AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults
🤖 AI Summary
Current assessments of older drivers rely on sporadic clinical evaluations or simplistic screenings, which inadequately capture dynamic changes in everyday driving performance. This work proposes AURA, a novel framework that integrates artificial intelligence and Internet-of-Things technologies to enable continuous, privacy-preserving, real-time safety evaluation through multimodal in-vehicle sensors. By combining multiscale behavioral modeling with context-aware analysis at the edge, AURA delivers personalized assessments that uniquely unify continuity, contextualization, and interpretability in geriatric driving evaluation. The approach effectively disentangles age-related functional decline from transient environmental influences, thereby establishing a technically actionable foundation to support safe and independent driving among older adults.
Application Category
📝 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 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 organizes fine-grained actions into longer behavioral trajectories and separates age-related performance changes from situational factors such as traffic, road design, or weather. By integrating sensing, modeling, and interpretation within a privacy-preserving edge architecture, AURA provides a foundation for proactive, individualized support that helps older adults drive safely. This paper outlines the design principles, challenges, and research opportunities needed to build reliable, real-world monitoring systems that promote safer aging behind the wheel.
Problem
Research questions and friction points this paper is trying to address.
driving assessment
older adults
continuous monitoring
contextualized evaluation
real-world driving
Innovation
Methods, ideas, or system contributions that make the work stand out.
AIoT
continuous driving assessment
context-aware analysis
behavioral modeling
edge computing
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