Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems

高级驾驶员辅助系统 计划行为理论 可用性 技术接受与使用的统一理论 巡航控制 技术接受模型 毒物控制 驾驶模拟器 运输工程 期望理论 工程类 计算机科学 模拟 控制(管理) 人机交互 心理学 社会心理学 环境卫生 医学 航空航天工程 人工智能
作者
Md Mahmudur Rahman,Mary F. Lesch,William J. Horrey,Lesley Strawderman
出处
期刊:Accident Analysis & Prevention [Elsevier BV]
卷期号:108: 361-373 被引量:408
标识
DOI:10.1016/j.aap.2017.09.011
摘要

Advanced Driver Assistance Systems (ADAS) are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., have been acknowledged by transportation safety researchers and federal transportation agencies. Although these systems afford safety advantages, they may also challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of these systems into the transportation system. Recognizing the need for research into the factors affecting driver acceptance, this study assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT) for modelling driver acceptance in terms of Behavioral Intention to use an ADAS. Each of these models propose a set of factors that influence acceptance of a technology. Data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants interacted with either a fatigue monitoring system or an adaptive cruise control system combined with a lane-keeping system. Based on their experience, participants responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, etc. A sample of 430 surveys were collected for this study. Results found that all the models (TAM, TPB, and UTAUT) can explain driver acceptance with their proposed sets of factors, each explaining 71% or more of the variability in Behavioral Intention. Among the models, TAM was found to perform the best in modelling driver acceptance followed by TPB. The findings of this study confirm that these models can be applied to ADAS technologies and that they provide a basis for understanding driver acceptance.
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