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

这个项目通过测量人体在一段时间内对系统工作的反应来开发人类对自主系统的信任模型。每天，人们在运营环境中使用高度自治的系统，例如在仓库配送设施中与机器人合作，或者驾驶飞机。如果一个人对自治的信任相对于它的能力来说太高或太低，这个人可能会过于依赖它，或者根本不使用它。随着某人与自主系统互动并学习其能力，信任会随着时间的推移而变化。然而，人们缺乏对自己信任的认识会影响行为，从而影响性能和安全。当某人在做这些任务时，很难衡量他们的信任，因为准确的信任衡量往往会扰乱正在进行的工作。如果信任可以随着时间的推移而持续测量，那么人与自主系统的交互就可以变得更安全、更高效。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的、经过技术培训的劳动力队伍。将使用自主系统工作的人通常没有接受过关于信任的重要性以及信任如何随着时间的推移而改变的培训。此外，目前服务不足和代表性不足的人口进入这一紧缺劳动力的机会有限。为了满足这些需求，该项目通过根据生理信号对信任动态进行建模，有助于从根本上理解操作环境中的信任。信任的变化表现为生理反应，这可以通过监测心脏、眼睛、皮肤和大脑来检测。虽然人们对估计信任的生理监测越来越感兴趣，但这项工作仅限于理想条件下的实验室，尚未成功应用于操作环境。该项目有四个研究目标(R#)。R1使用生理特征对初始信任进行建模，这些生理特征用于预测运营商的自我报告信任。R2评估在与自主反复交互后学习到的信任动态。当自主代理的可靠性发生变化时，R3通过评估运营商的信任动态来研究信任校准。R4试图了解可穿戴传感器在操作环境中建立信任模型的效用。使用了两个演示环境：一个人在配送仓库中与模拟机器人一起工作来完成订单，另一个人在自动飞行规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工作人员了解信任的重要性，并使下一代人类自主团队的研究人员和操作员参与进来。E1通过针对HAT操作员的培训模块，培训操作员信任对工作场所安全和性能的重要性。它还通过一系列操作员访谈来告知研究目标，以确定本研究所使用的任务。E2增加了科罗拉多州STEM的机会和参与度。E2中的主要工作是管理名为Traveling Trunks的体验式学习模块，这是一种易于管理的课程，使用HAT作为挂钩来提高学生的兴趣。它们在农村、服务不足的高中教室中传播。E2还纳入了外展和研究指导，以进一步加强STEM管道。总而言之，这些目标广泛地促进了对自主信任的更好理解，以揭开其能力的神秘面纱，并促进在运营环境中明智地采用自主。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.