[论文解读] AIoT-based Continuous, Contextualized, and Explainable Driving Assessment for Older Adults

Yimeng Liu, Fangwei Zhang+10 more|arXiv (Cornell University)|Feb 28, 2026

Older Adults Driving Studies被引用 0

一句话总结

本文提出 AURA，一种利用多模态车内感知与边缘计算来区分衰老相关变化与情境因素的持续、情境感知、可解释的老年驾驶评估的AIoT框架。

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.

研究动机与目标

• 由于认知与感官能力下降以及老年驾驶员比例上升，推动 safer aging in driving 的研究动机。
• 开发能够在自然环境中捕捉细粒度行为的持续、真实世界驾驶评估框架。
• 实现情境感知的解读，以将年龄相关变化与环境因素分离。
• 提供对驾驶者、家庭与临床医生可操作的可解释AI输出。

提出的方法

• 引入 AURA，一种用于持续、隐私保护的车内感知与边缘处理的AIoT框架。
• 使用多模态传感捕捉车辆控制、车内行为与环境情境。
• 采用情境感知的数据融合与纵向建模，生成行为轨迹与安全指标。
• 通过因果、机制性解释以及车内部署实现可解释AI，保护隐私。
• 利用 CARLA 仿真器、LongROAD、DRIVES 等数据集研究老年驾驶模式，并开发可解释的无监督分析管线。

Figure 1 . Overview of AURA

实验结果

研究问题

• RQ1 如何利用持续、真实世界的驾驶数据来评估老年驾驶员的认知与功能性变化？
• RQ2 哪些情境因素（交通、天气、前方车辆）会调制年龄相关的驾驶行为？
• RQ3 如何将驾驶信号转化为可解释、临床有用的评估与干预？
• RQ4 哪些建模方法能产出稳定、可解释的洞见，而非不透明的预测？

主要发现

• 老年人在仿真与真实世界数据中都表现出制动/油门变化速度较慢、车速较低、头部扫描更多的特征。
• 老年驾驶高度依赖情境，前方车辆存在与天气条件显著改变行为模式。
• LongROAD 与 DRIVES 数据揭示衰老相关的出行能力下降与强烈的自我调控，包括夜间出行规避与路线偏好。
• PCA 与聚类显示个体内部具有较强的稳定性与多样化的个人画像，但跨个体在认知状态上的分离有限。
• 可解释AI 挑战被承认；当前的无监督方法能识别签名但无法揭示潜在原因，凸显需要因果建模。

Figure 2 . Overview of the CARLA simulator environment employed for controlled driving data collection.