An Approach to Autonomous Contingency Management in Urban Air Mobility: The Communication Network Awareness Machine System
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Urban Air Mobility↗
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Results for “Human Autonomy Teaming”
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Human Factors in the Emerging Market of Advanced Air Mobility
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Human-Autonomy Teaming (HAT)↗
Attention Allocation During Human-Autonomy Interactions: A Human-in-the-Loop Simulation Experiment on a Multi-Vehicle Management Task
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Human-Autonomy Teaming↗
m:N Working Group Status Report
This document reports on the status of the m:N working group including the barriers, sub-group status, DAA white paper and roadmapping exercise.
multi-vehicle control↗
A New Control Paradigm: Multiple Aircraft Controlled by Multiple Operators
Remotely piloted aircraft systems (RPAS) are becoming more and more prevalent in the aerospace operations. This is true in a number of diverse domains; urban air mobility, medical product delivery, infrastructure inspection, high altitude pseudo-satellites, search and rescue, auto cargo and several other applications. One aspect that all of these share in common is the need for scalability to be viable and continue to grow. The Association of Uncrewed Vehicle Systems International (AUVSI) develops an annual economic report. They project that in the first three years of integration more than 70,000 jobs will be created in the US alone, with an economic impact of more than $13.6 billion. This benefit will grow through 2025 when we foresee more than 100,000 jobs created and economic impact of $82 billion. For many of these domains to reach these levels and have the scalability needed, they will require a remote pilot to control multiple aircraft (1:N) or the extension of that, multiple pilots controlling multiple aircraft (m:N). This is a new control paradigm that raises multiple issues in various areas. The issues include regulatory, technical, safety, community acceptance and Human Factors. Human factors issues include displays, pilot workload, pilot situation awareness just to name a few. This panel brings together researchers, developers and operators that have been working in the area of m:N. They will discuss the need, the issues and some potential solutions.
multi-vehicle control↗
m:N Working Group Status Report: Collaboration with Government, Industry, and Academia to Reduce m:N Operational Barriers
This is a status report on the m:N working group that has met three times over the last two years. They have identified barriers by domain, detect and avoid use-cases, and further analysis.
multi-vehicle control↗
Humans as Failsafe
Automation often relies on the human to "jump" into the loop and solve problems when the automation can’t due to conditions outside of which it was designed for or automaton failure. This talk proposes a methodology (Human Autonomy Assistant) to address that situation. Examples are provided from a wild fire context.
multi-vehicle control↗
m:N: m Operators Controlling N Vehicles
UAS are growing quickly and the promise of economic growth is large and real. However, for many domains to realize this potential, multi-vehicle control by a single operator (or m:N) is required. NASA has stood up an industry/gov't working group to identify issues and barriers. This work will be reviewed.
multi-vehicle control↗
Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment
Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.
Advanced Air Mobility↗
Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment
Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.
Advanced Air Mobility↗
Human Factors Issues: m:N Automation
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multi-vehicle control↗
Attentional Limits and Advanced Air Mobility Ecosystem: A Model of Attention Allocation, Trust, and System Performance
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Advanced Air Mobility↗
Advancements in Remote Ground Control Station Operator Pilot in Command Training Program for Beyond Visual Line of Sight Flight Operations
The training program for a Remote Ground Control Station Operator Pilot in Command (R-GCSO PIC) at NASA Langley Research Center marks a pivotal evolution in preparing operators for Beyond Visual Line of Sight (BVLOS) operations. This program, developed within the Advanced Air Mobility (AAM) project High Density Vertiplex (HDV) subproject, was crafted to bridge the gap between traditional Ground Control Station Operators (GCSO) and R-GCSO PICs, focusing on uncrewed aircraft systems (UAS). It encompassed extensive theoretical and practical training, including hands-on experience with advanced simulators and live flight operations, while ensuring a deep understanding of BVLOS complexities. The training leveraged NASA technologies like the MPATH (Measuring Performance for Autonomy Teaming with Humans) ground control station software and incorporated human factors principles to enhance operational readiness. This paper details the program's development, execution, and the critical insights gained, emphasizing the necessity of continuous adaptation in training methodologies to meet the evolving demands of UAS operations in the National Airspace System.
Ground control station operator↗
True Partnership: An Actual Human Machine Team
Presentation covering current SOA and what is required to move to autonomous teammates.
human machine team↗
Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success
An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.
E L Meszaros↗
Anomaly Detection, Active Learning, Precursor Identification,and Human Knowledge for Autonomous System Safety
The project Autonomy Teaming and TRajectories for ComplexTrusted Operational Reliability (ATTRACTOR) researched and developed Artificial Intelligence with application to multi-Unmanned Aerial Systems (UAS) missions. Such missions, like other complex systems-of-systems, are likely to have previously-unknown, safety relevant anomalies occur due to many possible factors including system failures or degradations, emergent behavior, changes in the environment in which the systems operate, changes in the way the systems are operated. We discuss the application of anomaly detection, active learning, and precursor identification to identify such anomalies and the conditions under which they are more likely to appear. We demonstrate results on simulated multi-UAS missions that show promise to be applied to real missions.
machine learning↗
Analyzing Natural Language Context in Human-Machine Teaming using Supervised Machine Learning
Building a foundation for trustworthiness and trust verification in multi-asset teaming is the research challenge of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Design Reference Mission (DRM) for ATTRACTOR is a search and rescue mission objective governed by a multi-member team consisting of human and machine operators. A crucial component to the effort is the communication between humans and autonomous agents throughout both planning and execution stages of the mission. Intuitive communication methods and modalities are posited as critical enablers for certifying trust and trustworthiness. This paper reports on the data collection and analysis conducted in support of the Human Informed Natural-language GANs Evaluation (HINGE)project to attain explainable and trusted communication between human-machine assets. Two identically curated image description datasets were acquired for HINGE, both consisting of two unique input modalities (typed vs. verbal) and retrieved in two distinct contexts (general vs. specific). The gathered datasets were assessed and compared using Parts-of-Speech (POS)features, sentence similarity metrics, and linguistic analysis. Then, the datasets were modeled and tested separately and in combination with one another using machine learning algorithms. The comparison and testing results reveal a superior dataset, by which a preferred context and input is understood, for generating image representations of missing persons using a Generative Adversarial Network (GAN).
Bryan A Barrows↗
Towards Informing an Intuitive Mission Planning Interface for Autonomous Multi-Asset Teams via Image Descriptions
Establishing a basis for certification of autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR). The Human-Machine Interface (HMI) team is working to capture and utilize the multitude of ways in which humans are already comfortable communicating mission goals and translate that into an intuitive mission planning interface. Several input/output modalities (speech/audio, typing/text, touch, and gesture) are being considered and investigated in the context human-machine teaming for the ATTRACTOR design reference mission (DRM) of Search and Rescue or (more generally) intelligence, surveillance, and reconnaissance (ISR). The first of these investigations, the Human Informed Natural-language GANs Evaluation (HINGE) data collection effort, is aimed at building an image description database to train a Generative Adversarial Network (GAN). In addition to building an image description database, the HMI team was interested if, and how, modality (spoken vs. written) affects different aspects of the image description given. The results will be analyzed to better inform the designing of an interface for mission planning.
Generative Adversarial Network (GAN)↗