Airplane state awareness (ASA) is a pilot performance attribute derived from the more general attribute known as situation awareness. Airplane state alludes primarily to attitude and energy state, but also infers other state variables, such as the state of automated or autonomous systems, that can affect attitude or energy state. Recognizing that loss of ASA has been a contributing factor to recent accidents, an industry-wide team has recommended several Safety Enhancements (SEs) to resolve or mitigate the problem. Two of these SEs call for research and development of new technology that can predict energy and/or auto-flight system states, and intuitively notify or alert flight crews to future unsafe or otherwise undesired states. In addition, it is desired that future air vehicles will be able to operate with a high degree of awareness of their own well-being. This form of ASA requires onboard predictive capabilities that can inform decision-making functions of critical markers trending to unsafe states. This paper describes a high-fidelity flight simulation study designed to address the two industryrecommended SEs for current aircraft, as well as this desired self-awareness capability for future aircraft. Eleven commercial airline crews participated in the testing, completing more than 220 flights. Flight scenarios were utilized that span a broad set of conditions including several that emulated recent accidents. An extensive data set was collected that includes both qualitative data from the pilots, and quantitative data from a unique set of instrumentation devices. The latter includes a head-/eye-tracking system and a physiological measurement system. State-of-the-art flight deck systems and indicators were evaluated, as were a set of new technologies. These included an enhancement to the bank angle indicator; predictive algorithms and indications of where the auto-flight system will take the aircraft and when automation mode changes will occur or where energy-related problems may occur; and synoptic (i.e., graphical) depictions of the effects of loss of flight critical data, combined with streamlined electronic checklists. Topics covered by this paper include the research program context, test objectives, descriptions of the technologies under test, platform and operational environment setup, a summary of findings, and future work.

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A flight simulation study was conducted at NASA Langley Research Center to evaluate flight deck systems that (1) predict aircraft energy state and/or autoflight configuration, (2) present the current state and expected future state of automated systems, and/or (3) show the state of flight-critical data systems in use by automated systems and primary flight instruments. Four new technology concepts were evaluated vis-à-vis current state-of-the-art flight deck systems and indicators. This human-in-the-loop study was conducted using commercial airline crews. Scenarios spanned a range of complex conditions and several emulated causal factors and complexity in recent accidents involving loss of state awareness by pilots (e.g. energy state, automation state, and/or system state). Data were collected via questionnaires administered after each flight, audio/video recordings, physiological data, head and eye tracking data, pilot control inputs, and researcher observations. This paper focuses specifically on findings derived from the questionnaire responses. It includes analysis of pilot subjective measures of complexity, decision making, workload, situation awareness, usability, and acceptability.

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard / integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area.A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products.The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.