Application of human-autonomy teaming to an advanced ground station for reduced crew operations

Abstract

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 R-HATS.

Comparison and synthesis of two aerospace case studies to develop human-autonomy teaming requirements

“…Even with increased autonomous system support, SPO may cause an increase in the pilot workload, specifically when the autonomous system fails to understand the pilot's intention and other context information (Zhang et al, 2021), as well as introduce significant safety issues (Dorneich et al, 2016). Hence, HAT for SPO/RCO concepts must be studied to identify the transition plan from dual-pilot operations to SPO and develop risk mitigation techniques (Ho et al, 2017;Shively, 2017).…”

“…For example, a single pilot and autonomous system should fluidly hand over tasks to each other depending on the situation (Zhang et al, 2021). Like a human teammate, an autonomous teammate could dynamically modify task assignments between the human and autonomous teammates as conditions change (Johnson, 2010;Comerford et al, 2013;Neogi et al, 2016;Ho et al, 2017). However, allocating the tasks should be done with an awareness of each teammate's capabilities and limitations (Ulusoy and Reisman, 2022).…”

Approaches for assessing communication in human-autonomy teams

“…Interviews with subject matter experts are used to identify the relevant and key individuals on the chosen team level. For example, Ho et al's (2017) work illustrated a multi-stakeholder example involving the end-users (i.e., pilots), engineers (i.e., developers and technical personnel), and managers (i.e., trainers and high-level decision makers).…”

Physician Well-being and the Future of Health Information Technology

“…In creating this next level of interaction design, an existing body of research involving human-human collaboration as well as human-computer models of collaboration [34][35][36][37][38][39][40] may all be relevant along with ongoing human-computer interaction research. Humanistic decision support might build on insights from decision-making research across topics such as decisional conflict, 41 debiasing, 42,43 patient/physician shared decision making, 44 decision regret, [45][46][47][48] decision closure, 49,50 and ambiguity and uncertainty.…”