Insights from psychophysiological workload analysis of human-driven vehicle drivers in interactions with autonomous vehicles

초록

This study develops a methodology to evaluate autonomous vehicle (AV) behavior in mixed traffic by incorporating the psychophysiological workload of manually driven vehicle (MV) drivers during vehicle-to-vehicle interactions. The framework is applied to unprotected left turns at unsignalized intersections, analyzing interactions between left-turning AVs and oncoming MVs-situations central to urban safety and mobility. A multiagent driving simulation (MADS) platform synchronized time and space across two interconnected simulators, enabling real-time analysis of AV-MV trajectories. Electroencephalogram (EEG) signals from MV drivers were used to derive an Anxiety and Nervousness Index (ANI), based on the beta-to-alpha power ratio, to quantify stress and discomfort. Statistical modeling revealed a robust inverse relationship between ANI and post-encroachment time (PET), which represents the temporal separation at the projected conflict point and serves as a surrogate measure of crash potential: driver anxiety declined as PET increased. The rate of decline diminished beyond a PET of 2.7 s, defined as the marginal improvement point (MIP). Guided by this threshold, we propose AV decision protocols: accelerate when PET > 2.7 s to improve flow, and decelerate or yield when PET <= 2.7 s to protect human comfort. These findings underscore that AV behavior should integrate human cognitive and psychological responses alongside technical performance. The proposed methodology establishes humancentered behavioral thresholds for AVs in mixed traffic and provides a foundation for improving reliability and promoting safer AV-MV interactions at urban intersections.

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