Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers’ ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions. Older and younger drivers completed a set of common tasks using the IVIS of a representative sample of six different vehicles while they drove along a low-density residential street. Evaluation measures included a Detection Response Task (DRT), to assess both cognitive and visual attention, and subjective measures following each condition using the NASA Task Load Index (TLX). Two age cohorts were evaluated: younger drivers between 21 and 36 years of age, and older drivers between 55 and 75 years of age. Participants completed experimental tasks involving interactions with the IVIS to achieve a specific goal (i.e., using the touch screen to tune the radio to a station; using voice commands to find a specified navigation destination, etc.). Performance of tasks varied according to different modes of interaction available in the vehicles. Older drivers took longer to complete tasks, were slower to react to stimuli, and reported higher task demand when interacting with IVIS. Older drivers stand to benefit the most from advancements in-vehicle technology, but ironically may struggle the most to use them. The results document significant age-related costs in the potential for distraction from IVIS interactions on the road.

[1]  C. Owsley,et al.  Distracted Driving and Risk of Crash or Near-Crash Involvement Among Older Drivers Using Naturalistic Driving Data With a Case-Crossover Study Design , 2019, The journals of gerontology. Series A, Biological sciences and medical sciences.

[2]  Camille L. Wheatley,et al.  Visual and Cognitive Demands of Using Apple CarPlay, Google’s Android Auto and Five Different OEM Infotainment Systems , 2018 .

[3]  Woon Kim,et al.  The Longitudinal Research on Aging Drivers (LongROAD) Study: Understanding the Design and Methods , 2017 .

[4]  A. Gow,et al.  Older Adults Perceptions of Technology and Barriers to Interacting with Tablet Computers: A Focus Group Study , 2017, Front. Psychol..

[5]  David L. Strayer,et al.  Visual and Cognitive Demands of Using In-Vehicle Infotainment Systems , 2017 .

[6]  Erica M. Barhorst-Cates,et al.  Let me be your guide: physical guidance improves spatial learning for older adults with simulated low vision , 2017, Experimental Brain Research.

[7]  Tsippy Lotan,et al.  The challenge of safe driving among elderly drivers , 2017, Healthcare technology letters.

[8]  Erica M. Barhorst-Cates,et al.  The Effects of Restricted Peripheral Field-of-View on Spatial Learning while Navigating , 2016, PloS one.

[9]  David L. Strayer,et al.  Extending the Detection Response Task to Simultaneously Measure Cognitive and Visual Task Demands , 2016 .

[10]  David L. Strayer,et al.  Validating Two Assessment Strategies for Visual and Cognitive Load in a Simulated Driving Task , 2016 .

[11]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[12]  Donald L. Fisher,et al.  SPIDER: A Framework for Understanding Driver Distraction , 2016, Hum. Factors.

[13]  Bodo Winter,et al.  A Very Basic Tutorial for Performing Linear Mixed Effects Analyses: Tutorial 2 , 2015 .

[14]  R. Porter,et al.  Conducting qualitative research in mental health: Thematic and content analyses , 2015, The Australian and New Zealand journal of psychiatry.

[15]  David L. Strayer,et al.  Measuring Cognitive Distraction in the Automobile II: Assessing In-Vehicle Voice-Based InteractiveTechnologies , 2014 .

[16]  Joshua E. Domeyer,et al.  Using Occlusion to Measure the Effects of the NHTSA Participant Criteria on Driver Distraction Testing , 2014 .

[17]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[18]  Feng Guo,et al.  Keep your eyes on the road: young driver crash risk increases according to duration of distraction. , 2014, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[19]  Mijke Rhemtulla,et al.  Planned Missing Data Designs for Developmental Researchers , 2013 .

[20]  David L. Strayer,et al.  Measuring Cognitive Distraction in the Automobile , 2013 .

[21]  Matthew Rizzo,et al.  Distracted driving in elderly and middle-aged drivers. , 2012, Accident; analysis and prevention.

[22]  M. Farage,et al.  Design Principles to Accommodate Older Adults , 2012, Global journal of health science.

[23]  Charlene Hallett,et al.  Driver distraction and driver inattention: definition, relationship and taxonomy. , 2011, Accident; analysis and prevention.

[24]  C. Owsley Aging and vision , 2011, Vision Research.

[25]  Adam Gazzaley,et al.  In Brief , 2011, Nature Reviews Neuroscience.

[26]  B. Ott,et al.  How does dementia affect driving in older patients? , 2010, Aging health.

[27]  Elizabeth N. Mazzae,et al.  Measuring Distraction Potential of Operating In-Vehicle Devices , 2009 .

[28]  Kaarin J Anstey,et al.  The On‐Road Difficulties of Older Drivers and Their Relationship with Self‐Reported Motor Vehicle Crashes , 2009, Journal of the American Geriatrics Society.

[29]  Frank Drews,et al.  Text Messaging During Simulated Driving , 2009, Hum. Factors.

[30]  Jennifer D. Davis,et al.  A longitudinal study of drivers with Alzheimer disease , 2008, Neurology.

[31]  Mary L. Cummings,et al.  Effects of Single Versus Multiple Warnings on Driver Performance , 2007, Hum. Factors.

[32]  John W Graham,et al.  Planned missing data designs in psychological research. , 2006, Psychological methods.

[33]  Louis Tijerina,et al.  Driver Workload Metrics Task 2 Final Report , 2006 .

[34]  Michel Bédard,et al.  Visual Attention and Older Drivers: The Contribution of Inhibition of Return to Safe Driving , 2006, Experimental aging research.

[35]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[36]  John D. Lee,et al.  Preface to the Special Section on Driver Distraction , 2004, Hum. Factors.

[37]  Arthur D. Fisk,et al.  Designing for Older Adults: Principles and Creative Human Factors Approaches , 2004 .

[38]  D. Strayer,et al.  Cell phone-induced failures of visual attention during simulated driving. , 2003, Journal of experimental psychology. Applied.

[39]  Michael J. Goodman,et al.  NHTSA DRIVER DISTRACTION RESEARCH: PAST, PRESENT, AND FUTURE , 2001 .

[40]  A. Hartley,et al.  Age-related differences and similarities in dual-task interference. , 1999, Journal of experimental psychology. General.

[41]  ● Pytorch,et al.  Attention! , 1998, Trends in Cognitive Sciences.

[42]  T. Salthouse The processing-speed theory of adult age differences in cognition. , 1996, Psychological review.

[43]  W H Brouwer,et al.  Divided Attention in Experienced Young and Older Drivers: Lane Tracking and Visual Analysis in a Dynamic Driving Simulator , 1991, Human factors.

[44]  J. Cerella Information processing rates in the elderly. , 1985, Psychological bulletin.

[45]  A. Battersby Plans and the Structure of Behavior , 1968 .

[46]  Claude Marin-Lamellet,et al.  Difficulties experienced by older drivers during their regular driving and their expectations towards Advanced Driving Aid Systems and vehicle automation , 2018 .

[47]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[48]  Donald L Fisher,et al.  Modeling situation awareness and crash risk. , 2014, Annals of advances in automotive medicine. Association for the Advancement of Automotive Medicine. Annual Scientific Conference.

[49]  D. Strayer,et al.  Provided for Non-commercial Research and Educational Use Only. Not for Reproduction, Distribution or Commercial Use. Cognitive Distraction While Multitasking in the Automobile , 2022 .

[50]  Claes Tingvall,et al.  A GROWING PROBLEM OF DRIVER DISTRACTION , 2011 .

[51]  Raymond J. Shaw,et al.  Attention and Aging: A Functional Perspective , 2000 .

[52]  Paul Green,et al.  THE 15-SECOND RULE FOR DRIVER INFORMATION SYSTEMS , 1999 .

[53]  Arthur F. Kramer,et al.  Aging and dual-task performance. , 1996 .

[54]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[55]  Diane Williams,et al.  Age and the complexity hypothesis. , 1980 .