Understanding Mental States in Active and Autonomous Driving with EEG

TL;DR

This study compares EEG-based mental state indicators between active and autonomous driving, revealing significant differences in neural activation patterns and emphasizing the importance of scenario-specific models for driver monitoring.

Contribution

It provides the first direct EEG comparison of mental states in active versus autonomous driving, highlighting neural and behavioral differences and challenges in model generalization.

Findings

Autonomous driving shows lower cortical activation but similar mental state trends.

Models trained on one mode poorly generalize to the other.

Distinct neural patterns are linked to motor engagement and attention.

Abstract

Understanding how driver mental states differ between active and autonomous driving is critical for designing safe human-vehicle interfaces. This paper presents the first EEG-based comparison of cognitive load, fatigue, valence, and arousal across the two driving modes. Using data from 31 participants performing identical tasks in both scenarios of three different complexity levels, we analyze temporal patterns, task-complexity effects, and channel-wise activation differences. Our findings show that although both modes evoke similar trends across complexity levels, the intensity of mental states and the underlying neural activation differ substantially, indicating a clear distribution shift between active and autonomous driving. Transfer-learning experiments confirm that models trained on active driving data generalize poorly to autonomous driving and vice versa. We attribute this distribution shift primarily to differences in motor engagement and attentional demands between the two driving modes, which lead to distinct spatial and temporal EEG activation patterns. Although autonomous driving results in lower overall cortical activation, participants continue to exhibit measurable fluctuations in cognitive load, fatigue, valence, and arousal associated with readiness to intervene, task-evoked emotional responses, and monotony-related passive fatigue. These results emphasize the need for scenario-specific data and models when developing next-generation driver monitoring systems for autonomous vehicles.