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

Abstract

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 multi-agent 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 human-centered behavioral thresholds for AVs in mixed traffic and provides a foundation for improving reliability and promoting safer AV–MV interactions at urban intersections.