[2603.00691] AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults

Computer Science > Artificial Intelligence arXiv:2603.00691 (cs) [Submitted on 28 Feb 2026] Title:AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults Authors:Yimeng Liu, Fangwei Zhang, Maolin Gan, Jialuo Du, Jingkai Lin, Yawen Wang, Fei Sun, Honglei Chen, Linda Hill, Ruofeng Liu, Tianxing Li, Zhichao Cao View a PDF of the paper titled AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults, by Yimeng Liu and 11 other authors View PDF HTML (experimental) Abstract:The world is undergoing a major demographic shift as older adults become a rapidly growing share of the population, creating new challenges for driving safety. In car-dependent regions such as the United States, driving remains essential for independence, access to services, and social participation. At the same time, aging can introduce gradual changes in vision, attention, reaction time, and driving control that quietly reduce safety. Today's assessment methods rely largely on infrequent clinic visits or simple screening tools, offering only a brief snapshot and failing to reflect how an older adult actually drives on the road. Our work starts from the observation that everyday driving provides a continuous record of functional ability and captures how a driver responds to traffic, navigates complex roads, and manages routine behavior. Leveraging this insight, we propose AURA, an Artificial Intelligence of Things (AIoT) framework for contin...