通过结构方程模型理解人类驾驶员对高度自动化车辆的信任

Qingkun Li, Zhenyuan Wang, Weimin Liu, Wenjun Wang, Chao Zeng, Bo Cheng
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引用次数: 0

摘要

高度自动化的车辆预计很快就会普及。对于高度自动化的车辆,驾驶权限在自动驾驶系统和人类驾驶员之间切换。驾驶员对高度自动化车辆(THAV)的适当信任水平对切换过程的安全性起着至关重要的作用。因此,对高度自动化车辆进行THAV评估及影响因素研究是十分必要的。本文基于探索性因子分析和验证性因子分析,建立了THAV的二阶测量模型。然后,基于结构方程模型,系统地探讨了影响THAV的因素。结果表明,所建立的测量模型能够有效地测量THAV。此外,教育程度、年龄和驾驶经验对THAV有显著影响,性别和事故经历对THAV的影响不显著。本研究有助于系统地了解驾驶员对高度自动化车辆的信任程度,以人为本的自动驾驶系统的发展,以及提高高度自动化车辆的接受度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Human Drivers' Trust in Highly Automated Vehicles via Structural Equation Modeling
Highly automated vehicles are expected to become commonplace shortly. Driving authority is switched between the automated driving system and the human driver for highly automated vehicles. The appropriate level of drivers' trust in highly automated vehicles (THAV) plays an essential role in the safety of the switching process. Hence, the assessment of THAV and the investigation of its influencing factors are necessary for highly automated vehicles. In this paper, a second-order measurement model for THAV was established based on exploratory factor analysis and confirmatory factor analysis. Then, the affecting factors of THAV were systematically explored based on structural equation modeling. The results indicated that the proposed measurement model could effectively measure THAV. In addition, education, age, and driving experience had significant effects on THAV, while gender and accident experience showed insignificant effects on THAV. This study contributes to a systematic understanding of drivers' trust in highly automated vehicles, the development of human-centered automated driving systems, and enhancing the acceptance of highly automated vehicles.
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