Human Factor Challenges of Remotely Piloted Aircraft

The control stations of many unmanned systems have been characterized by inadequate human-system interfaces. Some of the interface problems may have been prevented had an existing regulation or cockpit design principle been applied. In other cases, the design problems may indicate a lack of suitable guidance material. The human factors of unmanned operations will be reviewed, and a NASA program to develop human-factor guidelines for control stations will be described. To be effective, guidelines must be relevant to a wide range of systems, must not be overly prescriptive, and must not impose premature standardization on evolving technologies. Several types of guidelines are described. These relate to required capabilities, information requirements, properties of the human machine interface, and general cognitive engineering principles

Human Factors Guidelines for UAS in the National Airspace System

The ground control stations (GCS) of some UAS have been characterized by less-than-adequate human-system interfaces. In some cases this may reflect a failure to apply an existing regulation or human factors standard. In other cases, the problem may indicate a lack of suitable guidance material. NASA is leading a community effort to develop recommendations for human factors guidelines for GCS to support routine beyond-line-of-sight UAS operations in the national airspace system (NAS). In contrast to regulations, guidelines are not mandatory requirements. However, by encapsulating solutions to identified problems or areas of risk, guidelines can provide assistance to system developers, users and regulatory agencies. To be effective, guidelines must be relevant to a wide range of systems, must not be overly prescriptive, and must not impose premature standardization on evolving technologies. By assuming that a pilot will be responsible for each UAS operating in the NAS, and that the aircraft will be required to operate in a manner comparable to conventionally piloted aircraft, it is possible to identify a generic set of pilot tasks and the information, control and communication requirements needed to support these tasks. Areas where guidelines will be useful can then be identified, utilizing information from simulations, operational experience and the human factors literature. In developing guidelines, we recognize that existing regulatory and guidance material will, at times, provide adequate coverage of an area. In other cases suitable guidelines may be found in existing military or industry human factors standards. In cases where appropriate existing standards cannot be identified, original guidelines will be proposed

NASA's UAS Integration into the NAS: A Report on the Human Systems Integration Phase 1 Simulation Activities

In 2011 the National Aeronautics and Space Administration (NASA) began a five-year Project to address the technical barriers related to routine access of Unmanned Aerial Systems (UAS) in the National Airspace System (NAS). Planned in two phases, the goal of the first phase was to lay the foundations for the Project by identifying those barriers and key issues to be addressed to achieve integration. Phase 1 activities were completed two years into the five-year Project. The purpose of this paper is to review activities within the Human Systems Integration (HSI) subproject in Phase 1 toward its two objectives: 1) develop GCS guidelines for routine UAS access to the NAS, and 2) develop a prototype display suite within an existing Ground Control Station (GCS). The first objective directly addresses a critical barrier for UAS integration into the NAS - a lack of GCS design standards or requirements. First, the paper describes the initial development of a prototype GCS display suite and supporting simulation software capabilities. Then, three simulation experiments utilizing this simulation architecture are summarized. The first experiment sought to determine a baseline performance of UAS pilots operating in civil airspace under current instrument flight rules for manned aircraft. The second experiment examined the effect of currently employed UAS contingency procedures on Air Traffic Control (ATC) participants. The third experiment compared three GCS command and control interfaces on UAS pilot response times in compliance with ATC clearances. The authors discuss how the results of these and future simulation and flight-testing activities contribute to the development of GCS guidelines to support the safe integration of UAS into the NAS. Finally, the planned activities for Phase 2, including an integrated human-in-the-loop simulation and two flight tests are briefly described

An integrated approach to rotorcraft human factors research

As the potential of civil and military helicopters has increased, more complex and demanding missions in increasingly hostile environments have been required. Users, designers, and manufacturers have an urgent need for information about human behavior and function to create systems that take advantage of human capabilities, without overloading them. Because there is a large gap between what is known about human behavior and the information needed to predict pilot workload and performance in the complex missions projected for pilots of advanced helicopters, Army and NASA scientists are actively engaged in Human Factors Research at Ames. The research ranges from laboratory experiments to computational modeling, simulation evaluation, and inflight testing. Information obtained in highly controlled but simpler environments generates predictions which can be tested in more realistic situations. These results are used, in turn, to refine theoretical models, provide the focus for subsequent research, and ensure operational relevance, while maintaining predictive advantages. The advantages and disadvantages of each type of research are described along with examples of experimental results

Detect and Avoid: Efforts from NASA's UAS Integration into the NAS Project

NASA's Unmanned Aerial Systems (UAS) integration into the National Air Space (NAS) project has been working closely with the FAA and RTCA Special Committee 228 to identify and break down barriers to UAS integration. A focus of this work is on detect and avoid (DAA) technologies. A pilot has responsibility to see and avoid other aircraft and to remain "well clear," using their best judgment (Federal Aviation Regulations (FAR) Sec. 91.113). For UAS to perform this function, the see function is replaced by sensors to detect the other aircraft. Secondly, the pilot judgment of well clear has to be replaced by a mathematical expression. For Phase 1 of this effort, a well clear violation was defined if all three of these conditions are true: a) the horizontal clearance is less than 4000 ft., and b) the vertical clearance is less than 450 ft., and c) the time to loss of well clear is less than 35 seconds. This definition was developed with a great deal of community input and testing to ensure interoperability with Air Traffic Control (ATC) and pilots of manned aircraft. Appropriate guidance, alerting and displays were developed to allow UAS, with the appropriate sensors, to effectively maintain well clear. This work contributed to FAA Technical Standard Orders: TSO-C211, Detect and Avoid and TSO-C212, ATAR for Traffic Surveillance. Phase 2 of this work extends the operational environment to include the terminal area and lesser capable aircraft that might not have the payload capability to carry the RADAR defined in Phase 1. This session reports on work from Phase 1 and initial work in Phase 2

Beyond Point Design: General Pattern to Specific Implementations

Elsewhere we have discussed a number of problems typical of highly automated systems and proposed tenets for addressing these problems based on Human-Autonomy Teaming (HAT)[1]. We have examined these principles in the context of aviation [2,3]. Here we discuss the generality of these tenets by examining how they might be applied to 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. We then illustrate how the generalities found across aviation, photography and navigation can be captured in a design pattern

Validation of Minimum Display Requirements for a UAS Detect and Avoid System

The full integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS), a prerequisite for enabling a broad range of public and commercial UAS operations, presents several technical challenges to UAS developers, operators and regulators. A primary barrier is the inability for UAS pilots (situated at a ground control station, or GCS) to comply with Title 14 Code of Federal Regulations sections 91.111 and 91.113, which require pilots to see and avoid other aircraft in order to maintain well clear. UAS pilots removal from the flight deck of the aircraft necessitates the development of a UAS-specific system for detecting nearby traffic and displaying traffic information to the pilot to support their ability to maintain an objectively defined DAA well clear threshold from other aircraft. This new UAS-specific function of remaining DAA well clear is called traffic avoidance. The resulting Detect and Avoid (DAA) system, however, will be subject to a collection of requirements that manufacturers will be obligated to meet in order to certify their equipment. RTCA Special Committee 228 (SC-228), a consortium of representatives from government, industry and academia, is responsible for developing and documenting the Minimum Operational Performance Standards (MOPS) for UAS DAA systems. The present study is the final in a series of human-in-the-loop (HITL) experiments designed to explore and test the various display and alerting requirements being incorporated into the DAA MOPS. Whereas the prior DAA HITLs examined a wide variety of DAA display features and concepts, the current experiment aims to validate the latest minimum display requirements for Phase 1 of the DAA MOPS. Rather than test different display concepts, this study tests two configurations of a MOPS-compatible DAA display: a version that is integrated into the primary navigation and control display of the GCS and a version that is physically separated from the primary display. This manipulation tests the draft minimum requirement that allows the DAA traffic display to be a separate, or standalone, configuration. This type of configuration is a more achievable near-term technology solution since it does not stipulate additional certification or integration requirements on UAS manufacturers. However, a standalone display configuration has the potential to result in pilot performance issues resulting from the cognitive costs of switching between the primary DAA display and the primary navigation and control display. This configuration is also particularly susceptible to errors if the displays are in different orientations (e.g., north-up versus track-up). Both the integrated and standalone display configurations were presented to 16 active UAS pilots in a medium-fidelity simulation, which included confederate air traffic controllers and pseudo pilots operating simulated manned traffic. Pilots were tasked with navigating two different mission routes while maintaining DAA well clear with scripted conflicts. Pilot response times (i.e., measured response) and ability to remain DAA well clear are reported. Primary results indicate that both display configurations resulted in favorable response times and well clear rates. While there were clear trends of pilots objectively performing better in the integrated display condition, with several measured response metrics reaching statistical significance, the differences between the two displays were typically moderate. While the primary variable of DAA display location did not have an especially large impact on pilot performance on its own, when examined alongside the type of DAA threat the pilot was facing (a caution-level versus a warning-level alert), the response time benefits associated with the integrated display were amplified. The implications of these American Institute of Aeronautics and Astronautics 2 results on the Phase 1 DAA MOPS and the connection of this data to previous studies is also discussed

Application of Human-Autonomy Teaming (HAT) Patterns to Reduce Crew Operations (RCO)

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

An Evaluation of Detect and Avoid (DAA) Displays for Unmanned Aircraft Systems: The Effect of Information Level and Display Location on Pilot Performance

A consortium of government, industry and academia is currently working to establish minimum operational performance standards for Detect and Avoid (DAA) and Control and Communications (C2) systems in order to enable broader integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS). One subset of these performance standards will need to address the DAA display requirements that support an acceptable level of pilot performance. From a pilot's perspective, the DAA task is the maintenance of self separation and collision avoidance from other aircraft, utilizing the available information and controls within the Ground Control Station (GCS), including the DAA display. The pilot-in-the-loop DAA task requires the pilot to carry out three major functions: 1) detect a potential threat, 2) determine an appropriate resolution maneuver, and 3) execute that resolution maneuver via the GCS control and navigation interface(s). The purpose of the present study was to examine two main questions with respect to DAA display considerations that could impact pilots' ability to maintain well clear from other aircraft. First, what is the effect of a minimum (or basic) information display compared to an advanced information display on pilot performance? Second, what is the effect of display location on UAS pilot performance? Two levels of information level (basic, advanced) were compared across two levels of display location (standalone, integrated), for a total of four displays. The authors propose an eight-stage pilot-DAA interaction timeline from which several pilot response time metrics can be extracted. These metrics were compared across the four display conditions. The results indicate that the advanced displays had faster overall response times compared to the basic displays, however, there were no significant differences between the standalone and integrated displays. Implications of the findings on understanding pilot performance on the DAA task, the development of DAA display performance standards, as well as the need for future research are discussed

Application of Human-Autonomy Teaming (HAT) Patterns to Reduce Crew Operations (RCO)

Unmanned aerial systems, robotics, advanced cockpits, and air traffic management are all examples of domains that are seeing dramatic increases in automation. While automation may take on some tasks previously performed by humans, humans will still be required, for the foreseeable future, to remain in the system. The collaboration with 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