用机器学习分析年轻人对自动驾驶的信任影响因素
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
- 基于1457人调查数据，用机器学习识别信任关键因子
- 感知风险收益、使用可行性态度等是核心预测因素
- 结果对自动驾驶人性化设计有重要指导意义
低信任是自动驾驶汽车普及的主要障碍。为设计可信的自动驾驶汽车，需深入理解影响人们信任判断的个体特质、态度和经历。本研究基于1457名年轻成年人的调查数据，运用机器学习方法分析涵盖心理社会属性、认知特征、驾驶风格、经验及对自动驾驶风险与收益的感知等多维度因素。通过可解释人工智能技术SHAP，发现感知风险与收益、对可行性和可用性的态度、制度信任、先前使用经验以及个人心智模型是最重要预测因子。令人意外的是，许多心理社会特征及与技术和驾驶相关的特定因素并非强预测因子。研究强调了个体差异在面向多样化群体设计可信自动驾驶系统中的重要性，并为未来设计与研究提供关键启示。
原文摘要 · Abstract (English)
Low trust remains a significant barrier to Autonomous Vehicle (AV) adoption. To design trustworthy AVs, we need to better understand the individual traits, attitudes, and experiences that impact people's trust judgements. We use machine learning to understand the most important factors that contribute to young adult trust based on a comprehensive set of personal factors gathered via survey (n = 1457). Factors ranged from psychosocial and cognitive attributes to driving style, experiences, and perceived AV risks and benefits. Using the explainable AI technique SHAP, we found that perceptions of AV risks and benefits, attitudes toward feasibility and usability, institutional trust, prior experience, and a person's mental model are the most important predictors. Surprisingly, psychosocial and many technology- and driving-specific factors were not strong predictors. Results highlight the importance of individual differences for designing trustworthy AVs for diverse groups and lead to key implications for future design and research.
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