Attention Allocation During Human-Autonomy Interactions: A Human-in-the-Loop Simulation Experiment on a Multi-Vehicle Management Task
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Recent developments in technology have permitted an increased use of autonomy. To work best with humans, autonomy should have the qualities of a good team member. But how can these qualities be measured? One way is to use similar measures to those used to measure good teams with human members. For example, the Non-Technical Skills (NOTECHS) framework measures Crew Resource Management (CRM) skills that allow pilots to work together as a team. The framework consists of skill categories, elements of those categories, and behavioral markers that demonstrate good or poor performance in the elements. This paper introduces CMSD (CooperationManagementSituation AwarenessDecision Making), a measurement system based on NOTECHS and other widely-used skill level systems, which provides quantitative measures of Human-Autonomy Teaming (HAT).
Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human autonomy teaming (HAT) in aviation, a study was conducted to evaluate proposed tenets of HAT. Participants performed a flight-following task at a ground station both with and without HAT features enabled. Overall, participants preferred the ground station with HAT features enabled over the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task, reduced the necessary workload and were efficient. Overall, there was general agreement that HAT features supported teaming with the automation. These results will be used to refine and expand our proposed framework for human-autonomy teaming.
The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust).
Aspects of Human Autonomy Teaming - background on safe and efficient crew autonomy teaming project and introduction of three following talks.
Human-Automation Teaming (HAT), is now recognized as a promising solution to the problems of humans managing increasingly complex work systems. A human-automation team can be defined as the interdependent coupling between one or more human operators and one or more autonomous systems requiring collaboration and coordination to accomplish system and task goals (e.g., Langan-Fox et al., 2009). In this conception, automated agents are considered team members that can operate at various levels of automation, be focused on one or more human-information-processing stages, and the interactions with human operators may be adaptable, adjustable or mixed initiative. We investigated some metrics for assessing HAT effectiveness in a demonstration of a HAT tool used by ground station operators in a Reduced Crew Operations project that was conducted at NASA Ames Human Automation Teaming Laboratory. In this paper, we focus on operator metrics of HAT effectiveness, specifically workload and operator behaviors.
The NATO HFM-247 Working Group is creating a summary report of the group's activities on human-autonomy teaming. This chapter is a summary of our at NASA Ames work toward developing a framework for human-autonomy teaming (HAT) in aviation. The purpose of this project was to demonstrate and evaluate proposed tenets of HAT. The HAT features were derived from three tenets and were built into an automated recommender system on a ground station. These tenets include bi-directional communication, automation transparency, and operator directed interface. This study focused primarily on interactions with one piece of automation, the Autonomous Constrained Flight Planner (ACFP). The ACFP is designed to support rapid diversion decisions for commercial pilots in off-nominal situations. Much effort has gone into enhancing this tool not only in capability but also in transparency. In this study, participants used the ACFP at a ground station designed to aid dispatchers in a flight following role to reroute aircraft in situations such as inclement weather, system failures and medical emergencies. Participants performed this task both with HAT features enabled and without and provided feedback. We examined subjective and behavioral indicators of HAT collaborations using a proof-of-concept demonstration of HAT tenets. The data collected suggest potential advantages and disadvantages of HAT.
Over the last two years a number of workshops or similar meetings have been held under the auspice of NASA Ames Research Center that, although dealing with different primary foci, considered at some level the cross-cutting topic of humans-autonomous systems and the partnership between them. This publication presents findings from the reports of these meetings as they inform the process of designing autonomous systems to work in conjunction with humans. Some workshops were highly relevant to this question, others less so. Drawing from conclusions in these reports, this publication presents, at a high level, a summary of issues that could usefully be considered in the design process of automated systems working with human agents. Although differing in emphases, reports from the several workshops reflect many similar beliefs regarding human-autonomy teaming (HAT). Conclusions can be viewed as generally applicable to various aviation venues—air, ground, and air-ground interactions. The objective of the workshops was primarily to identify issues, concerns, and research needs associated with evolving human-autonomy systems—not in providing solutions. The analysis herewithin is generally limited to the findings in the workshop reports. And, since there was often commonality in findings among the several reports, no attempt is made to ascribe particular findings or observations to particular reports.
This talk describes the need for human autonomy teaming (HAT), the basic principles of HAT and the application of those principles in various domains.
This is an invited talk on autonomy and workload for an AFRL Blue Sky workshop sponsored by the Florida Institute for Human Machine Studies. The presentation reviews various metrics of workload and how to move forward with measuring workload in a human-autonomy teaming environment.
Within human factors there is burgeoning interest in the "human-autonomy teaming" (HAT) concept as a way to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous systems as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as "plays." It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations, healthcare and medicine, autonomous vehicles, photography, and aviation. The current paper presents one such effort to apply HAT. It details the design of a HAT agent, developed by Human Automation Teaming Solutions, Inc., to facilitate teamwork between the automation and the human operator of an advanced ground dispatch station. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO); consequently, we have named the agent R-HATS. Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by R-HATS, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, R-HATS determines what tasks need to be completed and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. R-HATS has been designed to both support end users and researchers in RCO and HAT. Additionally, R-HATS and its underlying architecture were developed with generalizability in mind as a modular software applicable outside of RCO/aviation domains. This paper will also discuss future further development and testing of RHATS.
An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes
An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes.
Known problems with automation include lack of mode awareness, automation brittleness, and risk of miscalibrated trust. Human-Autonomy Teaming (HAT) is essential for improving these problems. We have identified some critical components of HAT and ran a part-task study to introduce these components to a ground station that supports flight following of multiple aircraft. Our goal was to demonstrate, evaluate, and refine HAT principles. This presentation provides a brief summary of the study and initial findings.
Automation is playing an increasingly important role in many operations. It is often cheaper faster and more precise than human operators. However, automation is not perfect. There are many situations in which a human operator must step in. We refer to these instances as contingencies and the act of stepping in contingency management. Here we propose coupling Human Autonomy Teaming (HAT) with contingency management. We describe two aspects to HAT, bi-directional communication, and working agreements (or plays). Bi-directional communication like Crew Resource Management in traditional aviation, allows all parties to contribute to a decision. Working agreements specify roles and responsibilities. Importantly working agreements allow for the possibility of roles and responsibilities changing depending on environmental factors (e.g., situations the automation was not designed for, workload, risk, or trust). This allows for the automation to "automatically" become more autonomous as it becomes more trusted and/or it is updated to deal with a more complete set of possible situations. We present a concrete example using a prototype contingency management station one might find in a future airline operations center. Automation proposes reroutes for aircraft that encounter bad weather or are forced to divert for environmental or systems reasons. If specific conditions are met, these recommendations may be autonomously datalinked to the affected aircraft.
Automation has entered nearly every aspect of our lives, but it often remains hard to understand. Why is this? Automation is often brittle, requiring constant human oversight to assure it operates as intended. This oversight has become harder as automation has become more complicated. To resolve this problem, Human-Autonomy Teaming (HAT) has been proposed. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. This paper proposes a framework for HAT, incorporating two key tenets: bi-directional communication, and operator directed authority. We have successfully applied these tenets to integrating the autonomous constrained flight planner (an aide for planning diverts) into a dispatch station. We propose the development of general design patterns that may allow these results to be generalized to domains such as photography and automotive navigation. While these domains are very different, we find application of our HAT tenets provides a number of opportunities for improving interaction between human operators and automation.
This presentation describes the development and demonstration of human autonomy teaming technologies for improving aviation safety and efficiency during nominal and off-nominal operations by developing and validating increasingly autonomous systems concepts, technologies, and procedures.
To achieve the scalability envisioned for many Advanced Air Mobility (AAM) applications, uncrewed aerial system (UAS) concepts are being pursued with the goal of enabling fewer human operators to manage more increasingly autonomous vehicles. NASA’s Transformational Tools and Technologies – Revolutionary Aviation Mobility (T3-RAM) subproject has identified human-autonomy teaming (HAT) as a critical area of research required to support these operations. Under T3-RAM, the HAT Foundational Research Activity has been tasked with providing basic research to identify HAT and human-automation interaction (HAI) principles that can be used to achieve scalable multi-vehicle UAS operations. This paper first outlines a research model to produce ecologically relevant basic research, then contextualizes completed and planned research and development activities within this model. Proposed research threads are presented, along with their practical and theoretical implications.