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This article describes an iterative application of Rapid Assessment Procedures (RAP) to study human-machine interaction issues with a recently implemented, highly automatic system on the F-16 fighter aircraft known as the Automatic Ground Collision Avoidance System (Auto-GCAS). Auto-GCAS is a sophisticated technology that has the ability to detect an imminent ground collision threat and automatically execute a last-moment maneuver to avoid a crash. We employed RAP methods at multiple United States Air Force and Air National Guard sites in the United States and abroad. Over a three-year period, we conducted semi-structured interviews with 402 F-16 pilots experienced with the system. The method we employed, termed here iterative RAP, is reviewed in detail and evaluated in the framework of Utarini, Winkvist, and Pelto's (2001) eleven criteria for quality RAP studies. Results from this study include assisting in correcting system misunderstandings and anomalies, improving information flow about Auto-GCAS, and contributing towards the perception of military safety systems as valuable. This article concludes by (1) discussing positive effects that iterative RAP can contribute to the defense community and (2) arguing our method's utility towards the study of complex bureaucracies and multi-sited research, particularly following the introduction of new technology or policy.

We examined F-16 pilots’ trust of the Automatic Ground Collision Avoidance System (Auto-GCAS), an automated system fielded on the F-16 to reduce the occurrence of controlled flight into terrain. We looked at the impact of experience (i.e., number of flight hours) as a predictor of trust perceptions and complacency potential among pilots. We expected that novice pilots would report higher trust and greater potential for complacency in relation to Auto-GCAS, which was shown to be partly true. Although novice pilots, compared with experienced pilots, reported equivalent trust perceptions, they also reported greater complacency potential.

In this paper we describe results from the first year of field study examining U.S. Air Force (USAF) F-16 pilots’ trust of the Automatic Ground Collision Avoidance System (Auto-GCAS). Using semistructured interviews focusing on opinion development and evolution, system transparency and understanding, the pilot–vehicle interface, stories and reputation, usability, and the impact on behavior, we identified factors positively and negatively influencing trust with data analysis methods based in grounded theory. Overall, Auto-GCAS is an effective life-/aircraft-saving technology and is generally well received and trusted appropriately, with trust evolving based on factors including having a healthy skepticism of the system, attributing system faults to hardware problems, and having trust informed by reliable performance (e.g., lives saved). Unanticipated findings included pilots reporting reputation to not be negatively affected by system activations and an interface anticipation cue having the potential to change operational flight behavior. We discuss emergent research avenues in areas of transparency and culture, and values of conducting trust research with operators of real-world systems having high levels of autonomy.

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 generaliz ability 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.

We performed a human-in-the-loop study to explore the role of transparency in engendering trust and reliance within highly automated systems. Specifically, we examined how transparency impacts trust in and reliance upon the Autonomous Constrained Flight Planner (ACFP), a critical automated system being developed as part of NASA's Reduced Crew Operations (RCO) Concept. The ACFP is designed to provide an enhanced ground operator, termed a super dispatcher, with recommended diversions for aircraft when their primary destinations are unavailable. In the current study, 12 commercial transport rated pilots who played the role of super dispatchers were given six time-pressured “all land” scenarios where they needed to use the ACFP to determine diversions for multiple aircraft. Two factors were manipulated. The primary factor was level of transparency. In low transparency scenarios the pilots were given a recommended airport and runway, plus basic information about the weather conditions, the aircraft types, and the airport and runway characteristics at that and other airports. In moderate transparency scenarios the pilots were also given a risk evaluation for the recommended airport, and for the other airports if they requested it. In the high transparency scenario additional information including the reasoning for the risk evaluations was made available to the pilots. The secondary factor was level of risk, either high or low. For high-risk aircraft, all potential diversions were rated as highly risky, with the ACFP giving the best option for a bad situation. For low-risk aircraft the ACFP found only low-risk options for the pilot. Both subjective and objective measures were collected, including rated trust, whether the pilots checked the validity of the automation recommendation, and whether the pilots eventually flew to the recommended diversion airport. Key results show that: 1) Pilots' trust increased with higher levels of transparency, 2) Pilots were more likely to verify ACFP's recommendations with low levels of transparency and when risk was high, 3) Pilots were more likely to explore other options from the ACFP in low transparency conditions and when risk was high, and 4) Pilots' decision to accept or reject ACFP's recommendations increased as a function of the transparency in the explanation. The finding that higher levels of transparency was coupled with higher levels of trust, a lower need to verify other options, and higher levels of agreement with ACFP recommendations, confirms the importance of transparency in aiding reliance on automated recommendations. Additional analyses of qualitative data gathered from subjects through surveys and during debriefing interviews also provided the basis for new design recommendations for the ACFP.

The current research discusses transparency as a means to enable trust of automated systems. Commercial pilots (N = 13) interacted with an automated aid for emergency landings. The automated aid provided decision support during a complex task where pilots were instructed to land several aircraft simultaneously. Three transparency conditions were used to examine the impact of transparency on pilot’s trust of the tool. The conditions were: baseline (i.e., the existing tool interface), value (where the tool provided a numeric value for the likely success of a particular airport for that aircraft), and logic (where the tool provided the rationale for the recommendation). Trust was highest in the logic condition, which is consistent with prior studies in this area. Implications for design are discussed in terms of promoting understanding of the rationale for automated recommendations.

Unmanned aerial systems, advanced cockpits, and air traffic management are all seeing dramatic increases in automation. However, while automation may take on some tasks previously performed by humans, humans will still be required to remain in the system for the foreseeable future. The collaboration between humans and these increasingly autonomous systems will begin to resemble cooperation between teammates, rather than simple task allocation. It is critical to understand this human-autonomy teaming (HAT) to optimize these systems in the future. One methodology to understand HAT is by identifying recurring patterns of HAT that have similar characteristics and solutions. This paper applies a methodology for identifying HAT patterns to an advanced cockpit project.