In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety.

Air route alternation caused by unexpected events in abnormal or emergency situations often produces adverse consequences on an operator’s cognition and behavior in flight tasks. Under such a circumstance, it is especially necessary to examine the utility of the interaction displays usually designed based on the routine environment. This study was aimed to investigate the effects of air route alternation and display design on operators’ situation awareness (SA), task performance and mental workload during simulated flight tasks. Twenty-four participants attended an experiment where they were instructed to perform simulated flight tasks with three types of display designs in both air-route-as-planned and air-route-altered conditions. Subjective measures, behavioral measures and eye movement measures were adopted to assess the participants’ SA, task performance and mental workload. The results show that unexpected air route alternation increases mental workload as well as deteriorates the SA and task performance due to the gap between attention resource demand and supply. Reducing the demand of the operator’s attention resource should be the focus when coping with unexpected events in abnormal situations. In addition, reasonable information layout, such as a center-layout design of the critical decision-making information, is more important than information salience for improving the SA and task performance in abnormal situations. Nevertheless, indicators with a high-salience design, such as a more open window design and immersive design, are still worth recommending.

Situation awareness (SA) discrimination is significant, allowing for the pilot to maintain task performance and ensure flight safety, especially during high-stress flight tasks. Although previous research has attempted to identify and classify SA, existing SA discrimination models are predominantly binary and rely on traditional machine learning methods with limited physiological modalities. The current study aimed to construct a triple-class SA discrimination model for pilots facing high-stress tasks. To achieve this, a flight simulation experiment under typical high-stress tasks was carried out and deep learning algorithms (multilayer perceptron (MLP) and the attention mechanism) were utilized. Specifically, eye-tracking (ET), heart rate variability (HRV), and electroencephalograph (EEG) modalities were chosen as the model’s input features. Comparing the unimodal models, the results indicate that EEG modality surpasses ET and HRV modalities, and the attention mechanism structure has advantageous implications for processing the EEG modalities. The most superior model fused the three modalities at the decision level, with two MLP backbones and an attention mechanism backbone, achieving an accuracy of 83.41% and proving that the model performance would benefit from multimodal fusion. Thus, the current research established a triple-class SA discrimination model for pilots, laying the foundation for the real-time evaluation of SA under high-stress aerial operating conditions and providing a reference for intelligent cockpit design and dynamic human–machine function allocation.

Effective integration of human factors and systems engineering has become a technical challenge that constrains the full realization of human performance in unmanned aircraft systems (UAS) for urban air transportation. To address this challenge, breakthroughs are needed in key technologies related to human-system integration (HSI) of UAS. Based on literature review and industry practices, unique HF challenges of UAS are identified, and two research issues, HSI analysis throughout UAS development lifecycle and HSI practice under UAS typical lifecycle stages, are summarized. To address these issues, a model-based human-system integration (MBHSI) design framework is proposed for the UAS development lifecycle, along with an HSI practice framework for UAS under typical human readiness levels. The HSI design and practice framework can provide references for HF design of UAS in urban air transportation.