Multimodal prediction of situation awareness during automated driving: a gaze and EEG-based approach
研究开发了两种基于眼动和脑电图的生理模型，分别预测自动驾驶中驾驶员的全层级和认知层级情境意识，发现结合两种模态能更准确评估高级情境意识。
Drivers’ situation awareness (SA) is often assessed using indirect behavioural indicators such as takeover performance, despite known limitations. Many studies have also focused heavily on visual attention, primarily capturing the perception level of SA. To address this, we developed two physiological SA prediction models. Model 1 links physiological responses to full-level SA (perception, comprehension, and projection), while Model 2 targets comprehension-level SA to examine mechanisms beyond perception. A simulated driving study was conducted with 39 participants, during which eye-tracking and EEG data were collected. In Model 1, both modalities distinguished the degree of full-level SA. In Model 2, eye-tracking metrics differentiated successful from unsuccessful comprehension. In both models, the eye-tracking-based model outperformed the EEG-based model. However, combined model showed better performance, particularly in predicting comprehension-level SA. These findings demonstrate the benefit of integrating cognitive state monitoring beyond perception alone, especially for assessing higher-level SA in automated driving.