Impact of Advanced Synoptics and Simplified Checklists During Aircraft Systems Failures

AbstractNatural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds demanded in todays aviation environment. Increasingly Autonomous Systems (IAS) are uniquely suited to solve this problem. NASA is conducting research and development of IAS - hardware and software systems, utilizing machine learning algorithms, seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. IAS offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. A human-in-the-loop test was conducted in NASA Langleys Integration Flight Deck B-737-800 simulator to evaluate advanced synoptic pages with simplified interactive electronic checklists as an IAS for routine air carrier flight operations and in response to aircraft system failures. Twelve U.S. airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to failures. These data are fundamental to and critical for the design and development of future increasingly autonomous systems that can better support the human in the cockpit. Synoptic pages and electronic checklists significantly improved pilot responses to non-normal scenarios, but implementation of these aids and other intelligent assistants have barriers to implementation (e.g., certification cost) that must overcome

Evaluation of Technology Concepts for Traffic Data Management and Relevant Audio for Datalink in Commercial Airline Flight Decks

Datalink is currently operational for departure clearances and in oceanic environments and is currently being tested in high altitude domestic enroute airspace. Interaction with even simple datalink clearances may create more workload for flight crews than the voice system they replace if not carefully designed. Datalink may also introduce additional complexity for flight crews with hundreds of uplink messages now defined for use. Finally, flight crews may lose airspace awareness and operationally relevant information that they normally pickup from Air Traffic Control (ATC) voice communications with other aircraft (i.e., party-line transmissions). Once again, automation may be poised to increase workload on the flight deck for incremental benefit. Datalink implementation to support future air traffic management concepts needs to be carefully considered, understanding human communication norms and especially, the change from voice- to text-based communications modality and its effect on pilot workload and situation awareness. Increasingly autonomous systems, where autonomy is designed to support human-autonomy teaming, may be suited to solve these issues. NASA is conducting research and development of increasingly autonomous systems, utilizing machine-learning algorithms seamlessly integrated with humans whereby task performance of the combined system is significantly greater than the individual components. Increasingly autonomous systems offer the potential for significantly improved levels of performance and safety that are superior to either human or automation alone. Two increasingly autonomous systems concepts - a traffic data manager and a conversational co-pilot - were developed to intelligently address the datalink issues in a complex, future state environment with significant levels of traffic. The system was tested for suitability of datalink usage for terminal airspace. The traffic data manager allowed for automated declutter of the Automatic Dependent Surveillance-Broadcast (ADS-B) display. The system determined relevant traffic for display based on machine learning algorithms trained by experienced human pilot behaviors. The conversational co-pilot provided relevant audio air traffic control messages based on context and proximity to ownship. Both systems made use of the connected aircraft concepts to provide intelligent context to determine relevancy above and beyond proximity to ownship. A human-in-the-loop test was conducted in NASA Langley Research Centers Integration Flight Deck B-737-800 simulator to evaluate the traffic data manager and the conversational co-pilot. Twelve airline crews flew various normal and non-normal procedures and their actions and performance were recorded in response to the procedural events. This paper details the flight crew performance and evaluation during the events

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nations air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two

Driving automation: Learning from aviation about design philosophies

Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation

Towards Autonomous Aviation Operations: What Can We Learn from Other Areas of Automation?

Rapid advances in automation has disrupted and transformed several industries in the past 25 years. Automation has evolved from regulation and control of simple systems like controlling the temperature in a room to the autonomous control of complex systems involving network of systems. The reason for automation varies from industry to industry depending on the complexity and benefits resulting from increased levels of automation. Automation may be needed to either reduce costs or deal with hazardous environment or make real-time decisions without the availability of humans. Space autonomy, Internet, robotic vehicles, intelligent systems, wireless networks and power systems provide successful examples of various levels of automation. NASA is conducting research in autonomy and developing plans to increase the levels of automation in aviation operations. This paper provides a brief review of levels of automation, previous efforts to increase levels of automation in aviation operations and current level of automation in the various tasks involved in aviation operations. It develops a methodology to assess the research and development in modeling, sensing and actuation needed to advance the level of automation and the benefits associated with higher levels of automation. Section II describes provides an overview of automation and previous attempts at automation in aviation. Section III provides the role of automation and lessons learned in Space Autonomy. Section IV describes the success of automation in Intelligent Transportation Systems. Section V provides a comparison between the development of automation in other areas and the needs of aviation. Section VI provides an approach to achieve increased automation in aviation operations based on the progress in other areas. The final paper will provide a detailed analysis of the benefits of increased automation for the Traffic Flow Management (TFM) function in aviation operations