Engineering topics

Le Vie, Lisa R.

Publications and source records attributed to Le Vie, Lisa R..

7 records

NASA NTRS2019

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 nation’s 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.

Houston, Vincent E.↗

NASA NTRS2018

Impact of Advanced Synoptics and Simplified Checklists During Aircraft Systems Failures

Abstract—Natural human capacities are becoming increasingly mismatched to the enormous data volumes, processing capabilities, and decision speeds demanded in today’s 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 Langley’s 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.

Etherington, Timothy J.↗

NASA NTRS2017

Autonomous System Technologies for Resilient Airspace Operations

Increasing autonomous systems within the aircraft cockpit begins with an effort to understand what autonomy is and developing the technology that encompasses it. Autonomy allows an agent, human or machine, to act independently within a circumscribed set of goals; delegating responsibility to the agent(s) to achieve overall system objective(s). Increasingly Autonomous Systems (IAS) are the highly sophisticated progression of current automated systems toward full autonomy. Working in concert with humans, these types of technologies are expected to improve the safety, reliability, costs, and operational efficiency of aviation. IAS implementation is imminent, which makes the development and the proper performance of such technologies, with respect to cockpit operation efficiency, the management of air traffic and data communication information, vital. A prototype IAS agent that attempts to optimize the identification and distribution of "relevant" air traffic data to be utilized by human crews during complex airspace operations has been developed.

Houston, Vincent E.↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗

NASA NTRS2016

Survey of Quantitative Research Metrics to Assess Pilot Performance in Upset Recovery

Accidents attributable to in-flight loss of control are the primary cause for fatal commercial jet accidents worldwide. The National Aeronautics and Space Administration (NASA) conducted a literature review to determine and identify the quantitative standards for assessing upset recovery performance. This review contains current recovery procedures for both military and commercial aviation and includes the metrics researchers use to assess aircraft recovery performance. Metrics include time to first input, recognition time and recovery time and whether that input was correct or incorrect. Other metrics included are: the state of the autopilot and autothrottle, control wheel/sidestick movement resulting in pitch and roll, and inputs to the throttle and rudder. In addition, airplane state measures, such as roll reversals, altitude loss/gain, maximum vertical speed, maximum/minimum air speed, maximum bank angle and maximum g loading are reviewed as well.

Le Vie, Lisa R.↗

NASA NTRS2014

Using Vision System Technologies to Enable Operational Improvements for Low Visibility Approach and Landing Operations

Flight deck-based vision systems, such as Synthetic and Enhanced Vision System (SEVS) technologies, have the potential to provide additional margins of safety for aircrew performance and enable the implementation of operational improvements for low visibility surface, arrival, and departure operations in the terminal environment with equivalent efficiency to visual operations. To achieve this potential, research is required for effective technology development and implementation based upon human factors design and regulatory guidance. This research supports the introduction and use of Synthetic Vision Systems and Enhanced Flight Vision Systems (SVS/EFVS) as advanced cockpit vision technologies in Next Generation Air Transportation System (NextGen) operations. Twelve air transport-rated crews participated in a motion-base simulation experiment to evaluate the use of SVS/EFVS in NextGen low visibility approach and landing operations. Three monochromatic, collimated head-up display (HUD) concepts (conventional HUD, SVS HUD, and EFVS HUD) and two color head-down primary flight display (PFD) concepts (conventional PFD, SVS PFD) were evaluated in a simulated NextGen Chicago O'Hare terminal environment. Additionally, the instrument approach type (no offset, 3 degree offset, 15 degree offset) was experimentally varied to test the efficacy of the HUD concepts for offset approach operations. The data showed that touchdown landing performance were excellent regardless of SEVS concept or type of offset instrument approach being flown. Subjective assessments of mental workload and situation awareness indicated that making offset approaches in low visibility conditions with an EFVS HUD or SVS HUD may be feasible.

Kramer, Lynda J.↗

NASA NTRS2014

Review of Research On Angle-of-Attack Indicator Effectiveness

The National Aeronautics and Space Administration (NASA) conducted a literature review to determine the potential benefits of a display of angle-of-attack (AoA) on the flight deck of commercial transport that may aid a pilot in energy state awareness, upset recovery, and/or diagnosis of air data system failure. This literature review encompassed an exhaustive list of references available and includes studies on the benefits of displaying AoA information during all phases of flight. It also contains information and descriptions about various AoA indicators such as dial, vertical and horizontal types as well as AoA displays on the primary flight display and the head up display. Any training given on the use of an AoA indicator during the research studies or experiments is also included for review

Le Vie, Lisa R.↗