17. Identifying the Factors Influencing Older Adults’ Perceptions of Fully Automated Vechicles

Shabnam Haghzare ^1,2, Jennifer Campos ^2,3, Alex Mihailidisa ^1,2,4

¹ Institute of Biomaterials and Biomedical Engineering, University of Toronto
² Toronto Rehabilitation Institute, University Health Network
³ Department of Psychology, University of Toronto
⁴ Department of Occupational Science and Occupational Therapy, University of Toronto

Abstract
As the capabilities of Artificial Intelligence (AI) advances and AI systems take on more important societal functions, their integration into society becomes increasingly important. This fact is especially true for AI based consumer prod­ucts in which consumers’ satisfaction with the product plays a crucial role in how the system is perceived and consequently relied on. Examples of such advanced AI systems are Fully Automated Vehicles (FAVs), which are not only a con­sumer product but also high-risk ones. Thus, both the consumers’ satisfaction and their safety should be jointly addressed in the design process. While ac­counting for consumers’ feedback in the design process, the design should strive to accommodate all people. However, in the context of FAVs, the demographic of older adults are often overlooked. This is while FAVs have the potential to significantly improve older adults’ general health by increasing their mobility. Nonetheless, limited research has focused on older adults’ perceptions of such technologies. The current driving simulation-based study will investigate factors that may govern older adults perceptions of FAVs with respect to trust, accept­ability, and safety. Participants ( 65+) will experience scenarios of manual and fully automated driving in a high-fidelity driving simulator. Their perceptions of the FAV will be measured before and after the driving experiences using ques­tionnaires. Physiological and behavioral data will also be collected throughout the driving sessions to investigate whether negative or positive perceptions of technology are associated with behavioral or physiological measures. In addi­tion, participants’ driving performance and driving styles will be captured dur­ing manual driving to investigate whether an alignment between an individuals driving style and the FAV’s driving style will lead to a more positive perception towards the technology. The identified features and behavioral/ physiological responses can then help to inform designs and guidelines surrounding the ac­ceptability of FAVs for older adults