Trust in Automation: Research Review and Future Perspectives
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摘要: 随着自动化能力的快速提升, 人机关系发生深刻变化, 人的角色逐渐从自动化的主要控制者转变为与其共享控制的合作者. 为了实现绩效和安全目标, 人机协同控制需要操作人员适当地校准他们对自动化机器的信任, 自动化信任问题已经成为实现安全有效的人机协同控制所面临的最大挑战之一. 本文回顾了自动化信任相关文献, 围绕自动化信任概念、模型、影响因素及测量方法, 对迄今为止该领域的主要理论和实证工作进行了详细总结. 最后, 本文在研究综述和相关文献分析的基础上提出了现有自动化信任研究工作中存在的局限性, 并从人机系统设计的角度为未来的自动化信任研究提供一些建议.Abstract: With the rapid improvement of automation capability, the human-machine relationship has undergone profound changes. The role of human has gradually changed from the main controller of automation to the partner sharing control with it. Human-machine collaborative control requires the human operator to appropriately calibrate their trust in automatic machine in order to achieve performance and safety goals. Trust in automation has proved to be one of the greatest challenges to achieve safe and effective human-machine collaborative control. This paper reviews the literature related to trust in automation, and summarizes the main theoretical and empirical work in this field up to now in detail, centering on the concepts, models, influencing factors and measurement methods of trust in automation. Finally, this paper explains limitations that are present in existing research works based on research review and relevant literature analysis, and provides some suggestions for future research on trust in automation from the point of human-machine system design.
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图 2 自动化信任定义涉及的重要特征
Fig. 2 Important characteristics involved in the definitions of trust in automation
图 3 自动化信任模型文献的时间分布
Fig. 3 Time distribution of the literature on models of trust in automation
图 6 与文献发表趋势、重点应用领域及研究对象相关的自动化信任文献分析结果
Fig. 6 Results of literature analysis related to literature publication trends, key application areas and research objects of trust in automation
表 1 自动化信任计算模型总结
Table 1 Summary of computational models of trust in automation
类型 离线信任模型 在线信任模型 输入 先验参数 先验参数及实时行为和
生理及神经数据作用 在可能的情景范围内进行
模拟以预测自动化信任水平在系统实际运行期间实时
估计自动化信任水平应用 用于自动化系统设计阶段 用于自动化系统部署阶段 结果 静态改进自动化设计 动态调整自动化行为 
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表 2 常见的自动化信任行为测量方法总结
Table 2 Summary of common behavioural measures of trust in automation
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表 3 重要的生理及神经测量方法及其依据
Table 3 Important physiological and neural measures of trust in automation and their basis
测量方法 方法依据 通过眼动追踪捕获操作者的凝视
行为来对自动化信任进行持续测量.监视行为等显性行为与主观自动化信任的联系更加紧密[77]. 虽然关于自动化信任与监视行为的实验证据并不是单一的[142], 但大多数实证研究表明, 自动化信任主观评分与操作者监视频率之间存在显著的负相关关系[48]. 表征操作者监视程度的凝视行为可以为实时自动化信任测量提供可靠信息[133, 142, 143]. 利用EEG信号的图像特征来检测
操作者的自动化信任状态.许多研究检验了人际信任的神经关联[144-148], 使用神经成像工具检验自动化信任的神经关联是可行的. EEG比其他工具(如功能性磁共振成像)具有更好的时间动态性[149], 在脑-机接口设计中使用EEG图像模式来识别用户认知和情感状态已经具有良好的准确性[149]. 自动化信任是一种认知结构, 利用EEG信号的图像特征来检测操作者的自动化信任校准是可行的, 并且已经取得了较高的准确性[67, 68, 150]. 通过EDA水平推断自动化信任水平 已有研究表明, 较低的自动化信任水平可能与较高的EDA水平相关[151]. 将该方法与其他生理及神经测量方法结合使用比单独使用某种方法的自动化信任测量准确度更高, 例如将EDA与眼动追踪[142]或EEG结合使用[68, 67].
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表 4 自动化信任的主要研究团体及其研究贡献
Table 4 Main research groups of trust in automation and their research contributions
序号 国别 机构 团队及代表学者 研究贡献 文献数 1 美国 美国陆军研究实验室 人类研究和工程局的
Chen J Y C提出基于系统透明度的一系列自动化信任校准方法 26 2 美国 美国空军研究实验室 人类信任与交互分部的Lyons J B 进行军事背景下的自动化信任应用研究 24 3 美国 中佛罗里达大学 仿真模拟与培训学院的Hancock P A 建立人-机器人信任的理论体系并进行相关影响因素实证研究 21 4 美国 克莱姆森大学 机械工程系的Saeidi H和Wang Y 建立基于信任计算模型的自主分配策略来提高人机协作效能 20 5 美国 乔治梅森大学 心理学系的de Visser E J 建立并完善自动化信任修复相关理论, 着重研究
自动化的拟人特征对信任修复的作用18 6 日本 筑波大学 风险工程系的Itoh M和Inagaki T 基于自动化信任校准的人-自动驾驶汽车协同系统设计方法 14
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