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

Yimeng Liu, Fangwei Zhang, Maolin Gan, Jialuo Du, Jingkai Lin, Fei Sun, Honglei Chen, Linda Hill, Ruofeng Liu, Tianxing Li, Zhichao Cao

Abstract: captures how a driver responds to traffic, navigates complex roads, and manages routine behav- ior. Leveraging this insight, we propose AURA, an Artificial Intelligence of Things (AIoT) framework for continuous, real- world assessment of driving safety among older adults. AURA integrates richer in-vehicle sensing, multi-scale behavioral modeling, and context-aware analysis to extract detailed indicators of driving performance from routine trips. It orga- nizes fine-grained actions into longer behavioral trajectories and separates age-related performance changes from situ- ational factors such as traffic, road design, or weather. By integrating sensing, modeling, and interpretation within a privacy-preserving edge architecture, AURA provides a foun- dation for proactive, individualized support that helps older adults drive safely. This paper outlines the design princi- ples, challenges, and research opportunities needed to build reliable, real-world monitoring systems that promote safer aging behind the wheel.