This Future of Work at the Human Technology Frontier (FW-HTF) planning project will provide knowledge needed to understand and improve workforce performance of older adults in transportation and delivery systems, which are heavily impacted by recent advances in automated cars, trucks, and drones. While automation may replace a large number of human drivers, new jobs may also arise involving novel functions afforded by the new technology. An outstanding challenge is to help existing workers transition into these new jobs. Aging workers?who may be experiencing age-related cognitive and physical declines and who may find it more difficult to learn and adopt new technologies?will require extra provisions to successfully transition into new roles and remain productive within the transportation and delivery industry. This project will perform fundamental research to advance knowledge of neural and behavioral indices of cognitive performance in elderly workers as they supervise the performance of autonomous systems. Such knowledge is needed to design human-machine interfaces that will enable real-time collaboration between an automated system and a human operator of variable cognitive needs. This grant will also support planning activities in fostering collaborations among experts in engineering, psychology, neurosciences, learning sciences, sociology and economics to address the multi-faceted challenge in labor training and re-training in this major work sector. As an aging workforce continues to grow nationally in a dynamically changing socio-economic environment, this project will both promote the progress of science, and advance national prosperity and welfare. Broader impacts of the project also include the engagement and support of under-represented STEM researchers at both the graduate and undergraduate levels.
This planning project aims to establish an integrated framework for designing human-machine interactions customizable for supporting aging workers in an increasingly automated work domain. The project will build an interdisciplinary team to investigate methods for improving human-machine interactions for the aging workforce, including controlling and monitoring interfaces, training embedded in work technologies, and dynamic work allocation and scheduling. Three tasks are planned. A preliminary study will quantitatively characterize individual differences in physiological and cognitive responses during multitasking scenarios in a driving simulator. This will enable the design of adaptable human-machine interfaces that optimize real-time collaboration between an human operator and an automated system based on physiological profiles (to augment an elderly worker?s cognitive and sensorimotor abilities) and work performance (to optimize the allocation of resources and work activities). The project team will also investigate neural and behavioral indices of cognitive performance in elderly workers to predict an individual?s ability to learn and adapt to new technologies. This will aid the development of training that can be embedded within the work system to speed learning and adoption of new and challenging interactions with automated systems. Finally, the project investigates socioeconomic and other individual factors that impact aging workers? trust and acceptance of future technologies such as autonomous trucks and drones, which promises to inform technology interventions facilitating job transitions and retraining. Additional planning activities through local, national and international workshops and meetings will contribute to the effective integration of knowledge and methods from multiple disciplines in addressing the vision and research objectives of this project.
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.
