Classification of flight phases based on pilots’ visual scanning strategies

Eye movements analysis has great potential for understanding operator behaviour in many safety-critical domains, including aviation. In addition to traditional eye-tracking measures on pilots’ visual behavior, it seems promising to incorporate machine learning approaches to classify pilots’ visual scanning patterns. However, given the multitude of pattern measures, it is unclear which are better suited as predictors. In this study we analyzed the visual behaviour of eight pilots, flying different flight phases in a moving-base flight simulator. With this limited dataset we present a methodological approach to train linear Support Vector Machine models, using different combinations of the attention ratio and scanning pattern features. The results show that the overall accuracy to classify the pilots’ visual behaviour in different flight phases, improves from 51.6% up to 64.1% when combining the attention ratio and instrument scanning sequence in the classification model

SUPRA - Enhanced Upset Recovery Simulation

The SUPRA research project – Simulation of Upset Recovery in Aviation – has been funded by the European Union the Framework Program to enhance the flight simulation envelope for upset recovery simulation. Within the project an extended aerodynamic model, capturing the key aerodynamics during and beyond stall for a large category transport aircraft and new motion cueing solutions for both hexapod and centrifuge-based platforms were developed. This paper describes the recent piloted evaluation experiments. In the first experiment a group of ten experimental test pilots, with actual experience in stall conditions, subjectively judged the validity of the aerodynamic model and the motion cueing solutions in the simulators in different upset conditions. Pilots rated the stall behaviour of the SUPRA

Measuring the Lookout Behavior of Student Pilots in a Virtual Reality Flight Simulator

Learning adequate gaze behavior is essential in flight training. In this exploratory study we investigated the development of gaze behavior in flight training in a virtual reality (VR) flight simulator. Following standardized study material, fifteen participants without flying experience repeatedly practiced three basic flight maneuvers in a VR simulator of a small aircraft. During some runs, participants performed an additional N-back task to measure cognitive spare capacity. From the recorded gaze data we computed the percentage of time during which the gaze was directed outside the cockpit, i.e., the “Lookout”. This outside dwell ratio differed between flight maneuvers. A higher outside dwell ratio was associated with better flight performance. Remarkably, the outside dwell ratio increased with the additional N-back task. A heatmap indicated staring behavior during the N-back. In a follow-up study we will extend the analysis of gaze behavior with more dynamic measures than only the dwell ratio