{"title":"Experience of drivers of all age groups in accepting autonomous vehicle technology","authors":"","doi":"10.1080/15472450.2023.2197115","DOIUrl":null,"url":null,"abstract":"<div><p>Autonomous vehicles (AVs) may benefit the health and safety of drivers across the driving lifespan, but perceptions of drivers are not known. Lived experiences of drivers exposed to AVs in combination with surveys, can more accurately reveal their perceptions. We quantified facilitators and barriers from data collected in older (N = 104) and younger drivers (N = 106). Perceptions were assessed via Autonomous Vehicle User Perception Survey (AVUPS) subscales (i.e., <em>intention to use</em>, <em>barriers</em>, <em>well-being</em>, and <em>acceptance</em>) pertaining to group exposure (simulator first [SF] or autonomous shuttle first [ASF]). We quantified the effects of group, time, and group × time interaction. Multiple linear regressions identified predictors (e.g., <em>optimism</em>, <em>ease of use</em>, <em>life space</em>, <em>driving exposure</em>, and <em>driving difficulty, age, gender, race)</em> of the AVUPS subscales. The regression analyses indicated that <em>optimism</em> and <em>ease of use</em> positively predicted <em>intention to use</em>, <em>barriers</em>, <em>well-being</em>, and the <em>total acceptance</em> score. <em>Driving difficulty</em> significantly predicted <em>barriers</em>, whereas <em>miles driven</em> negatively predicted <em>well-being.</em> The regression results indicated that predictors of user <em>acceptance</em> of AV technology included <em>age, race, optimism</em>, <em>ease of use,</em> with 33.6% of the variance in <em>acceptance</em> explained. The findings reveal foundational information about driver <em>acceptance</em>, <em>intention to use</em>, <em>barriers</em>, and <em>well-being</em> related to AVs. New knowledge pertains to how <em>demographics</em>, <em>optimism</em>, <em>ease of use</em>, <em>life space</em>, <em>driving exposure</em>, and <em>driving difficulty</em> inform AV acceptance. We provided strategies to inform city planners and other stakeholders on improving upon deployment practices of AVs.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 5","pages":"Pages 651-667"},"PeriodicalIF":2.8000,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/org/science/article/pii/S1547245023000415","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
Autonomous vehicles (AVs) may benefit the health and safety of drivers across the driving lifespan, but perceptions of drivers are not known. Lived experiences of drivers exposed to AVs in combination with surveys, can more accurately reveal their perceptions. We quantified facilitators and barriers from data collected in older (N = 104) and younger drivers (N = 106). Perceptions were assessed via Autonomous Vehicle User Perception Survey (AVUPS) subscales (i.e., intention to use, barriers, well-being, and acceptance) pertaining to group exposure (simulator first [SF] or autonomous shuttle first [ASF]). We quantified the effects of group, time, and group × time interaction. Multiple linear regressions identified predictors (e.g., optimism, ease of use, life space, driving exposure, and driving difficulty, age, gender, race) of the AVUPS subscales. The regression analyses indicated that optimism and ease of use positively predicted intention to use, barriers, well-being, and the total acceptance score. Driving difficulty significantly predicted barriers, whereas miles driven negatively predicted well-being. The regression results indicated that predictors of user acceptance of AV technology included age, race, optimism, ease of use, with 33.6% of the variance in acceptance explained. The findings reveal foundational information about driver acceptance, intention to use, barriers, and well-being related to AVs. New knowledge pertains to how demographics, optimism, ease of use, life space, driving exposure, and driving difficulty inform AV acceptance. We provided strategies to inform city planners and other stakeholders on improving upon deployment practices of AVs.
期刊介绍:
The Journal of Intelligent Transportation Systems is devoted to scholarly research on the development, planning, management, operation and evaluation of intelligent transportation systems. Intelligent transportation systems are innovative solutions that address contemporary transportation problems. They are characterized by information, dynamic feedback and automation that allow people and goods to move efficiently. They encompass the full scope of information technologies used in transportation, including control, computation and communication, as well as the algorithms, databases, models and human interfaces. The emergence of these technologies as a new pathway for transportation is relatively new.
The Journal of Intelligent Transportation Systems is especially interested in research that leads to improved planning and operation of the transportation system through the application of new technologies. The journal is particularly interested in research that adds to the scientific understanding of the impacts that intelligent transportation systems can have on accessibility, congestion, pollution, safety, security, noise, and energy and resource consumption.
The journal is inter-disciplinary, and accepts work from fields of engineering, economics, planning, policy, business and management, as well as any other disciplines that contribute to the scientific understanding of intelligent transportation systems. The journal is also multi-modal, and accepts work on intelligent transportation for all forms of ground, air and water transportation. Example topics include the role of information systems in transportation, traffic flow and control, vehicle control, routing and scheduling, traveler response to dynamic information, planning for ITS innovations, evaluations of ITS field operational tests, ITS deployment experiences, automated highway systems, vehicle control systems, diffusion of ITS, and tools/software for analysis of ITS.