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

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

TL;DR

AURA, an Artificial Intelligence of Things (AIoT) framework for continuous, real-world assessment of driving safety among older adults, provides a foundation for proactive, individualized support that helps older adults drive safely.

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.

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

TL;DR

AURA, an Artificial Intelligence of Things (AIoT) framework for continuous, real-world assessment of driving safety among older adults, provides a foundation for proactive, individualized support that helps older adults drive safely.

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.
Paper Structure (19 sections, 1 equation, 11 figures, 2 tables)

This paper contains 19 sections, 1 equation, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Cognitive Decline as the Core Issue
  4. Driving Assessment
  5. Observations and Insights
  6. Datasets and Experimental Scope
  7. Senior Driving Pattern
  8. Context and Environmental Awareness
  9. Explainable AI
  10. The AURA Framework
  11. Multi-Scale Representation Learning
  12. Context-Integrated Modeling
  13. Transparent and Actionable Intelligence
  14. Broader Impact
  15. Go Beyond AURA
  16. ...and 4 more sections

Figures (11)

  • Figure 1: Overview of AURA
  • Figure 2: Overview of the CARLA simulator environment employed for controlled driving data collection.
  • Figure 3: Driving behavior is different between young and senior drivers.
  • Figure 4: Head movement with age difference.
  • Figure 5: Route-level safety score differences between senior and young drivers. Red points mark locations with maximal group divergence, while black points indicate baseline similarity. Senior drivers show widespread instability and reduced fluidity, with delayed reactions at intersections and larger route deviations in curves.
  • ...and 6 more figures