研究影响司机使用高级驾驶辅助系统的关键因素
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
- 通过机器学习和SHAP分析识别影响采纳的关键因素
- 信任度高者使用率更高，可靠性担忧是主要障碍
- 年龄、性别、驾驶习惯等影响接受度，适合车企参考
高级驾驶辅助系统（ADAS）通过提升环境感知能力、减少人为失误来增强高速公路安全性。然而，误解、信任问题和知识盲区阻碍了其广泛采用。本研究调查了乘用车司机对ADAS的感知、信息来源及使用模式。基于全美范围的调查数据，采用机器学习模型预测ADAS采纳行为，并利用SHAP（SHapley Additive Explanations）识别关键影响因素。结果表明，信任度越高，ADAS使用频率越高；而对系统可靠性的担忧仍是主要障碍。特定功能如前向碰撞预警（Forward Collision Warning）和驾驶员监控系统（Driver Monitoring Systems）显著提升采纳可能性。人口统计学因素（年龄、性别）和驾驶习惯（经验、频率）也影响接受程度。研究强调社会经济、人口结构与行为因素在ADAS采纳中的作用，为汽车制造商、政策制定者及安全倡导者提供提升认知、信任与可用性的指导。
原文摘要 · Abstract (English)
Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Specific features, such as Forward Collision Warning and Driver Monitoring Systems, significantly influence adoption likelihood. Demographic factors (age, gender) and driving habits (experience, frequency) also shape ADAS acceptance. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability.
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