CAREER: Enabling Human-Aware and Responsive Automation through Cognitive State Modeling and Estimation

这项教师早期职业发展计划（Career）拨款将资助使机器、机器人和车辆等自主系统能够安全协作地响应人类互动的研究，从而促进科学的进步，促进国家的繁荣和福利。预计整个医疗保健、制造和运输行业的自动化程度将显著提高。在涉及人类参与者的情况下，自动化的安全集成面临的主要挑战是缺乏人类行为、意图和认知状态的精确数学模型，以及从人类那里收集相关实时数据的可靠方法。该项目通过建立一个新的建模框架来克服这些挑战，该框架考虑了社会科学中建立的人类认知结构，例如信任、工作量、感知风险和自信，同时可以进行严格的数学分析。它展示了这个框架如何促进传感和控制算法的设计，这些算法可以让自动系统确定，例如，在驾驶自动驾驶汽车时，人类接管任务的信心有多大。通过研究和教育的紧密结合，该项目将培养工程专业学生解决与社会自动化快速发展相关的社会政治和伦理挑战。这将通过新的跨学科课程和经常性的沉浸式学习体验来实现，这些体验将学生与州和联邦政策制定者联系在一起。本研究旨在为人机交互的控制理论框架奠定基础，该框架可以利用实时数据不断提高预测精度。它通过定义人类认知状态空间并在与控制设计标准工具兼容的模型公式中描述其动态特性来实现这一目标。这种表述的一个独特特点是它的可解释性，通过将模型建立在管理人类决策的既定概念框架中来维持。将创建一种用于实时参数和认知状态估计的原则方法，该方法将来自具有不同功能和查询成本的多个传感器的信息混合在一起，并能够适应不同的个体。将建立一种激发人类认知动态的可推广技术，以及选择最适合特定人机交互环境的认知状态估计算法的指导方针。计划进行实验室和现场试验，包括使用配备1级驾驶员辅助功能的卡车进行试验，以验证建模框架和估计算法。该奖项反映了美国国家科学基金会的法定使命，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。

This Faculty Early Career Development Program (CAREER) grant will fund research that enables autonomous systems such as machines, robots, and vehicles, to respond safely and collaboratively to human interactions, thereby promoting the progress of science, and advancing the national prosperity and welfare. Significant increases in automation are expected across the healthcare, manufacturing, and transportation sectors. Major challenges to the safe integration of automation in situations involving human participants are the lack of accurate mathematical models of human behavior, intent, and cognitive state, as well as of reliable ways of gathering relevant real-time data from humans. This project overcomes these challenges by building a new modeling framework that accounts for human cognitive constructs established within the social sciences, such as trust, workload, perceived risk, and self-confidence, while being amenable to rigorous mathematical analysis. It demonstrates how this framework can facilitate the design of sensing and control algorithms that can allow an autonomous system to determine, for example, how confident the human is in taking over a task, say during driving of an autonomous vehicle. Through close integration of research and education, the project will train engineering students to tackle questions surrounding socio-political and ethical challenges associated with the rapid expansion of automation in society. This will be achieved through new interdisciplinary coursework and recurrent immersive learning experiences that bring students together with state and federal policymakers. This research aims to develop the foundations of a control-theoretic framework for human-automation interaction that can use real-time data to continually improve prediction accuracy. It achieves this aim by defining a human cognitive state space and characterizing its dynamics in a model formulation that is compatible with standard tools of control design. A unique feature of this formulation is its interpretability, maintained by grounding the model in established conceptual frameworks governing human decision-making. A principled methodology for real-time parameter and cognitive state estimation will be created that blends information from multiple sensors with different capabilities and costs of querying, and enables adaptation to different individuals. A generalizable technique for exciting human cognitive dynamics, as well as guidelines for choosing the cognitive state estimation algorithm best suited for a particular human-automation interaction context, will be established. Laboratory and field experiments, including tests using trucks equipped with Level 1 driver assistance features, are planned for validation of the modeling framework and estimation algorithms.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.