Older drivers’ experience with automated vehicle technology

Classen, Sherrilene; Mason, Justin; Hwangbo, Seung Woo; Wersal, James; Rogers, Jerry D.; Sisiopiku, Virginia P.

doi:10.1016/j.jth.2021.101107

Cited by 24 publications

(45 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study sample ( N = 104 older drivers; M age = 74.3 ± 6.0) was predominantly White ( n = 93; 89%) and consisted of 47 (45%) males and 57 (55%) females, ranging from 65 to 91 years old. The three AVUPS domain scores and total acceptance score did not differ between genders (binary) ( 15 ); thus, gender was not used as a covariate in the models. Descriptive statistics for participants' age, driving habits , and cognition are displayed in Tables 2 , 3 .…”

Section: Resultsmentioning

confidence: 99%

“…This is a secondary analysis from a pre-posttest experimental design study ( 15 ). For this study we utilized surveys at baseline and after exposure to the automated shuttle (AS).…”

Section: Methodsmentioning

confidence: 99%

“…We enrolled participants who were recruited from community partner interactions, older driver stakeholders, flyers placed in community settings, and Facebook groups. Detailed methodology and research protocol are discussed in our previous publications ( 15 , 36 , 38 ).…”

Section: Methodsmentioning

confidence: 99%

“…Despite current barriers to older drivers accepting AS that include lack of trust in the systems (21,22) and hesitation to use the emerging technology (23,24), research indicates that their perceptions change, positively, after being exposed to an AS, operating at Level 4 of automation (15,25). Some researchers have assessed user perceptions (alone) via survey (26)(27)(28)(29), while others have reported on favorable passenger experiences in AS after riding it (30).…”

Section: Older Drivers and Autonomous Shuttlesmentioning

confidence: 99%

“…The AS—one mode in the family of shared mobility services ( 15 ) holds plausible opportunities to allow older drivers who require an alternative to automobile driving, to stay mobile in their communities. Particularly, the use of AS, may preserve independence in community mobility among the aging population with cognitive (and/or other) declines ( 16 ).…”

Section: Literature Reviewmentioning

confidence: 99%

See 4 more Smart Citations

Predicting Autonomous Shuttle Acceptance in Older Drivers Based on Technology Readiness/Use/Barriers, Life Space, Driving Habits, and Cognition

et al. 2021

Self Cite

View full text Add to dashboard Cite

Shared autonomous vehicle services (i. e., automated shuttles, AS) are being deployed globally and may improve older adults (>65 years old) mobility, independence, and participation in the community. However, AS must be user friendly and provide safety benefits if older drivers are to accept and adopt this technology. Current potential barriers to their acceptance of AS include a lack of trust in the systems and hesitation to adopt emerging technology. Technology readiness, perceived ease of use, perceived barriers, and intention to use the technology, are particularly important constructs to consider in older adults' acceptance and adoption practices of AS. Likewise, person factors, i.e., age, life space mobility, driving habits, and cognition predict driving safety among older drivers. However, we are not sure if and how these factors may also predict older adults' intention to use the AS. In the current study, we examined responses from 104 older drivers (Mage = 74.3, SDage = 5.9) who completed the Automated Vehicle User Perception Survey (AVUPS) before and after riding in an on-road automated shuttle (EasyMile EZ10). The study participants also provided information through the Technology Readiness Index, Technology Acceptance Measure, Life Space Questionnaire, Driving Habits Questionnaire, Trail-making Test Part A and Part B (TMT A and TMT B). Older drivers' age, cognitive scores (i.e., TMT B), driving habits (i.e., crashes and/or citations, exposure, and difficulty of driving) and life space (i.e., how far older adults venture from their primary dwelling) were entered into four models to predict their acceptance of AVs—operationalized according to the subscales (i.e., intention to use, perceived barriers, and well-being) and the total acceptance score of the AVUPS. Next, a partial least squares structural equation model (PLS-SEM) elucidated the relationships between, technology readiness, perceived ease of use, barriers to AV acceptance, life space, crashes and/or citations, driving exposure, driving difficulty, cognition, and intention to use AS. The regression models indicated that neither age nor cognition (TMT B) significantly predicted older drivers' perceptions of AVs; but their self-reported driving difficulty (p = 0.019) predicted their intention to use AVs: R2 = 6.18%, F (2,101) = 4.554, p = 0.040. Therefore, intention to use was the dependent variable in the subsequent PLS-SEM. Findings from the PLS-SEM (R2 = 0.467) indicated the only statistically significant predictors of intention to use were technology readiness (β = 0.247, CI = 0.087-0.411) and barriers to AV acceptance (β = −0.504, CI = 0.285-0.692). These novel findings provide evidence suggesting that technology readiness and barriers must be better understood if older drivers are to accept and adopt AS.

show abstract

Section: Resultsmentioning

confidence: 99%

“…This is a secondary analysis from a pre-posttest experimental design study ( 15 ). For this study we utilized surveys at baseline and after exposure to the automated shuttle (AS).…”

Section: Methodsmentioning

confidence: 99%