Augmented Reality Cues and Elderly Driver Hazard Perception

Abstract

Objective:

The aim of this study was to evaluate the effectiveness of augmented reality (AR) cues in improving driving safety among elderly drivers who are at increased crash risk because of cognitive impairments.

Background:

Cognitively challenging driving environments pose a particular crash risk for elderly drivers. AR cuing is a promising technology to mitigate risk by directing driver attention to roadway hazards. We investigate whether AR cues improve or interfere with hazard perception in elderly drivers with age-related cognitive decline.

Method:

A total of 20 elderly (M = 73 years, SD = 5) licensed drivers with a range of cognitive abilities measured by a speed-of-processing (SOP) composite participated in a 1-hr drive in an interactive, fixed-base driving simulator. Each participant drove through six straight, 6-mile-long, rural roadway scenarios following a lead vehicle. AR cues directed attention to potential roadside hazards in three of the scenarios, and the other three were uncued (baseline) drives. Effects of AR cuing were evaluated with respect to (a) detection of hazardous target objects, (b) interference with detecting nonhazardous secondary objects, and (c) impairment in maintaining safe distance behind a lead vehicle.

Results:

AR cuing improved the detection of hazardous target objects of low visibility. AR cues did not interfere with detection of nonhazardous secondary objects and did not impair ability to maintain safe distance behind a lead vehicle. SOP capacity did not moderate those effects.

Conclusion:

AR cues show promise for improving elderly driver safety by increasing hazard detection likelihood without interfering with other driving tasks, such as maintaining safe headway.

Keywords

driver behavior simulation and virtual reality sensory and perceptual processes psychomotor processes aging and individual differences displays and controls

Get full access to this article

View all access options for this article.

References

Abrams

R. A.

Christ

S. E.

(2003). Motion onset captures attention. Psychological Science, 14, 427–432.

Anderson

S. W.

Aksan

Dawson

J. D.

E. Y.

Johnson

A. M.

Rizzo

(in press). Neuropsychological assessment of driving safety risk in older adults with and without neurologic disease. Journal of Clinical and Experimental Neuropsychology.

Azuma

(1997). A survey of augmented reality. Teleoperators and Virtual Environments, 6, 355–385.

Azuma

Baillot

Behringer

Feiner

Julier

MacIntyre

(2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47.

Ball

Edwards

J. D.

Ross

L. A.

(2007). The impact of speed of processing training on cognitive and everyday functions. Journal of Gerontology: Psychological Sciences, 62(Special Issue 1), 19–31.

Ball

Owsley

(1993). The useful field of view test: A new technique for evaluating age-related declines in visual function. Journal of the American Optometric Association, 64, 71–79.

Ball

Owsley

Sloane

M. E.

Roenker

D. L.

Bruni

J. R.

(1993). Visual attention problems as a predictor of vehicle crashes in older drivers. Investigative Ophthalmology and Visual Science, 34, 3110–3123.

Ball

Owsley

Stalvey

Roenker

D. L.

Sloane

M. E.

Graves

(1998). Driving avoidance and functional impairment in older drivers. Accident Analysis & Prevention, 30, 313–322.

Bliss

(1997). Alarm reaction patterns by pilots as a function of reaction modality. International Journal of Aviation Psychology, 7, 1–14.

10.

Bliss

Acton

S. A.

(2003). Alarm mistrust in automobiles: How collision alarm reliability affects driving. Applied Ergonomics, 34, 499–509.

11.

Bromberg

Oron-Gilad

Ronen

Borowsky

Parmet

(2012). The perception of pedestrians from the perspective of elderly experienced and experienced drivers. Accident Analysis & Prevention, 44, 48–55.

12.

Brouwer

W. H.

Waterink

Van Wolffelaar

P. C.

Rothengatter

J. A.

(1991). Divided attention in experienced young and older drivers: Lane tracking and visual analysis in a dynamic driving simulator. Human Factors, 33, 573–582.

13.

Cerelli

E. C.

(1995). Crash data and rates for age-sex groups of drivers, 1994. Washington, DC: National Highway Traffic Safety Administration, National Center for Statistics and Analysis, Research and Development.

14.

Chandraratna

S. K.

Stamatiadis

(2003). Problem driving maneuvers of elderly drivers. Transportation Research Record, 1843, 89–95.

15.

Chapanis

(1994). Hazards associated with three signal words and four colors on warning signs. Ergonomics, 37, 265–275.

16.

Dawson

J. D.

Anderson

S. W.

E. Y.

Dastrup

Rizzo

(2009). Predictors of driving safety in early Alzheimer’s disease. Neurology, 72, 521–527.

17.

Dawson

J. D.

E. Y.

Anderson

S. W.

Johnson

A. M.

Rizzo

(2010). Neuropsychological predictors of driving errors in older adults. Journal of the American Geriatrics Society, 58, 1090–1096.

18.

Dingus

T. A.

Hulse

M. C.

Mollenhauer

M. A.

Fleischman

R. N.

Mcgehee

D. V.

Manakkal

(1997). Effects of age, system experience, and navigation technique on driving with an advanced traveler information system. Human Factors, 39, 177–199.

19.

Edwards

J. D.

Vance

D. E.

Wadley

V. G.

Cissell

G. M.

Roenker

D. L.

Ball

K. K.

(2005). Reliability and validity of useful field of view test scores as administered by personal computer. Journal of Clinical and Experimental Neuropsychology, 27, 529–543.

20.

Gelasca

E. D.

Tomasic

Ebrahimi

(2005, January). Which colors best catch your eyes: A subjective study of color saliency. Paper presented at the First International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ.

21.

Hakamies-Blomqvist

Wahlström

(1998). Why do older drivers give up driving? Accident Analysis & Prevention, 30, 305–312.

22.

Reed

Spence

(2007). Multisensory in-car warning signals for collision avoidance. Human Factors, 49, 1107–1114.

23.

Spence

(2005). Assessing the effectiveness of various auditory cues in capturing a driver’s visual attention. Journal of Experimental Psychology–Applied, 11, 157–174.

24.

Horswill

M. S.

Marrington

S. A.

McCullough

C. M.

Wood

Pachana

N. A.

McWilliam

Raikos

M. K.

(2008). The hazard perception ability of older drivers. Journal of Gerontology: Psychological Sciences, 63, 212–218.

25.

Kim

Dey

A. K.

(2009). Simulated augmented reality windshield display as a cognitive mapping aid for elder driver navigation. In Proceedings of the 27th International SIGCHI conference on human factors in computing systems (pp. 133–142). New York, NY: Association for Computing Machinery.

26.

Kramer

A. F.

Cassavaugh

Horrey

W. J.

Becic

Mayhugh

J. L.

(2007). Influence of age and proximity warning devices on collision avoidance in simulated driving. Human Factors, 49, 935–949.

27.

Lamble

Laakso

Summala

(1999). Detection thresholds in car following situations and peripheral vision: Implications for positioning of visually demanding in-car displays. Ergonomics, 42, 807–815.

28.

Lee

J. D.

McGehee

D. V.

Brown

T. L.

Reyes

M. L.

(2002). Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Human Factors, 44, 314–334.

29.

Lees

M. N.

Cosman

J. D.

Fricke

Lee

J. D.

Rizzo

(2010). Translating cognitive neuroscience to the driver’s operational environment: A neuroergonomics approach. American Journal of Psychology, 123, 391–411.

30.

Lees

Lee

J. D.

(2007). The influence of distraction and driving context on driver response to imperfect collision warning systems. Ergonomics, 30, 1264–1286.

31.

Maltz

Shinar

(2004). Imperfect in-vehicle collision avoidance warning systems can aid drivers. Human Factors, 46, 357–366.

32.

Martin

Audet

Corriveau

Hamel

D’Amours

Smeesters

(2010). Comparison between younger and older drivers of the effect of obstacle direction on the minimum obstacle distance to brake and avoid a motor vehicle accident. Accident Analysis & Prevention, 42, 1144–1150.

33.

Matthews

C. G.

Klove

(1964). Instruction manual for the Adult Neuropsychology Test Battery. Madison: University of Wisconsin Medical School.

34.

Mayhew

D. R.

Simpson

H. M.

Ferguson

S. A.

(2006). Collisions involving senior drivers: High-risk conditions and locations. Traffic Injury Prevention, 7, 117–124.

35.

Owsley

Ball

McGwin

Jr. Sloane

M. E.

Roenker

D. L.

White

M. F.

Overley

E. T.

(1998). Visual processing impairment and risk of motor vehicle crash among older adults. JAMA: The Journal of the American Medical Association, 279, 1083–1088.

36.

Pollatsek

Fisher

D. L.

Pradhan

(2006). Identifying and remedying failures of selective attention in younger drivers. Current Directions in Psychological Science, 15, 255–259.

37.

Ponds

Brouwer

van Wolffelaar

(1988). Age differences in divided attention in a simulated driving task. Journal of Gerontology, 6, 151–156.

38.

Pradhan

A. K.

Hammel

K. R.

DeRamus

Pollatsek

Noyce

D. A.

Fisher

D. L.

(2005). Using eye movements to evaluate effects of driver age on risk perception in a driving simulator. Human Factors, 47, 840–852.

39.

Reitan

R. M.

(1955). The relation of the trail making test as an indicator of organic brain damage. Journal of Consulting Psychology, 19, 393–394.

40.

Reitan

R. M.

(1958). Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.

41.

Rusch

Schall

Jr. Gavin

Lee

J. D.

Vecera

Rizzo

(in press). Directing driver attention with augmented reality cues. Transportation Research Part F: Traffic Psychology and Behaviour.

42.

Salthouse

T. A.

(1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428.

43.

Schall

Jr. Rusch

Lee

Vecera

Rizzo

(2010). Attraction without distraction: Effects of augmented reality cues on driver hazard perception. Journal of Vision, 10, 236.

44.

Scott

J. J.

Gray

(2008). A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Human Factors, 50, 264–275.

45.

Sorkin

R. D.

(1988). Why are people turning off our alarms? Journal of the Acoustical Society of America, 84, 1107–1108.

46.

Stalvey

B. T.

Owsley

Sloane

M. E.

Ball

K. K.

(1999). The Life Space Questionnaire: A measure of the extent of mobility of older adults. Journal of Applied Gerontology, 18, 460–478.

47.

Tonnis

Sandor

Lange

Klinker

Bubb

(2005). Experimental evaluation of an augmented reality visualization for directing a car driver’s attention. In Proceedings of Symposium on Mixed and Augmented Reality (ISMAR ’05) (pp. 56–59). Washington, DC: IEEE Computer Society.

48.

E. Y.

Rizzo

Anderson

S. W.

Shi

Dawson

J. D.

(2005). Driver landmark and traffic sign identification in early Alzheimer’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 76, 764–768.

49.

E. Y.

Rizzo

Johnson

A. M.

Dastrup

Anderson

S. W.

Dawson

J. D.

(2009). Road safety in drivers with Parkinson disease. Neurology, 73, 2112–2119.

50.

Wickens

C. D.

Dixon

S. R.

(2007). The benefits of imperfect diagnostic automation: A 517 synthesis of the literature. Theoretical Issues in Ergonomics Science, 8, 201–212.

51.

Yeh

Wickens

C. D.

(2001). Display signaling in augmented reality: Effects of cue reliability and image realism on attention allocation and trust calibration. Human Factors, 43, 355–365.