CRII: CHS: Bridging the Age-Related Performance Gap: Multimodal Interfaces to Support Older Adults in Transitioning to Manual Control in Autonomous Systems

先进的自主系统有可能在许多安全关键的运输和工作环境（如驾驶、医疗和制造业）中显著减少工作量并扩展人类的能力。然而，即使是最复杂的系统也经常受到设计限制和/或可能遇到偶尔的故障，需要人工干预。迄今为止，对于如何最好地帮助不同年龄和能力水平的操作员在各种自主系统中注意、诊断和恢复手动控制，还没有达成共识。65岁及以上的成年人现在是增长最快的年龄组，预计他们将在生命的后期阶段遇到自动化水平不断提高的系统，但感知和认知方面的挑战往往阻碍他们有效地利用这些技术。该项目的目标是更好地理解人类与自动化交互的年龄相关差异，并开始开发支持老年人手动恢复各种自动化技术的方法和工具。综合感官反馈将作为一种潜在的技术进行探索，因为它已被证明可以改善注意力管理并使老年人受益。项目成果将有助于更深入地了解不同操作人员的能力和局限性，并将有助于指导下一代人机界面的开发。这项工作对提高许多复杂操作的安全性具有更广泛的意义，例如自动驾驶和自动化过程组装。公共教育活动将包括以社区和研究人口（高年级）为重点的研讨会，大学预科和暑期外展，以及为代表性不足的学生提供的本科研究项目。多模态接口向视觉、听觉和触觉感官通道呈现信息。通过操纵信号参数，这些接口能够捕获注意力，通知操作人员系统状态，并提供决策辅助以执行所需的操作。然而，在权力转移的背景下，这种方法能在多大程度上有效地与一系列在感官和认知能力上有相当大差异的操作员进行沟通，目前还没有得到量化。鉴于先进自主技术的快速发展和未来十年预计的人口变化，填补这些研究空白将是至关重要的。该项目将生成与年龄相关的经验数据，研究自主系统内复杂的相互作用。将进行一系列实验，使用半自动驾驶模拟，涉及不同年龄段的受试者。该研究将量化多模式转换（接管）请求的年龄相关时间差异，确定特定年龄的过渡时间作为前置时间和感官模式通知的函数，并研究各种触觉信号在支持情境感知和减少过渡时间方面的有效性。研究结果有望为人类感知、信息处理和表现的定量和定性模型提供信息。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。

Advanced autonomous systems have the potential to significantly reduce workload and extend human capabilities in a number of safety-critical transportation and work environments, such as driving, medicine, and manufacturing. However, even the most sophisticated systems are often constrained by design limits and/or may experience occasional malfunctions requiring human-in-the-loop manual interventions. To date, there is no consensus on how best to assist operators of varying age and ability levels in noticing, diagnosing, and recovering manual control across a wide range of autonomous systems. Adults 65 years and older are now the fastest growing age group, and are expected to encounter systems with increasing levels of automation throughout later stages of life, yet perceptual and cognitive challenges often prevent their effective use of such technology. The goals of this project are to better understand age-related differences in human-automation interactions, and to begin the development of methods and tools that support the manual recovery of older adults for various automated technologies. Combined sensory feedback will be explored as a potential technique to these ends, as it has been shown to improve attention management and benefit older individuals. Project outcomes will contribute to a more in-depth understanding of the capabilities and limitations of different operator demographics, and will help guide the development of next generation human-machine interfaces. The work has broader implications for enhancing safety in many complex operations, such as autonomous driving and automated process assembly. Public educational activities will include community and study population (senior) focused workshops, pre-college and summer outreach, and undergraduate research programs for underrepresented students.Multimodal interfaces present information to the visual, auditory, and tactile sensory channels. By manipulating signal parameters, these interfaces are able to capture attention, inform operators of system status, and provide decision aids to perform needed actions. However, the extent to which this approach can effectively communicate to a range of operators with considerable variability in sensory and cognitive abilities in the context of transfer-of-authority has not been quantified. Given the rapid development of advanced autonomous technology and the projected population changes expected within the next decade, it will be critical to fill these research gaps. This project will generate age-related empirical data on complex interactions within autonomous systems. A series of experiments will be conducted using semi-autonomous driving simulations and involving subjects from different age groups. The research will quantify age-related time differences in noticing multimodal transition (takeover) requests, determine age-specific transition times as a function of lead time and sensory modality notification, and investigate the effectiveness of various tactile signals to support situation awareness and reduce transition times. Results are expected to inform quantitative and qualitative models of human perception, information processing, and performance.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.