The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$1 (16–20Hz), $\beta$2 (20–24Hz) and $\beta$3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$1 ∼ $\beta$3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$1 ∼ $\beta$3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.

In fighter pilot training, much of upgrade pilots’ (UPs’) learning takes place during mission debriefs. A debrief provides instructor pilots (IPs) the opportunity to correct situation awareness (SA) upon which the UPs base their tactical decisions. Unless the debrief is conducted with proper depth and breadth, the IPs’ feedback on UPs’ SA and tactical decision-making may be incomplete or false, resulting in poor, or even negative learning. In this study, a new debrief protocol based on the Critical Decision Method (CDM) is introduced. The protocol specifically addresses the SA of UPs. An evaluation was conducted to examine if a short CDM training programme to IPs would enhance their ability to provide performance feedback to UPs regarding their SA and tactical decision-making. The IPs were qualified flying instructors and the UPs were air force cadets completing their air combat training with BAe Hawk jet trainer aircraft. The impact of the training intervention was evaluated using Kirkpatrick’s four-level model. The first three levels of evaluation (Reactions, Learning and Behaviour) focused on the IPs, whereas the fourth level (Results) focused on the UPs. The training intervention had a positive impact on the Reactions, Learning and debrief Behaviour of the IPs. In air combat training missions, the UPs whose debriefs were based on the CDM protocol, had superior SA and overall performance compared to a control group.

The effects of air traffic conflict geometry have been well investigated in prior studies, particularly in the context of the pilot, though little in the context of the air traffic control officer (ATCO). No study to date has investigated the effects of conflict geometry on human factors variables of ATCOs through objective and physiological approaches. This study examines the effects of conflict geometries on ATCO situation awareness, stress level and brain activity during conflict resolution. Fifteen participants were instructed to resolve six different conflict geometries: crossing level, crossing non-level, converging level, converging non-level, overtaking level, and overtaking non-level. The results indicate that converging and crossing conflicts led to lower situation awareness (SA), higher stress level, and higher theta activation at the temporal and parietal lobes. Level conflict led to lower SA. The findings offer two implications, providing insights for the formal guidelines in ATC conflict resolution training and provision of inputs for the conflict resolution aid development.

A novel statistical model is presented to quantify situation awareness in the operation of small civilian Unmanned Aircraft Systems (UAS). Today, the vast majority of small Unmanned Aircraft Systems (UAS) operation takes place under Visual Line of Sight (VLOS) of a human operator, who is wholly responsible for the safety of the flight. As operation begins to move to Beyond Visual Line of Sight (BVLOS), it is likely that this responsibility will become shared between operator and the increasingly autonomous UAS itself. Before we seek to quantify the safety of such a system, it is beneficial to analyse the safety of existing Visual Line of Sight (VLOS) operations to provide a target level of safety. Prior to considering any on-board decision making, it is essential to ensure that the artificial situation awareness system of a UAS in Beyond Visual Line of Sight (BVLOS) is at least as good as awareness of a human operator. The paper provides a probabilistic theory and model for the high-level abstractions of situation awareness to guide future assessment of BVLOS operations.

With the widespread use of mobile devices and increased demand for mobile services, Location-Based Services (LBS) represent a promising addition to service offerings of network operators as well as third-party service providers. Based on long-term research in LBS, our group has proposed a generic Enhanced LBS Reference Model (ELRM), which describes the concept, the architecture and the functionalities of the LBS. In addition, an evolutionary information process has been identified within the LBS, that represents knowledge maturity from position awareness to situation awareness. Both the ELRM and the information evolution process in LBS are presented in this article and illustrated by a case study within the framework of the 3GPP-standardised IP Multimedia Subsystem (IMS). This case-study emphasises the opportunities for navigation- and LBS-related solutions development provided by modern telecommunication technologies.