Dynamics of Affective States During Takeover Requests in Conditionally Automated Driving Among Older Adults with and without Cognitive Impairment

2025-05-23

AI Summary

This study investigates affective dynamics during conditional automated driving take-over requests (TORs) in individuals with mild cognitive impairment (MCI) versus healthy older adults. Using a multi-scenario driving simulator integrated with computer vision–based facial expression analysis, we quantified changes in valence and arousal and performed within-group and between-group comparisons using Wilcoxon signed-rank and Mann–Whitney U tests. We report the first empirical evidence of “affective blunting” in the MCI group: significantly reduced arousal, positively shifted valence, and impaired adaptive modulation of affect in response to varying road workload—contrasting with healthy controls, who exhibited markedly increased arousal under high workload. These findings demonstrate that cognitive impairment attenuates both emotional responsiveness and situation awareness during TORs, providing critical empirical support and a novel design paradigm for adaptive, affect-aware take-over assistance systems tailored to vulnerable road users.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Driving is a key component of independence and quality of life for older adults. However, cognitive decline associated with conditions such as mild cognitive impairment and dementia can compromise driving safety and often lead to premature driving cessation. Conditionally automated vehicles, which require drivers to take over control when automation reaches its operational limits, offer a potential assistive solution. However, their effectiveness depends on the driver's ability to respond to takeover requests (TORs) in a timely and appropriate manner. Understanding emotional responses during TORs can provide insight into drivers' engagement, stress levels, and readiness to resume control, particularly in cognitively vulnerable populations. This study investigated affective responses, measured via facial expression analysis of valence and arousal, during TORs among cognitively healthy older adults and those with cognitive impairment. Facial affect data were analyzed across different road geometries and speeds to evaluate within- and between-group differences in affective states. Within-group comparisons using the Wilcoxon signed-rank test revealed significant changes in valence and arousal during TORs for both groups. Cognitively healthy individuals showed adaptive increases in arousal under higher-demand conditions, while those with cognitive impairment exhibited reduced arousal and more positive valence in several scenarios. Between-group comparisons using the Mann-Whitney U test indicated that cognitively impaired individuals displayed lower arousal and higher valence than controls across different TOR conditions. These findings suggest reduced emotional response and awareness in cognitively impaired drivers, highlighting the need for adaptive vehicle systems that detect affective states and support safe handovers for vulnerable users.

Problem

• Investigates emotional responses during automated driving takeovers in older adults
• Compares affective states between cognitively healthy and impaired drivers
• Identifies need for adaptive vehicle systems based on emotional awareness

Innovation

• Facial expression analysis measures valence and arousal
• Wilcoxon test compares within-group affective changes
• Mann-Whitney U test evaluates between-group differences