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SA Index High Level FCM
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This paper reports an ongoing effort that supports NASA research in Airportal Transition and Integration Management as NASA prepares for the challenges anticipated with the Next Generation Air Transportation System (NextGen). The NASA Airportal Project is directed toward achieving increases in capacity and throughput in the terminal area, while mai...
Contexts in source publication
Context 1
... FCM for this project is a computational model of SA that represents SA-Oriented design principles and experimental results from over 40 years of research in aviation and 20 years of research in SA. 23 The design of the FCM began with extensive knowledge elicitation sessions over two days in mid-February, and continued with an additional two-day session in April. The whiteboard sessions identified the high-level categories that are involved with SA and provided an initial quantification of the relationship between those categories and SA as represented in the model by an output value, termed the SA Index Value (Figure 2). Several researchers (between 3 and 6 in each session) with significant experience in investigating SA developed these values by seeking consensus and after a critical review of past SA research literature. ...
Context 2
... FCM is a complex map organized into 8 high level categories. These categories are represented as submaps that pertain to specific aspects of work relevant to SA for pilots and ATC personnel: SA Requirements, Automation Aids, Task Workload, Overall Workload, Alarms, General Design Principles, Uncertainty, and Complexity (see Figure 2). A discussion of the Automation Aids submap follows. ...
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Citations
... In these systems, human operators are often seen as the ultimate safety system and are expected to take over any situation in which the automation is unable to cope. Aviation has many hard-learned lessons to share with other industries such as automotive and medical in the areas of human-automation interaction (Jones et al. 2009;Foyle and Hooey 2008;Funk K. 1991;Miller and Parasuraman 2007;Mosier et al. 1998;Billings and Woods 1994), display design (Ahlstrom and Longo 2003;Funk et al 1999;Ho and Burns 2003 ), communication (Cardosi 1993;Pritchett, Midkiff, and Hansman 1995;Prinzo and Morrow 2002), team coordination (Federal Aviation Administration 1996; Cha and Funk 1997;Bowers et al. 1992) and general safety (Hammer 2010;Gibb and Olson 2008;Wiegmann and Shappell 2001;Federal Aviation Administration 1996;Funk K. 1991). ...
Human factors practitioners (HFPs) play many different roles in the design, creation, operation and maintenance of engineered systems. Less well known are the methods which are aimed at helping with the early stages of design, which are more systems-oriented and often involve questions of the concept of operation in which the engineered system will be fielded. Emerging from the field of cognitive engineering, these methods, including simulation, cognitive work analysis, cognitive task analyses and hierarchical task analysis, will be important as autonomous systems become increasingly capable. Even the most capable systems will continue to interact with humans, and it is at these interfaces between humans and engineered systems that HFP will continue to be needed. This paper describes recent work to leverage these methods to inform concepts of operation in aviation and space, machine learning algorithms and goal-oriented human–machine collaboration.
... • the roles of working memory and long-term memory (Endsley & Bolstad, 1994;Gonzalez & Wimisberg, 2007;Gutzwiller & Clegg, 2012;Sohn & Doane, 2004;Sulistayawati, Wickens, & Chui, 2011), • the mechanisms behind projection (Horswill & McKenna, 2004;Jones, Quoetone, Ferree, Magsig, & Bunting, 2003), • individual characteristics affecting SA abilities (Caretta, Perry, & Ree, 1996;Durso, Bleckley, & Dattel, 2006;Endsley & Bolstad, 1994;Gugerty & Tirre, 2000;O'Brien & O'Hare, 2007;Sulistayawati et al., 2011), • the role of automation on SA (Carmody & Gluckman, 1993;Endsley & Kiris, 1995;Jones, Strater, Riley, Connors, & Endsley, 2009;Kaber & Endsley, 1997a, 2004Riley et al., 2008;Sarter & Woods, 1995), and • the relationship between SA and workload (Bolstad & Endsley, 2000;Endsley, 1993a;Endsley & Rodgers, 1998;Wickens, 1992). ...
Situation awareness (SA) has become a widely used construct within the human factors community, the focus of considerable research over the past 25 years. This research has been used to drive the development of advanced information displays, the design of automated systems, information fusion algorithms, and new training approaches for improving SA in individuals and teams. In recent years, a number of papers criticized the Endsley model of SA on various grounds. I review those criticisms here and show them to be based on misunderstandings of the model. I also review several new models of SA, including situated SA, distributed SA, and sensemaking, in light of this discussion and show how they compare to existing models of SA in individuals and teams.
Pilots use their senses and training to generate situational awareness (SA). They then use this SA to make sound aeronautical decisions. Autonomous vehicles, by contrast, cannot rely on pilot expertise in off‐nominal situations. They must rely on their onboard sensors to build SA of the environment. As these sensors degrade, it is hypothesized that a point exists where the SA generated by these sensors is inadequate to allow the autonomous vehicle to make sound aeronautical decisions. In previous work, a point was defined based on broad assumptions within a modeling and simulation environment (i.e., the error within each sensor was known and not random). This research used a larger data set that contained random errors within the sensors. The data was then used to build predictive equations through a Monte Carlo simulation in the same simulation environment as previous work. While the data showed there was a statistically significant relationship between the error values in each sensor and the fused distance value, the resulting predictive equations were not able to provide adequate SA to make sound aeronautical decisions. This research highlights multiple issues the test and evaluation community will face when trying to develop new techniques for the verification and validation of autonomous systems.
Pilots use situational awareness (SA) to make appropriate aeronautical decisions. Autonomous vehicles will not have a human pilot (or operator) in the loop when off-nominal conditions present themselves, and will rely on sensors to build SA on their environment to make sound aeronautical decisions. As their sensors degrade, it is hypothesized that a point exists where the SA those decisions are based off will be inadequate for sound aeronautical decisions. It will be shown that this point can be identified through modeling and simulation of a simple sensor network to complete a task currently reserved for qualified pilots. This research highlights the process of determining an objective measure for the subjective end and relates it to a possible safety of flight certification for an autonomous system to perform tasks currently reserved for qualified pilots.
