Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers From Driving Video

• URL: http://arxiv.org/abs/2507.05463v1
• Date: Mon, 07 Jul 2025 20:30:00 GMT
• Title: Driving as a Diagnostic Tool: Scenario-based Cognitive Assessment in Older Drivers From Driving Video
• Authors: Md Zahid Hasan, Guillermo Basulto-Elias, Jun Ha Chang, Sahuna Hallmark, Matthew Rizzo, Anuj Sharma, Soumik Sarkar,
• Abstract summary: This research aims to extract "digital fingerprints" that correlate with functional decline and clinical features of Alzheimer's disease (AD) and mild cognitive impairment (MCI)<n>We propose a framework that uses large vision models and naturalistic driving videos to analyze driver behavior, classify cognitive status and predict disease progression.
• Score: 6.66498412613475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce scenario-based cognitive status identification in older drivers from Naturalistic driving videos and large vision models. In recent times, cognitive decline, including Alzheimer's disease (AD) and mild cognitive impairment (MCI), is often underdiagnosed due to the time-consuming and costly nature of current diagnostic methods. By analyzing real-world driving behavior captured through in-vehicle systems, this research aims to extract "digital fingerprints" that correlate with functional decline and clinical features of MCI and AD. Moreover, modern large vision models can draw meaningful insights from everyday driving patterns of older patients to early detect cognitive decline. We propose a framework that uses large vision models and naturalistic driving videos to analyze driver behavior, classify cognitive status and predict disease progression. We leverage the strong relationship between real-world driving behavior as an observation of the current cognitive status of the drivers where the vehicle can be utilized as a "diagnostic tool". Our method identifies early warning signs of functional impairment, contributing to proactive intervention strategies. This work enhances early detection and supports the development of scalable, non-invasive monitoring systems to mitigate the growing societal and economic burden of cognitive decline in the aging population.

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