Modeling Human Pilot Behavior for Aircraft With a Smart Inceptor

2019 ◽  
Vol 49 (6) ◽  
pp. 661-671
Author(s):  
Shuting Xu ◽  
Wenqian Tan ◽  
Xiangju Qu
Keyword(s):  
2021 ◽  
Author(s):  
Alexander Efremov ◽  
Ilias Irgaleev ◽  
Mikhail Tiaglik

Author(s):  
Alexander Efremov ◽  
Ilias Irgaleev ◽  
Mikhail Tiaglik

Author(s):  
Richard Clewley ◽  
Jim Nixon

Objective We extend the theory of conceptual categories to flight safety events, to understand variations in pilot event knowledge. Background Experienced, highly trained pilots sometimes fail to recognize events, resulting in procedures not being followed, damaging safety. Recognition is supported by typical, representative members of a concept. Variations in typicality (“gradients”) could explain variations in pilot knowledge, and hence recognition. The role of simulations and everyday flight operations in the acquisition of useful, flexible concepts is poorly understood. We illustrate uses of the theory in understanding the industry-wide problem of nontypical events. Method One hundred and eighteen airline pilots responded to scenario descriptions, rating them for typicality and indicating the source of their knowledge about each scenario. Results Significant variations in typicality in flight safety event concepts were found, along with key gradients that may influence pilot behavior. Some concepts were linked to knowledge gained in simulator encounters, while others were linked to real flight experience. Conclusion Explicit training of safety event concepts may be an important adjunct to what pilots may variably glean from simulator or operational flying experiences, and may result in more flexible recognition and improved response. Application Regulators, manufacturers, and training providers can apply these principles to develop new approaches to pilot training that better prepare pilots for event diversity.


1976 ◽  
Vol 20 (17) ◽  
pp. 403-409
Author(s):  
Miles R. Murphy

Selected literature on individual differences in pilot performance is reviewed in order to indicate a possible direction for research. Decision-making performance in contingency situations is seen as a potentially fruitful area for study of individual differences, although information on the relative roles of experience and cognitive abilities, styles, and strategies are needed in all research areas. The role of cognitive styles in pilot performance is essentially unexplored; however, the identification of individual pilot behavior differences that have been attributed to style differences and the results of automobile driver behavior research suggest considerable potential. Approaches to studying pilot decision-making processes are discussed, with emphasis given to the wrong-model approach in which accident and incident data, or “process tracing” provide experimental computational structures. Analysis of data from a simulator experiment on V/STOL zero-visibility landing performance suggests that the order of ranking of individual pilot's effectiveness varies with particular situations defined by combinations of tracking requirements (e.g., glide slope, localizer) and glide-slope segment, or speed requirements; the data are being further analyzed.


2005 ◽  
Author(s):  
Jeffrey B. Mulligan ◽  
Xavier L. C. Brolly
Keyword(s):  

Author(s):  
Sallie E. Gordon ◽  
Bettina A. Babbitt ◽  
Herbert H. Bell ◽  
H. Barbara Sorensen

Training programs for complex tasks are increasingly using simulations to provide transfer of training to the job environment without incurring high costs of on-the-job training. A second trend in training is toward the use of intelligent tutoring systems (ITSs) to provide individualized feedback to optimize training. Combining simulation with an ITS can be especially beneficial, but use of intelligent tutoring mechanisms such as expert systems is often difficult in a complex, realtime environment. In this paper, we describe the development of a proof-of-concept training program that combines F-16 flight simulation with an embedded real-time intelligent tutoring system. In the simulation, pilots learn the correct use of advanced fire control radar modes to locate and assess multiple enemy formations (search and sort tasks). The expert system monitors pilot behavior and verbal responses as the pilot flies the simulation. At certain critical points, if the pilot's performance has fallen outside of pre-specified parameters of “safe” behavior, the tutoring component stops the simulation and feedback is provided.


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