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

2025-07-07

AI Summary

Current diagnostic methods for cognitive decline—including Alzheimer’s disease and mild cognitive impairment—are time-consuming, costly, and suffer from high false-negative rates. To address this, we propose a non-invasive, early-screening paradigm leveraging naturalistic driving videos: in-vehicle systems capture real-world driving behavior, and, for the first time, large vision models (LVMs) are employed end-to-end to extract “driving digital fingerprints” predictive of cognitive decline. These features are integrated with clinical cognitive assessment criteria to build a unified framework for classification and disease progression prediction. Our approach enables interpretable, dynamic modeling of driving behavior as a biomarker of cognitive health, significantly improving accuracy in detecting early functional abnormalities under ecologically valid conditions. By transforming ordinary vehicles into scalable, low-cost, non-invasive mobile cognitive monitoring platforms, this work provides a practical, community-deployable technological pathway for large-scale, population-level early screening of cognitive decline.

Technology Category

• Computer Vision: Large Vision Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Application Domains: Mobility, Driving & Flight

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

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.

Problem

• Identify cognitive decline in older drivers using driving videos
• Detect early signs of Alzheimer's and MCI via driving behavior
• Develop non-invasive monitoring systems for cognitive assessment

Innovation

• Uses large vision models for cognitive assessment
• Analyzes naturalistic driving videos for early detection
• Leverages driving behavior as digital diagnostic fingerprints