The goal of carrying out this work was to identify potential application areas for augmented reality (AR) in pilot education by addressing gender preferences. Like the field of engineering, the aviation industry is dominated by men. Because the aviation industry forecasts a high demand for pilots, it is highly desirable to address gender diversity and improve teaching methods in pilot education. In this study, potential application areas for AR-supported pilot training were investigated by conducting a survey with 60 pilots and flight instructors (including 12 women). Typical AR use cases were presented in videos, and the pilots reported their preferences regarding similar or other AR applications used in different parts of the flight training program. AR navigation was the use case that was most frequently preferred by both female and male pilots. The majority of pilots agreed that AR could potentially be used in theoretical instruction, pre-flight aircraft inspection, and procedure training. In addition, both gender groups showed similar preferences for various gaming concepts that make learning more interesting and engaging, such as receiving positive feedback. However, a higher percentage of women than men reported that achieving a target or receiving points to successfully finish a task and answering questions during the game were satisfying. Including a story in the game to attract attention was preferred by a higher percentage of men than women. The results of this study can be used to design AR educational concepts that support gender diversity in pilot education and other technical domains.

With the help of immersive virtual reality technology, novel cockpit systems can be evaluated with pilots in an early design phase. This comparative study investigates the functional fidelity of a virtual reality flight simulator (VRFS) in comparison with a conventional flight simulator. Pilots’ movement time to reach cockpit controls, deviation from the ideal flight path, workload, and simulator sickness are evaluated using an operational scenario. The results show statistically significant differences in heading, altitude, and flight path, as well as delays in operating the controls in virtual reality. Yet, most participants could safely and reliably complete the flight task. For use cases in which adaptations to pace, exposure time, and flight task are acceptable, which is often the case in early phases of the design process, VRFSs can be viable tools for human factors engineering.

Objective: This study investigated drivers' evaluation of a conventional autonomous emergency braking (AEB) system on high and reduced tire–road friction and compared these results to those of an AEB system adaptive to the reduced tire–road friction by earlier braking. Current automated systems such as the AEB do not adapt the vehicle control strategy to the road friction; for example, on snowy roads. Because winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions, the potential of conventional AEB to prevent collisions could be significantly improved by including friction in the control algorithm. Whereas adaption is not legally required for a conventional AEB system, higher automated functions will have to adapt to the current tire–road friction because human drivers will not be required to monitor the driving environment at all times. For automated driving functions to be used, high levels of perceived safety and trust of occupants have to be reached with new systems. The application case of an AEB is used to investigate drivers' evaluation depending on the road condition in order to gain knowledge for the design of future driving functions. Methods: In a driving simulator, the conventional, nonadaptive AEB was evaluated on dry roads with high friction (μ = 1) and on snowy roads with reduced friction (μ = 0.3). In addition, an AEB system adapted to road friction was designed for this study and compared with the conventional AEB on snowy roads with reduced friction. Ninety-six drivers (48 males, 48 females) assigned to 5 age groups (20–29, 30–39, 40–49, 50–59, and 60–75 years) drove with AEB in the simulator. The drivers observed and evaluated the AEB's braking actions in response to an imminent rear-end collision at an intersection. Results: The results show that drivers' safety and trust in the conventional AEB were significantly lower on snowy roads, and the nonadaptive autonomous braking strategy was considered less appropriate on snowy roads compared to dry roads. As expected, the adaptive AEB braking strategy was considered more appropriate for snowy roads than the nonadaptive strategy. In conditions of reduced friction, drivers' subjective safety and trust were significantly improved when driving with the adaptive AEB compared to the conventional AEB. Women felt less safe than men when AEB was braking. Differences between age groups were not of statistical significance. Conclusions: Drivers notice the adaptation of the autonomous braking strategy on snowy roads with reduced friction. On snowy roads, they feel safer and trust the adaptive system more than the nonadaptive automation.

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features.

This study explored novel mixed reality (MR) use cases for pilot training using a mix of methods rooted in the general innovation theory of dynamic capabilities. The aim was to identify areas of improvement for various aspects of the flight training based on MR, in a socially and economically sustainable manner. Multiple surveys and workshops have been conducted with flight instructors, administrative staff, pilots and student pilots. The main result of this study is a systematic identification of the three most promising MR use cases: interactive theory training, cockpit procedure, and outside check training. These results are important because they inform the development of technical didactic tools for pilot training. The applicability of MR technologies to accommodate diverse user needs and preferences is addressed, while also considering aspects of economical sustainability.

This study evaluated an anticipation-based training program for private pilots to recover from unusual flight attitudes, stalls, and spins using visual reference cues. Flight performance, psychophysiological measures of cardiac and electrodermal activity, and subjective measures of workload and emotion were collected from 33 pilots during simulated and real flight. The results demonstrate positive training effects of anticipation-based training in real and simulated flight to improve performance and to change patterns of psychophysiological arousal, workload, and emotion in pilots.