AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults
arXiv:2603.00691v1 Announce Type: new 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 c
arXiv:2603.00691v1 Announce Type: new 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.
Executive Summary
The proposed AIoT framework, AURA, aims to enhance driving safety among older adults through continuous, contextualized, and explainable assessment. By leveraging in-vehicle sensing, behavioral modeling, and context-aware analysis, AURA provides a comprehensive understanding of driving performance, separating age-related changes from situational factors. This approach has the potential to promote safer aging behind the wheel, supporting independence and mobility for older adults.
Key Points
- ▸ AURA integrates richer in-vehicle sensing and multi-scale behavioral modeling
- ▸ Context-aware analysis extracts detailed indicators of driving performance
- ▸ Privacy-preserving edge architecture enables proactive, individualized support
Merits
Comprehensive Assessment
AURA's continuous and contextualized assessment provides a more accurate understanding of driving performance, addressing the limitations of traditional methods
Demerits
Data Privacy Concerns
The collection and analysis of sensitive driving data may raise concerns regarding data privacy and security, requiring careful consideration and mitigation strategies
Expert Commentary
The AURA framework represents a significant step forward in addressing the complex challenges of driving safety among older adults. By harnessing the power of AIoT and edge computing, AURA has the potential to provide a more nuanced understanding of driving performance, enabling targeted interventions and support. However, careful consideration must be given to data privacy and security concerns, as well as the potential for bias in the assessment framework. Ultimately, AURA's success will depend on its ability to balance individualized support with broader societal needs, promoting a safe and inclusive transportation ecosystem for all.
Recommendations
- ✓ Conduct thorough testing and validation of the AURA framework to ensure its accuracy and reliability
- ✓ Develop and implement robust data privacy and security protocols to mitigate potential risks and concerns