Rough Draft Peer Review Submission
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MeidanWang5.4-1.docxRunning head: BRAIN BASED HUMAN AUTOMATION DESIGN IN EFFECTIVE UAS OPERATION
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BRAIN BASED HUMAN AUTOMATION DESIGN IN EFFECTIVE UAS OPERATION
Brain-Based Human Automation Design in Effective UAS Operation
ASCI 530 – Unmanned Aerospace Systems
Research Project
Embry-Riddle Aeronautical University-Worldwide
October 13, 2019
Abstract
The unmanned aircraft system (“UAS”) is of developing significance in aviation. Congruent with this development, UAS design has encountered design, safety and operational challenges and brain-based human automation is at the center of these challenges. Indeed, human factors are transcendent in fact key to UAS design, even among such critical variables as UAS maintenance, regulatory issues and safety. Human factors or human-made errors in UAS design and operation, particularly in the form of over reliance on automation, can be avoided if they are foreseen and are well managed. Therefore, through the deployment of case study analysis and exploratory investigation, this research paper proposes that brain-based human automation design in UAS operation can effectively minimize accidents and incidents caused by human error.
Summary
Automation can be defined as the execution by a machine of a function previously carried out by a human (Parasuraman & Riley, 1997). The extensive utilization of automation in complicated applications in the fields of transportation, process control, decision support systems, and quality control and maintenance have encouraging enhancements in system performance, efficiency, and safety (Bailey et al., 2006). Unmanned aircraft systems (“UAS”) are a rapidly developing [technology device] which will continue to develop over time and with the introduction of innovative technologies. However, with the growth and development of UAS, comes design and safety challenges. Through the deployment of case study analysis and exploratory investigation, this research paper will concentrate on UAS design and operation and will examine how brain-based human automation can effectively aid and minimize accidents caused by human error. In effect, brain-based human automation design means the assimilation of a human brain interface into the drone operational system.
In 1996, the Air Force Scientific Board (AFSAB) identified the human-machine interface as the facet of UAS design that required the most improvement and development (Worch et al., 1996). Commercial UAS operational data is poorly recorded and in fact the most substantial source of data for UAS accident cases comes from the U.S. military. This data shows that human error has accounted for approximately half of all UAS mishaps. UAS accidents account for between 28% to 79% of incidents across the U.S. military and 21% to 68% across all UAS types (Marshall et al., 2016).
There is an integral connection between automation innovation and human factors in aviation. Human involvement plays an indispensable role in aviation operating systems, regardless of whether it in general aviation or automation. The ability to “detect and avoid” accidents is one of the leading technical challenges restricting the general operation and advancement of UAS in non-segregated airspaces (Giovanni et al., 2016). Accordingly, the human brain-based design is an exciting arena for analysis since the broad eradication of human-related errors offers universal benefits, not least in the areas of work efficiency and safety. Furthermore, in the context of ATC systems, civil and military UAS operations are currently subject to restrictions that put significant limits on their deployment due to safety concerns (Brooker, 2016). The examination of actual and potential UAS design problems brings substantial benefits across various governmental agencies who operate UAS as well as the growing demand for UAS in commercial aviation.
Issue Statement
Human intervention is an indispensable element in aviation, whether in manned aviation or UAS. The human brain machine interface is the element of UAS that requires the most improvement and development (Worch et al., 1996). Therefore, the specific issue for this research paper is how human error in UAS operations can be mitigated through design and operational changes and what recommendations can be put forth to improve operational safety in the UAS industry as a whole.
Significance of the Issue
Unmanned UASs are quickly becoming a part of the national airspace system (NAS). As UAS begin the transition from military and hobbyist platforms to commercial applications, including security monitoring, satellite transport, and cargo hauling, UAS are naturally becoming part of the national airspace system (NAS). The development – and indeed the full realization of UAS in the NAS – mandates careful UAS design and development in conjunction with the creation of FAA standards and regulations for UAS operations. The U.S. military’s experience that mishap rates for UASs are many times higher than for manned aircraft (Williams, 2004), in fact over thirty times higher, (Department of Defense, 2001), the importance of design and operational standards in UAS is clear, which in turn spotlights human factors in UAS design and operation.
Human factors are of particular importance in the creation of UAS flight guidelines. As noted by (Gavron, 1998), UAS flight presents significant challenges in human factors that transcend those of manned flight. Such challenges emanate from the fact that operator and aircraft are not co-located. In other words, the physical separation of operator and UAS creates noteworthy impediment to optimum human performance. These impediments comprise a loss of sensory cues valuable in-flight control, delays in control and communication, and barriers to observing the visual environment surrounding the UAS. Moreover, UAS operation poses the challenge of simultaneous multiple UAS operation by a single operator, a daunting task which surely places unique and heavy demands on the operator.
Humans factors in UAS operations becomes an issue of importance as consideration is applied to the nexus between commercial and military UAS operation. Proposed commercial uses for UASs include meteorological data collection; border surveillance; search and rescue; disaster monitoring; traffic monitoring; and telecommunications relay. This increased density of commercial UAS must be seen in light of the current reality that military UASs increasingly traverse through civilian airspace during the course of their deployment. The varied nature of UAS military mission characteristics (from line-of-sight communications to over-the-horizon communications, for example) raise the specter of communications delays between operator and vehicle with a corresponding impact on UAS human factors. UAS operators will likely be required to make frequent control inputs, adjusting flight scope or selecting new waypoints in response to developing mission strategy or flight conditions in some applications, whereas in others, UAS flight path will be predetermined and modification less common, reducing the frequency for operators to intervene in flight control operations, which in turn allows for greater reliance on automated vehicle guidance.
Background
To understand the importance of human brain automation in UAS, it is important to step back and consider some fundamentals in human error and ways in which the risks posed by such human factors can be mitigated. As we apply these fundamentals to risk in UAS operation, some understanding of the use of management systems to obviate such risk is pertinent to my research topic.
Nearly all accidents result from human error. This phenomenon arises from the fact that humans govern and accomplish all of the activities necessary to control the risk of accidents. Not only do humans cause accidents by making errors directly related to the UAS operation process itself, but such errors are caused by the creation of in the design and the implementation of management systems (such as chain of authority, accountability, procedures, feedback and continual improvement provisions). Ultimately these management systems govern the human error rate directly contacting or directly influencing the process. The process-related activities where errors have the most influence include process design, engineering and operation, predicting safeguards necessary to control the risk at an acceptable level and sustaining these safeguards for the life of the process, maintaining, inspecting and repairing the process and managing process changes.
At its most basic, there are two types of human error: errors of omission and errors of commission. These errors can occur inadvertently or because the worker believes his way is a better way. Intentional errors are considered as errors in judgment. While some may believe a lack of risk awareness causes such errors, the reality is that the operator who commits an intentional error is well aware of the risk. In other words, the operator believes they know a better way to accomplish a task.
Within UAS operational systems, management process is a valuable way to manage risk of human factor error. Management systems control the interaction of people with each other and with processes and are high level procedures used to control major operational activities such writing operating procedures, training employees, evaluating fitness for duty, conducting incident investigations, etc. If management systems are weak, layers of protection will fail and accidents will happen. As we have established, UAS accidents are caused by human error and Process Safety Management (PSM) is a tool which is focused on maintaining such human errors at an acceptable degree because: (1) all accidents happen due to errors made by humans, premature failure of equipment for example. There is a surfeit of management systems to control such human error and limit their safety impact; (2) when management systems have weakness, near misses can take place occur; and (3) when enough near misses occur, accidents/losses occur.
Figure 1-1. Controlling Risk Triangle. Reprinted from Process Improvement Institute, Inc. Retrieved October 13, 2019. Copyright 2019 by PII. Reprinted with permission.
As this graphic illustrates, if an organization does not directly control risk, the organization cannot directly control quality, safety, environmental impact, or production to acceptable levels. An organization must sustainably control Human factors must be controlled to manage the risk of accidental losses, which in turn impact UAS safety and operations
Related Research and Development
There are a number of published papers that have engaged in research similar to my undertaking. These papers, together with a short analysis of their research and their results is as follows:
Gavron, V.J. (1998). Human factors issues in the development, evaluation, and operation of uninhabited aerial vehicles. AUVSI '98: Proceedings of the Association for Unmanned Vehicle Systems International, 431-438.
The author discusses a number of unique human factors concerns unique to UAS flight. These include: Data link dropouts which may unnoticeable to the operator; UAS mission times may exceed human vigilance capability; Humans can observe only one stream of images at a time, while many UASs provide multiple image streams; Operators are sometimes given with unprioritized lists of multiple of targets (Gavron, 1998). This may be especially problematic in circumstances where the operator is asked to control multiple UAVs simultaneously; Manual control of vehicles with time delays is difficult; Control interface on some systems is poorly designed; Software is not standardized, even among similar UAS systems (Gavron, 1998). Proposed military uses for UASs include special operations; point reconnaissance, cued surveillance, and target acquisition (Gavron, 1998). Non-military uses are possible in the fields such as law enforcement, firefighting, agriculture, construction, archaeology, geology, and postal delivery (Gavron, 1998).
The authors note that UAV operators will probably spend much of their time in supervisory control mode but will be required to switch to manual control suddenly in response to system malfunctions, target acquisition, enemy actions, and other intermittent events (Gunn et.al, 2002). In other words, UAS operation is a form of vigilance. Subjects flew simulated UAV missions. In supervisory control mode, they were required to monitor a stream of digit pairs for a threat warning indicating the presence of an enemy aircraft (Gunn et.al, 2002). In the sensory task, the threat warning was signaled by a size difference between the two digits (Gunn et.al, 2002). In the cognitive task, the threat warning was signaled by an even-odd digit pairing. False alarms were lower for cognitive than for sensory displays (Gunn et.al, 2002). Target acquisition times were shorter for sensory displays than for cognitive. Subjective workload was higher with cognitive than with sensory displays (Gunn et.al, 2002).
Nelson, W. T., Anderson, T.R., McMillan, G.R. (2003). Alternative control technology for uninhabited aerial vehicles: Human factors considerations. Book chapter.
This research chapter discusses potential alternative control technologies for UAVs. These include position and orientation tracking, eye-position tracking, speech recognition, gesture recognition, and electrophysiological measures (Nelson et.al, 2003). The authors advocate increasingly immersive environments for UAV pilots, with eventual possibility that alternative control technologies will replace traditional controls (Nelson et.al, 2003).
Van Erp, J.B.F., & Van Breda, L. (1999). Human factors issues and advanced interface design in maritime unmanned aerial vehicles: A project overview. TNO report TM-99-A004. Soesterberg, The Netherlands: TNO Human Factors Research Institute. Report presents a summary of human factors issues in UAV control, and an overview of relevant research conducted at the TNO Human Factors Research Institute. The authors assume that vehicle control will generally be highly automated, and so focus their discussion on manual control (VanErp et.al, 1999). The authors note that the perceptual information the operator receives from the remote environment is likely to be degraded in several ways. Possible consequences for human performance include poor tracking; difficulty in judging camera, platform, and target motion; confusion about direction of platform flight; confusion about viewing direction of camera; disorientation; degraded situation awareness (VanErp et.al, 1999).
Technological advancements
For the purposes of this research paper, there are two advances in technology which deserve consideration, Artificial Intelligence (AI) together with Deep Learning (DL) and human-machine interfaces (HMIs).
AI is the science and engineering of making intelligent machines. It is the utilization of computer science to understand human intelligence and make tasks which would have otherwise been complex easy to technologically perform (Alan, 2017). In the context of UAS development, a segment of AI this is growing in importance is DL. DL is an AI technique that acquires knowledge through neural network development; a computer system designed to process information in a manner similar to the human brain (Alan, 2017). Neural networks can be taught to identify objects when it is shown many images of a single type of object. Accordingly, UASs can be trained to recognize a particular object and distinguish it from other objects (Alan, 2017).
AI reduces redundancies in UAS operation. Conventionally, a UAS operator travels around an object of interest recording data in form of pictures to be later reviewed by an expert. In all likelihood, there is a data shortfall forcing the UAS to perform multiple missions to capture all the requisite data. With the evolution in AI, however, this redundancy can be removed. Platforms such as ANRA’s DroneOSS platform allows the UAS operator to simply activate the UAS and the platform does the rest (Alan, 2017). It provides a complete end to end solution where optimum flight path is designed in order to optimally capture the most complete digest of data. In turn, this this allows the UAS to generate and analyze thorough 3D models based on the captured flight data (Alan, 2017).
In theory, AI in conjunction with numerous sensors can manipulate a UAS to gather required data while maintaining safety protocols (Alan, 2017). The UAS can autonomously employ AI to understand its operational objective independently of human factors. The UAS can then generate an initial report midflight right there or upload it consistent with the operator’s requirements and specifications. Of course, in the context of human error in UAS operation, the author is not satisfied that AI offers complete solutions.
HMIs are traditionally considered in the arenas of mobile entertainment and productivity. HMI innovation has extended to extend functionality to include interface with and control a wide range of devices and networks, including UAS. Perhaps because the low cost to entry and network security concerns, hobbyists, rather than military or commercial uses, have been at the vanguard of integrating this technology (Dennis et.al, 2015). The benefits include intuitive use, low cost, supportable using widely available commercially-off-the-shelf software and hardware, and capability to provide real-time and low latency data exchange supporting improved functionality (Dennis et.al, 2015). The limitations on such integration are formidable, however. The current regulatory landscape in the U.S. is a considerable barrier to widespread development. Until the regulatory environment is perfected, progress in HMI research and testing is uncertain. As the paradigm shifts from complex software and hardware interactions to simple, ready-to-use technologies in UAS operation, all options available to operators must be evaluated (Dennis et.al, 2015).
Alternative Actions
Recommendation
References
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