5228 University of Wisconsin-Madison, USA.
158055 J.D. Power, Troy, Michigan, USA.
Hum Factors. 2020 Mar;62(2):260-277. doi: 10.1177/0018720819872672. Epub 2019 Sep 10.
This study examined attitudes toward self-driving vehicles and the factors motivating those attitudes.
Self-driving vehicles represent potentially transformative technology, but achieving this potential depends on consumers' attitudes. Ratings from surveys estimate these attitudes, and open-ended comments provide an opportunity to understand their basis.
A nationally representative sample of 7,947 drivers in 2016 and 8,517 drivers in 2017 completed the J.D. Power U.S. Tech Choice Study, which included a rating for level of trust with self-driving vehicles and associated open-ended comments. These open-ended comments are qualitative data that can be analyzed quantitatively using structural topic modeling. Structural topic modeling identifies common themes, extracts prototypical comments for each theme, and assesses how the survey year and rating affect the prevalence of these themes.
Structural topic modeling identified 13 topics, such as "Tested for a long time," which was strongly associated with positive ratings, and "Hacking & glitches," which was strongly associated with negative ratings. The topics of "Self-driving accidents" and "Trust when mature" were more prominent in 2017 compared with 2016.
Structural topic modeling reveals reasons underlying consumer attitudes toward vehicle automation. These reasons align with elements typically associated with trust in automation, as well as elements that mediate perceived risk, such as the desire for control as well as societal, relational, and experiential bases of trust.
The analysis informs the debate concerning how safe is safe enough for automated vehicles and provides initial indicators of what makes such vehicles feel safe and trusted.
本研究考察了人们对自动驾驶汽车的态度，以及影响这些态度的因素。
自动驾驶汽车代表着潜在的变革性技术，但要实现这一潜力，取决于消费者的态度。调查评分估计了这些态度，而开放式评论则提供了理解其基础的机会。
2016 年，7947 名驾驶员和 2017 年 8517 名驾驶员完成了 J.D. Power 美国技术选择研究，其中包括对自动驾驶汽车信任程度的评分以及相关的开放式评论。这些开放式评论是定性数据，可以使用结构主题建模进行定量分析。结构主题建模确定了常见主题，为每个主题提取典型评论，并评估调查年份和评分如何影响这些主题的流行程度。
结构主题建模确定了 13 个主题，例如“经过长时间测试”，与积极评分强烈相关，以及“黑客攻击和故障”，与消极评分强烈相关。与 2016 年相比，2017 年“自动驾驶事故”和“成熟时的信任”等主题更为突出。
结构主题建模揭示了消费者对车辆自动化态度的背后原因。这些原因与通常与自动化信任相关的因素以及感知风险的因素相一致，例如对控制的渴望以及信任的社会、关系和经验基础。
该分析为有关自动驾驶汽车安全到何种程度的争论提供了信息，并提供了使此类车辆感到安全和值得信任的初步指标。
