Megan C Shyr | Search NASA

NASA NTRSDate not supplied

Eye-Tracking Analysis from a Flight-Director-Use and Pilot-Monitoring Study

Eye tracking may be a useful tool to investigate pilot monitoring and develop and conduct training. There is increased interest from airlines to use eye-tracking technologies in flight simulators. However, much is still unknown about how to best utilize eye-tracking data in pilot training. This paper presents eye-tracking results from a pilot-monitoring training study with 19 pilots. All pilots completed 15 monitoring challenges across four operational scenarios in a B737-700 full flight simulator. In addition, the study investigated the impact of having the flight director engaged or disengaged on the pilot monitoring side in the final approach. It was hypothesized that pilots would focus less on the primary flight display with the flight director off and look more around in the cockpit. To assess this, pilots performed half of the scenarios with the flight director on and half with the flight director off. However, pilots monitoring tended to look less at the primary flight display with the flight director on as indicated by lower Proportion Dwell Times, contrary to the hypothesis. Next, eye-tracking data were analyzed from two monitoring challenges involving waypoint restrictions and two involving extending the flaps at appropriate airspeeds. Pilots that successfully completed the challenges appeared to focus more on areas of interest that contained the most relevant information to successfully complete the challenge. In addition, successful pilots seemed to adapt their monitoring strategy more to the challenge at hand as observed by a distinct shift in focus on either the primary flight display or the navigation display depending on the challenge. Our findings suggest the importance of flexible gaze allocation across specific situations and raise the question whether and to what degree prespecified patterns of eye fixation can be identified and trained.

eye tracking↗

NASA NTRSDate not supplied

Eye-Tracking Analysis from a Flight-Director-Use and Pilot-Monitoring Study

Eye tracking may be a useful tool to investigate pilot monitoring and develop and conduct training. There is increased interest from airlines to use eye-tracking technologies in flight simulators. However, much is still unknown about how to best utilize eye-tracking data in pilot training. This paper presents eye-tracking results from a pilot-monitoring training study with 19 pilots. All pilots completed 15 monitoring challenges across four operational scenarios in a B737-700 full flight simulator. In addition, the study investigated the impact of having the flight director engaged or disengaged on the pilot monitoring side in the final approach. It was hypothesized that pilots would focus less on the primary flight display with the flight director off and look more around in the cockpit. To assess this, pilots performed half of the scenarios with the flight director on and half with the flight director off. However, pilots monitoring tended to look less at the primary flight display with the flight director on as indicated by lower Proportion Dwell Times, contrary to the hypothesis. Next, eye-tracking data were analyzed from two monitoring challenges involving waypoint restrictions and two involving extending the flaps at appropriate airspeeds. Pilots that successfully completed the challenges appeared to focus more on areas of interest that contained the most relevant information to successfully complete the challenge. In addition, successful pilots seemed to adapt their monitoring strategy more to the challenge at hand as observed by a distinct shift in focus on either the primary flight display or the navigation display depending on the challenge. Our findings suggest the importance of flexible gaze allocation across specific situations and raise the question whether and to what degree prespecified patterns of eye fixation can be identified and trained.

eye tracking↗

NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗

NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗

NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗