MPA-MCDNet: Momentum ProtoNet for Cognitive Load Detection in Autonomous Vehicle Takeover via VR-Simulated Scenarios

Abstract

Takeover requests (TORs) in autonomous vehicles (AVs) can impose cognitive load on drivers and affect the quality of control transfer. Detecting this cognitive load is difficult in real-world settings because synchronized multimodal data are costly and risky to collect. We developed a virtual reality (VR) driving platform for controlled TOR experiments and multimodal data collection. Based on the collected driving behavior, eye-tracking, and physiological signals, we propose momentum prototype network with attentive embedding aggregation for multimodal cognitive load detection network (MPA-MCDNet), a cognitive-load detection model that combines multimodal fusion with prototype-based episodic learning. The fusion module integrates heterogeneous time-series features, and the prototype-based module is used for the limited-trial setting in this study. Experiments on the collected dataset show that the platform supports TOR-related cognitive-load studies and that MPA-MCDNet performs well on five-class classification in our evaluation.