基于自动驾驶汽车接受度的个性化用户研究

An Overview of Personalized User Research Based on Acceptance of Autonomous Vehicles

中文关键词: 自动驾驶汽车  接受度  个性化设计  用户体验

英文关键词: s of the 2019 CHI Conference on Human Factors in Computing Systems. Berlin:CHI, 2019:1-6.

中文摘要:

      目的 自动驾驶汽车出现以来，如何提升其大众接受度一直是学术界和工业界的关注热点。本文通过对影响自动驾驶汽车接受度的个性特征进行梳理，为后续提升用户接受度的个性化设计实践提供参考。方法 对近10年来有关自动驾驶接受度的研究进行系统性梳理，总结了社会人口学特征、经验水平和心理特质三类用户因素对自动驾驶接受度的影响。结果 影响自动驾驶汽车接受度的社会人口学因素主要包括性别、年龄、地域、教育、收入，以及身体状况等方面。用户驾驶经验和先验知识水平亦会影响自动驾驶汽车的接受度。心理特质是解释用户接受度差异的核心因素，包括大五人格、自我认知、个人控制、焦虑特征等维度。根据前述用户特征可知，从包容性设计、娱乐交互组件、信息交换方式、自动驾驶风格和外观五个方面开展个性化设计将有助于提升用户接受度。结论 基于用户特征的个性化设计实践，将是提升用户自动驾驶汽车接受度的重要途径。未来应进一步开展多维用户因素的交互机制及权重分析研究，并通过实证研究来明确不同个性化设计的作用，以推进自动驾驶汽车的普及。

英文摘要:

      Since the emergence of autonomous vehicles, improving their acceptance among the general public has been a hot topic of concern in academia and industry. The work aims to provide a comprehensive review of the individual characteristics that affect the acceptance of autonomous vehicles, so as to provide references for personalized design practices to enhance user acceptance. A review of research on the acceptance of autonomous vehicles conducted in the past decade was carried out. The study systematically summarized the effects of three categories of user factors:social demography characteristics, experience level, and psychological traits on the acceptance of autonomous vehicles. Social demography characteristics factors that affected the acceptance of autonomous vehicles mainly included gender, age, geographical location, education, income, and physical condition. User driving experience and prior knowledge also impacted the acceptance of autonomous vehicles. Psychological traits were the core factors that explained differences in user acceptance, including the dimensions of the big five personality traits, self-perception, personal control, and anxiety characteristics. Personalized design focusing on the above-mentioned user characteristics, including inclusive design, entertainment interaction components, information exchange methods, autonomous driving styles, and appearance, can contribute to improving user acceptance. Personalized design practices based on user characteristics will be an important approach to enhancing the acceptance of autonomous vehicles. In the future, further research is needed to explore the interactive mechanisms and weight analysis of multidimensional user factors, and empirical studies should be conducted to clarify the effects of different personalized designs, thus promoting the popularization of autonomous vehicles.