Airline Pilots Eye Movements During Take Off And Landing In Visual Meteorological Conditions PDF Download

During a flight, pilots must rigorously monitor specific flight instruments (e.g., attitude indicator, airspeed, altimeter, engine parameters) as well as the external environment (e.g., locate terrain features on the ground, especially in clear weather conditions by low altitude) to update their situational awareness. This monitoring activity, which is critical during dynamic flight phases (e.g., takeoff, approach phase, and landing), consist in observing and interpreting the flight path, the selected automation modes, and the systems used onboard. This involves a real-time comparison between the data displayed on the instruments and the values expected during the flight phases. Appropriate monitoring of the cockpit enables to take corrective measures (e.g., adjust the aircraft's trajectory when a deviation is detected in the attitude zone) promptly when a parameter is deviated, thus guaranteeing an optimal level of safety. This monitoring activity is structured in a sequence of engagement and redirection of the operator's visual attention from one instrument to another. Moreover, accident reports have shown that piloting errors, such as incorrect trajectories or overspeed during landing, are often the result of inadequate monitoring of cockpit instruments. The purpose of this research work is to improve the flight safety thanks in particular to the integration of an eye-tracker. Eye movements are a window on the pilot's cognitive state and reveal the attentional paths taken by the operator through his visual path. In connection with cockpit monitoring issues, we have developed a Flight Eye Tracking Assistant (FETA) based on expert visual behaviors (e.g., 24 pilots with more than 1600 flight hours). This assistant warns the pilots, thanks to an audible alarm, when they no longer sufficiently consult a flight instrument in comparison with the expert eye movement database. A human factors evaluation of this assistant raised several issues with such an assistant and paved the way for further research including metrics that best reflect the eye paths in the cockpit and the need to find the right metric to quantify a pilot's visual attention onboard. Part of this research work is based on a comparison between novices and experts in order to quantify the mark of expertise. A method using the K coefficient applied to the AOIs allowed to qualify the visual attention of the pilots (focal vs ambient) during a flight simulator scenario with different loads of visuomotor activity. Machine learning methods based on transition matrices allowed to classify the expertise with an accuracy of 91%. Finally, two methods were used to qualify and quantify visual strategies in the cockpit. A method using Lempel-Ziv Complexity (LZC), a data compression algorithm, to highlight the complexity of the scanning sequences in the cockpit. Another called N-gram method, originally derived from DNA sequence research, which quantifies the patterns common to the expert group and the length of the patterns used. These contributions are discussed in the light of the improvement of a flying assistant based on eye tracking data for improving learning on the one hand and avoiding monitoring problems on the other. Finally, the evaluation of the FETA prototype raised perspectives on the choice of the most relevant modality (e.g. auditory, visual, haptic) for alerting.