We introduce scenario-based cognitive status identification in older drivers from naturalistic driving videos, leveraging large vision models. In recent times, cognitive decline including Dementia 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 sensors, this study aims to extract "digital fingerprints" that correlate with functional decline and clinical features of dementia. Moreover, modern large vision models can draw meaningful insights from everyday driving patterns across different roadway scenarios to early detect cognitive decline. We propose a framework that uses large vision models and naturalistic driving videos to analyze driver behavior, identify 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.

翻译：我们提出利用大型视觉模型从自然化驾驶视频中识别老年驾驶员的场景化认知状态。当前，由于现有诊断方法耗时且成本高昂，包括痴呆症和轻度认知障碍在内的认知衰退常被漏诊。本研究通过分析车载传感器采集的真实驾驶行为，旨在提取与功能衰退及痴呆症临床特征相关的"数字指纹"。此外，现代大型视觉模型能够从不同道路场景的日常驾驶模式中提取有效信息，实现认知衰退的早期检测。我们提出了一个结合大型视觉模型与自然化驾驶视频的分析框架，用于解析驾驶行为、识别认知状态并预测疾病进展。本研究基于真实驾驶行为与驾驶员当前认知状态间的强关联性，将车辆转化为"诊断工具"。该方法能识别功能损伤的早期预警信号，有助于制定主动干预策略。本工作提升了认知衰退的早期检测能力，支持开发可扩展的非侵入式监测系统，以缓解老龄化人口认知衰退带来的日益增长的社会经济负担。