Age and Technology Readiness Influences on Adults’ Experiences with Highly Automated Vehicles

Adults’ access to transportation may improve after the deployment of highly automated vehicles (HAVs). However, the public is hesitant to accept HAVs. This study investigated the influence of adults’ (n = 210) age—younger (18–29 years), middle-aged (30–64 years), young-old (65–74 years), and older (>75 years)—and readiness to use technology on their intention to use, perceived barriers, well-being, and acceptance of HAVs before and after riding in an autonomous shuttle and a driving simulator operating in autonomous mode. Two-way mixed analysis of variances (ANOVAs) were deployed to assess changes between groups in Automated Vehicle User Perception Survey (AVUPS) domain scores after exposure to the HAVs. When controlling for baseline differences, well-being increased after exposure to HAVs for young-old adults compared with both middle-aged and younger adults. Both young-old and older adults reported greater intention to use compared with middle-aged adults. Regardless of age or technology readiness, adults’ intention to use HAVs increased after experiencing the HAVs. Explorers displayed favorable perceptions (i.e., AVUPS domain scores) of HAVs whereas hesitators reported lower intention to use and acceptance of HAVs and more perceived barriers to adopting HAVs. The three other technology readiness classifications, skeptics, pioneers, and avoiders held relatively similar views of HAVs before and after exposure to both modes of HAVs. General beliefs about technology may inform automakers, autonomous mobility companies, human factors engineers, and driving rehabilitation scientists of adults’ willingness to embrace emerging technology, especially when widespread exposure to the technology is not yet a reality.

[1]  V. Sisiopiku,et al.  Experience of Drivers of All Age Groups in Accepting Autonomous Vehicle Technology , 2023, SSRN Electronic Journal.

[2]  V. Sisiopiku,et al.  Older drivers’ experience with automated vehicle technology , 2021 .

[3]  Hany M. Hassan,et al.  Older adults and their willingness to use semi and fully autonomous vehicles: A structural equation analysis , 2021, Journal of Transport Geography.

[4]  T. Lajunen,et al.  Attitudes Toward Four Levels of Self-Driving Technology Among Elderly Drivers , 2021, Frontiers in Psychology.

[5]  L. Molnar,et al.  Adapted Stopping Elderly Accidents, Deaths, and Injuries Questions for Falls Risk Screening: Predictive Ability in Older Drivers. , 2021, American journal of preventive medicine.

[6]  Laurie F. Beck,et al.  Older Adult Willingness to Use Fully Autonomous Vehicle (FAV) Ride Sharing , 2021, Geriatrics.

[7]  V. Sisiopiku,et al.  Older Drivers’ Motion and Simulator Sickness before and after Automated Vehicle Exposure , 2021, Safety.

[8]  V. Sisiopiku,et al.  Construct Validity and Test–Retest Reliability of the Automated Vehicle User Perception Survey , 2021, Frontiers in Psychology.

[9]  Shelia R. Cotten,et al.  Willingness to Use Automated Vehicles: Results From a Large and Diverse Sample of U.S. Older Adults , 2021, Gerontology & geriatric medicine.

[10]  V. Sisiopiku,et al.  Face and Content Validity of an Automated Vehicle Road Course and a Corresponding Simulation Scenario , 2020, Frontiers in Future Transportation.

[11]  Jennifer L. Campos,et al.  Older adults' acceptance of fully automated vehicles: Effects of exposure, driving style, age, and driving conditions. , 2020, Accident; analysis and prevention.

[12]  Tan Yigitcanlar,et al.  Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature , 2020, Journal of Open Innovation: Technology, Market, and Complexity.

[13]  V. Sisiopiku,et al.  Establishing Face and Content Validity of a Survey to Assess Users’ Perceptions of Automated Vehicles , 2020 .

[14]  S. Cotten,et al.  In care and digitally savvy? Modern ICT use in long-term care institutions , 2020, Educational Gerontology.

[15]  V. Sisiopiku,et al.  Older Drivers' Experience With Automated Vehicle Technology: Interim Analysis of a Demonstration Study , 2020, Frontiers in Sustainable Cities.

[16]  D. Paddeu,et al.  Passenger comfort and trust on first-time use of a shared autonomous shuttle vehicle , 2020 .

[17]  D. van Lierop,et al.  How will older adults use automated vehicles? Assessing the role of AVs in overcoming perceived mobility barriers , 2020, Transportation Research Part A: Policy and Practice.

[18]  William Payre,et al.  What impressions do users have after a ride in an automated shuttle? An interview study , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[19]  R. Pak,et al.  Looking for Age Differences in Self-Driving Vehicles: Examining the Effects of Automation Reliability, Driving Risk, and Physical Impairment on Trust , 2019, Front. Psychol..

[20]  J. Hudson,et al.  People’s attitudes to autonomous vehicles , 2019, Transportation Research Part A: Policy and Practice.

[21]  Peng Liu,et al.  Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors , 2019, Transportation Research Part C: Emerging Technologies.

[22]  Hany M. Hassan,et al.  Factors That Influence Older Canadians’ Preferences for using Autonomous Vehicle Technology: A Structural Equation Analysis , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[23]  J. D. Winter,et al.  Acceptance of Driverless Vehicles: Results from a Large Cross-National Questionnaire Study , 2018 .

[24]  Edwin R. Galea,et al.  Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age , 2018 .

[25]  Joseph Sharit,et al.  Factors Predicting Decisions About Technology Adoption Among Older Adults , 2017, Innovation in aging.

[26]  Kay W. Axhausen,et al.  Literature review on surveys investigating the acceptance of automated vehicles , 2017, Transportation.

[27]  Florian Evéquoz,et al.  On the Road with an Autonomous Passenger Shuttle: Integration in Public Spaces , 2017, CHI Extended Abstracts.

[28]  Lisa J. Molnar,et al.  Use, perceptions, and benefits of automotive technologies among aging drivers , 2016, Injury Epidemiology.

[29]  Michael Sivak,et al.  Motorists' preferences for different levels of vehicle automation , 2015 .

[30]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[31]  Sil Aarts,et al.  Older Adults' Reasons for Using Technology while Aging in Place , 2015, Gerontology.

[32]  Ananthanarayanan Parasuraman,et al.  An Updated and Streamlined Technology Readiness Index , 2015 .

[33]  Michael Sivak,et al.  A Survey of Public Opinion about Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia , 2014 .

[34]  India U.S. Consulate Chennai U.S. Department of Health & Human Services , 2014 .

[35]  J. Wiles,et al.  The meaning of "aging in place" to older people. , 2012, The Gerontologist.

[36]  Drew A. Linzer,et al.  poLCA: An R Package for Polytomous Variable Latent Class Analysis , 2011 .

[37]  J. Cummings,et al.  The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment , 2005, Journal of the American Geriatrics Society.

[38]  J. S. Stevenson In older adults , 1980 .

[39]  Wei Zhang,et al.  The roles of initial trust and perceived risk in public’s acceptance of automated vehicles , 2019, Transportation Research Part C: Emerging Technologies.

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

[41]  Daniel J. Fagnant,et al.  PREPARING A NATION FOR AUTONOMOUS VEHICLES: 1 OPPORTUNITIES, BARRIERS AND POLICY RECOMMENDATIONS FOR 2 CAPITALIZING ON SELF-DRIVEN VEHICLES , 2014 .

[42]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[43]  J. Prochaska,et al.  Transtheoretical therapy: Toward a more integrative model of change. , 1982 .