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

先进的自动驾驶系统有可能在许多安全关键型运输和工作环境中显著减少工作量并扩展人类能力，如驾驶、医疗和制造。然而，即使是最复杂的系统通常也会受到设计限制和/或偶尔会遇到需要人工干预的故障。到目前为止，对于如何最好地帮助不同年龄和能力水平的操作员注意、诊断和恢复广泛的自主系统中的手动控制，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段，预计在生命的后期阶段，他们会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会阻碍他们有效地使用这种技术。该项目的目标是更好地了解人-自动化交互中与年龄相关的差异，并开始开发各种自动化技术支持老年人手动康复的方法和工具。为了达到这些目的，我们将探索组合感觉反馈作为一种潜在的技术，因为它已经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影响，例如自动驾驶和自动化流程组装。公共教育活动将包括以社区和研究人群(老年人)为重点的讲习班，大学前和暑期外展，以及针对未被充分代表的学生的本科生研究计划。多模式界面将信息呈现给视觉、听觉和触觉感觉通道。通过操作信号参数，这些接口能够引起注意，通知操作员系统状态，并提供决策辅助以执行所需的操作。然而，这种方法在多大程度上能够有效地与在权力移交背景下感觉和认知能力有很大差异的一系列操作员进行沟通还没有量化。鉴于先进自主技术的快速发展以及预计未来十年内人口的变化，填补这些研究空白将是至关重要的。该项目将生成与年龄有关的关于自治系统内复杂相互作用的经验数据。将使用半自动驾驶模拟进行一系列实验，涉及不同年龄段的受试者。这项研究将量化与年龄相关的注意多模式转换(接管)请求的时间差，确定特定于年龄的转换时间作为提前时间和感觉通道通知的函数，并调查各种触觉信号支持情景感知和减少转换时间的有效性。预计结果将为人类感知、信息处理和绩效的定量和定性模型提供信息。该奖项反映了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。

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.