Flight simulation study of airplane state awareness and prediction technologies

Abstract

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 industry-recommended 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.