CAREER: Physiological Modeling of Longitudinal Human Trust in Autonomy for Operational Environments

职业：作战环境自主纵向人类信任的生理建模

• 批准号: 2238977
• 负责人: Allison Anderson
• 金额: $ 67.47万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238977&HistoricalAwards=false
• 关键词: CAREER; Physiological; Modeling; Longitudinal; Human

This project develops models of human trust in autonomous systems by measuring the body's response to working with the system over time. Every day, people work with highly autonomous systems in operational environments, such as teaming with robots in warehouse distribution facilities or flying an airplane. If a person's trust in the autonomy is too high or low relative to its capability, the person may rely on it too much or not use it at all. Trust changes over time as someone interacts with the autonomous system and learns its capabilities. However, the person's lack of awareness of their own trust influences behavior, and as a result impacts performance and safety. It is difficult to measure someone's trust while they are doing these tasks because accurate measurements of trust often disrupt the work being done. If trust can be measured continuously over time, the person's interaction with the autonomous system can be made safer and more efficient. Given the increased use of autonomy across sectors including manufacturing, aerospace, the tech industry, and military, continuous measures of trust may impact US economy and security. Further, there is a need to prepare a diverse, technically trained workforce for advanced operational environments. People who will be working with autonomous systems are often not trained on the importance of trust and how it may change over time. Further, there are currently limited opportunities for underserved and underrepresented populations to enter this in-demand workforce. To address these needs, the project contributes to fundamental understanding of trust in operational environments by modeling trust dynamics from physiological signals. Changes in trust manifest as physiological responses, which can be detected by monitoring the heart, eye, skin, and brain. While there is increasing interest in physiological monitoring to estimate trust, this work has been constrained to the laboratory under ideal conditions and has yet to be successful applied to operational environments. This project has four Research Goals (R#). R1 models initial trust with physiological features used to predict operators' self-reported trust. R2 assesses learned trust dynamics after repeated interactions with autonomy. R3 investigates trust calibration by assessing operators' trust dynamics when the reliability of the autonomous agent shifts. R4 seeks to understand the utility of wearable sensors to model trust in operational environments. Two demonstration environments are used: a person working with a simulated robot to fill orders in a distribution warehouse, and a person flying an aircraft with the assistance of an automated flight planner. The education component of this project has two educational goals (E#) to train the human-autonomous teaming (HAT) workforce on the importance of trust and engage the next generation of HAT researchers and operators. E1 trains operators on the importance of trust for workplace safety and performance through educational modules for HAT operators. It also informs the research goals through a series of operator interviews to define the task used in this research. E2 increases access and engagement in STEM in Colorado. The key effort in E2 administers experiential learning modules called Traveling Trunks, which are easy-to-administer lessons that use HAT as a hook to increase student interest. They are disseminated in rural, underserved high school classrooms. E2 also incorporates outreach and research mentorship to further reinforce the STEM pipeline. Together, these goals broadly promote an improved understanding of trust in autonomy to demystify its abilities and improve intelligent adoption of autonomy in operational settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目通过测量身体随着时间的推移对系统的反应，开发人类对自治系统的信任模型。 每天，人们都在操作环境中使用高度自主的系统，例如与仓库配送设施中的机器人合作或驾驶飞机。如果一个人对自主权的信任相对于其能力过高或过低，这个人可能会过度依赖它或根本不使用它。 信任随着时间的推移而变化，因为有人与自治系统交互并学习其功能。然而，人们缺乏对自己信任的意识会影响行为，从而影响性能和安全。在执行这些任务时，很难衡量某人的信任度，因为对信任度的准确衡量往往会破坏正在进行的工作。如果信任可以随着时间的推移不断测量，那么人与自治系统的交互可以变得更安全，更有效。鉴于制造业、航空航天、科技行业和军事等行业越来越多地使用自主权，持续的信任措施可能会影响美国的经济和安全。此外，还需要为先进的业务环境培养一支多样化、受过技术培训的工作人员队伍。使用自治系统的人通常没有接受过关于信任的重要性以及它如何随着时间的推移而变化的培训。此外，目前服务不足和代表性不足的人口进入这一需求旺盛的劳动力队伍的机会有限。为了满足这些需求，该项目通过从生理信号建模信任动态，有助于对操作环境中信任的基本理解。信任的变化表现为生理反应，可以通过监测心脏、眼睛、皮肤和大脑来检测。虽然有越来越多的兴趣，在生理监测，以估计信任，这项工作已被限制在理想条件下的实验室，并尚未成功地应用到操作环境。该项目有四个研究目标（R#）。R1模型的初始信任与生理特征用于预测运营商的自我报告的信任。R2在与自主性反复互动后评估学习到的信任动态。R3研究信任校准评估运营商的信任动态时，自主代理的可靠性转移。R4试图了解可穿戴传感器在操作环境中建模信任的实用性。使用两个演示环境：一个人与模拟机器人一起在配送仓库中填写订单，一个人在自动飞行计划器的帮助下驾驶飞机。该项目的教育部分有两个教育目标（E#），即培训人类自主团队（HAT）工作人员了解信任的重要性，并吸引下一代HAT研究人员和运营商。E1通过HAT操作员的教育模块，培训操作员信任工作场所安全和绩效的重要性。它还通过一系列的操作员访谈来定义本研究中使用的任务，从而告知研究目标。E2增加了科罗拉多STEM的访问和参与。E2的主要工作是管理体验式学习模块，称为旅行箱，这是易于管理的课程，使用HAT作为挂钩，以提高学生的兴趣。这些教材在农村、服务不足的高中教室中传播。E2还包括推广和研究导师，以进一步加强STEM管道。总之，这些目标广泛地促进了对自主性信任的理解，以揭开其能力的神秘面纱，并提高自主性在操作环境中的明智采用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。