Accident Analy-sis and Prevention, 41, 76-83.
