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A human-in-the-loop simulation was conducted to evaluate the impact of Unmanned Aircraft Systems (UAS) with low size, weight, and power (SWaP) sensors operating in a busy, low-altitude sector. Use of low SWaP sensors allow for UAS to perform detect-and-avoid (DAA) maneuvers against non-transponding traffic in the sector. Depending upon the detection range of the low SWaP sensor, the UAS pilot may or may not have time to coordinate with air traffic controllers (ATCos) prior to performing the DAA maneuver. ATCo’s sector performance and subjective ratings of acceptability were obtained in four conditions that varied in UAS-ATCo coordination (all or none) prior to the DAA maneuver and workload (higher or lower). For performance, ATCos committed more losses of separation in high than low workload conditions. They also had to make more flight plan changes to manage the UAS when the UAS pilot did not coordinate DAA maneuvers compared to when they did coordinate the maneuvers prior to execution. Although the ATCos found the DAA procedures used by the UAS in the study to be acceptable, most preferred the UAS pilot to coordinate their DAA maneuvers with ATCos prior to executing them.

NASA's Unmanned Aircraft Systems Integration into the National Airspace System (UAS in the NAS) project examines the technical barriers associated with the operation of UAS in civil airspace. For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgements about a potential threat of a loss of well clear with another aircraft. NASA's Phase 1 research supported the development of a Detect and Avoid (DAA) system that supports the ground-based pilot's ability to detect potential traffic conflicts and determine a resolution maneuver, but existing display/alerting requirements did not account for multiple UAS control (1:N). Demands for increased scalability of UAS in the NAS operations are expected to create a need for simultaneous control of UAs, and thus, a new DAA HMI design will likely be necessary. Previous research, however, has found performance degradations as the number of vehicles under operator control has increased. The purpose of the current human-in-the-loop (HITL) simulation was to examine the viability of 1:N operations with the Phase 1 DAA alerting and guidance. Sixteen UAS pilots flew three scenarios with varying number of UAs under their control (1:1, 1:3, 1:5). In addition to their supervisory and sensor mission responsibilities, pilots were to utilize the DAA system to remain DAA well clear (DWC) during scripted conflicts of mixed severity. Measured response times, separation performance, mission task data, and subjective feedback were collected to assess how the multi-UAS control configuration impacted pilots' ability to maintain DAA well clear and perform the mission tasks. Overall, the DAA system proved surprisingly adaptive to multi-UAS control for preventing losses of DAA well clear (LoDWC). The findings suggest that, while multi-UAS operators are able to maintain safe separation (DWC) from other traffic, their ability to efficiently perform missions drastically decreases with their number of controlled vehicles. Pilot feedback indicated that, for this context, the use of automation support tools for completing and managing mission tasks would be appropriate and desired, especially for ensuring efficient use of assets. Finally, human-machine interface (HMI) design considerations for multi-UAS operations are discussed.

Technical requirements are currently under development for a detect and avoid system (DAA) that would support the operation of Unmanned Aircraft Systems (UAS) within the National Airspace System (NAS). Such a system would aid UAS operators in maintaining sufficient separation, or "well clear", from other aircraft in their vicinity. The first set of technical standards for a UAS DAA system (referred to as "Phase 1" requirements) was limited to UAS operations transiting through Class D, E, and G airspace to, or from, Class A (or special-use) airspace. Explicitly out of scope of the Phase 1 requirements was the operation of UAS within terminal airspace in the NAS. Fern, Rorie, Roberts and Monk (2018) explored the application of a Phase 1 DAA system within Class D terminal airspace in a human-in-the-loop simulation. The results of the study demonstrated a poor fit of the Phase 1 DAA well clear (DWC) definition to these terminal operations. The original DWC definition was designed for the en-route environment and could not account for the complex, tightly-choreographed nature of traffic typically found near an airport. The interaction of the en-route DWC definition and the terminal area traffic led to excessive DAA alerting against safely-separated traffic. The frequent alerting quickly degraded pilots' ability to discriminate between encounters where a maneuver was or was not necessary. This resulted in slower response times, higher rates of losses of DAA well clear, and more severe losses of DAA well clear than had been observed in previous DAA research (e.g., Rorie et al., 2017). The current paper reports on a follow-on study to Fern et al. (2018) that tested two DWC definitions designed to accommodate standard terminal area operating procedures, such as traffic in the downwind leg of a VFR traffic pattern while the UAS is on final. The two definitions utilized identical horizontal and vertical thresholds but differed in whether or not Tau (a function that takes into account the closure rate of two aircraft) was included as a part of the DWC hazard zone. The "Tau" DWC candidate definition nominally extended the DWC hazard zone by 15 seconds in the direction of the intruder aircraft, while the "No Tau" DWC candidate definition did not extend the DWC hazard zone. The results of this follow-on study indicated that both candidate definitions improved pilot and DAA system performance relative to Fern et al (2018). Pilot response times and rates of losses of DWC returned to levels comparable to earlier research in the en-route environment. Several important differences between the two DWC candidate definitions, however, are also discussed.

New technical standards for Unmanned Aircraft Systems (UAS) detect and avoid (DAA) systems mark recent progress toward realizing the goal of full integration of UAS into the National Airspace System (NAS). The DAA system is intended to provide a means of compliance with operating regulations that required pilots on board manned aircraft to remain "well clear" of other aircraft which is accomplished through out-the-window visual acquisition of other aircraft and application of a subjective judgment of safe separation. The requirements for the DAA system, including the specification of a DAA well clear threshold as well as functional requirements for detecting, tracking, alerting and guidance processing, and displays, are specified in DO-365, Minimum Operational Performance Standards (MOPS) for DAA Systems developed within RTCA (Radio Technical Commission for Aeronautics) Special Committee 228 (SC-228). Intended as the first in a series of phased versions, these requirements are frequently referred to as the "Phase 1" DAA system. The Phase 1 DAA system is limited for use by aircraft transitioning to and from Class A or special use airspace, through Class D, E, and G airspace. In particular, the Phase 1 DAA MOPS are not intended for terminal airspace operations, a critical gap for enabling a full range of UAS operations. The application of the Phase 1 DAA system and DAA well clear threshold within the terminal area is predicted to result in a high number of unnecessary alerts when the UAS is safely separated from other traffic. The goal of the present study was to examine pilot performance and operational issues related to the operation of the Phase 1 DAA system in a terminal area. This experiment was intended as an exploratory study that would be used to inform the development of a new terminal area-specific DAA well clear definition, and associated alerting and guidance requirements. The two main objectives of this study were to: 1) characterize pilot behavior in the terminal environment with the Phase 1 DAA system, and 2) investigate the effect of modifications to the Phase 1 DAA alerting and guidance structure. In particular, the authors were interested in determining whether the removal of specific alerting and guidance levels, without changing the DAA well clear definition or alerting thresholds, would impact pilot performance while conducting terminal operations. The results indicate that the Phase 1 well clear definition and alerting and guidance resulted in frequent alerting that degraded pilots' ability to discriminate between encounters where another aircraft was safely separated versus when a maneuver was necessary. The resulting impact on pilot performance was slower response times and higher frequency and severity of losses of DAA well clear compared to those observed for experiments examining pilot performance in the en route environment. There was no significant effect of alerting and guidance display configuration on pilot performance.

In 2012, NASA began exploring the feasibility of single pilot/reduced crew operations in the context of scheduled air carrier operations. The current study examined how important it was for ground-based personnel providing support to single piloted aircraft (ground operators) to have opportunities to acquire situation awareness (SA) prior to being called on to assist an aircraft. We looked at two distinct concepts of operation, which varied in how much information was available to ground operators prior to being called on to assist a critical event (no vs. some Situation Preview). Thirty-five commercial pilots participated in the current study. Results suggested that a ground operators’ lack of initial SA when called on for dedicated assistance is not an issue, at least when the ground operator station displays environmental and systems data which are important to gaining overall SA of the specified aircraft. With appropriate displays, ground operators were able to provide immediate assistance, even if they had minimal SA prior to getting a request.

Situation Awareness (SA) refers to an individual's understanding of what is happening in their environment, and what is likely to happen in the near future [1]. SA is an important construct to study in aviation because lower levels of SA are associated with increased aviation accidents and mishaps [2]. One technique to measure operator SA is through the use of online probe questions, where operators are queried about their task environment at various intervals while performing the task. The current study used the Situation Present Assessment Method (SPAM) [3] for administering online probes. With SPAM, a ready prompt appears on a panel and participants are instructed to accept the prompt when workload permits to be presented with a SA probe question. Latencies in accepting the ready prompt are indicative of the operator's workload level at that time. When ready prompts go unanswered, (ready time-outs; RTO), it is assumed workload is too high. When a prompt is accepted, probe questions are presented for a limited time (60 seconds in this study). Thus it is possible they too will go unanswered by timing out (question time-out; QTO). QTOs can occur for the following reasons: 1) operator did not know the answer, 2) due to the dynamic changes in the task, the operator workload became too high to devote resources to answering the question, 3) question was unanswerable due to the current context, or 4) question required more time to answer than allowed. In the present study, we analyzed a subset of the data from a larger study (see Brandt et al. [4]) to examine reasons for why probe questions go unanswered. For QTOs, the majority of questions went unanswered because the operator did not know how to answer the question, or because the operator was dealing with high workload. RTOs, which occurred more frequently, were normally due to task/workload.

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.

In this study, pilots were asked to achieve a specific time in trail while flying an arrival into Louisville International airport. Weather shortly before the start of the descent added variability to the initial intervals. A spacing tool calculated airspeeds intended to achieve the desired time in trail at the final approach fix. Pilots were exposed to four experimental conditions which varied how strictly the pilots were told to follow these speeds and whether speeds had to be entered into the autopilot manually. Giving the pilots more discretion had little effect on the final spacing interval. However, pilots required to enter speeds into the autopilot manually did not effectively manage their airplane's energy resulting in less accurate performance. While these results may not always generalize to alternative spacing implementations, one should not assume pilots manually closing the loop on automated commands can perform as well as a fully automated system.

This document describes the second human-in-the-loop study in a series that examines the role of a ground operator in enabling single pilot operations (SPO). The focus of this study was decision-making and communication between a distributed crew (airborne pilot and ground operator). A prototype ground station and tools designed to enhance collaboration were also assessed for further development. Eighteen crews flew challenging, off-nominal scenarios in three configurations: Baseline (current two-pilot operations) and SPO with and without Collaboration Tools. Subjective ratings were largely favorable to SPO; however, there was preference for the Baseline configuration. Crew comments suggest improvements to increase the usability of the collaboration tools.

To assess the impact of sleep disturbances on work performance/productivity.Employees (N = 4188) at four US corporations were surveyed about sleep patterns and completed the Work Limitations Questionnaire. Respondents were classified into four categories: insomnia, insufficient sleep syndrome, at-risk, and good sleep. Employer costs related to productivity changes were estimated through the Work Limitations Questionnaire. Performance/productivity, safety, and treatment measures were compared using a one-way analysis of variance model.Compared with at-risk and good-sleep groups, insomnia and insufficient sleep syndrome groups had significantly worse productivity, performance, and safety outcomes. The insomnia group had the highest rate of sleep medication use. The other groups were more likely to use nonmedication treatments. Fatigue-related productivity losses were estimated to cost $1967/employee annually.Sleep disturbances contribute to decreased employee productivity at a high cost to employers.

This paper discusses the quality of a recommender system implemented in a simulation to assist with choosing a diversionary airport for distressed aircraft. In the third of the series of studies investigating the feasibility of ground-supported single pilot operations (SPO) a recommender system was used by 35 airline pilots as an aid for selecting diversionary airports. These pilots, acting as ground operators, used the recommender system from a ground station when off-nominal events required them to provide ground support to a single piloted aircraft. The unique circumstances imposed by each of the scenarios required the ground operators, together with the recommender system, to consider the relative importance of different factors when recommending an airport. Post-trial questionnaires were used to evaluate the recommender system. Results indicated that the pilots did not find the recommender system very transparent and did not always trust its initial recommendation. However, pilots did appear to find the recommender system to be effective in supporting them with the high workload in off nominal situations, and interactions with the system appear to have been satisfactory. Pilots also reported in post simulation surveys a desire to have better explanations for those recommendations. Findings will inform the development of future iterations of the recommender system, as well as influence SPO procedures and further development of a prototype ground station.