Human factors quantification via boundary identification of flight performance margin

Yang, Chengshuai; Yin, Tangwen; Zhao, Wenhua; Huang, Dan; Fu, Shan

doi:10.1016/j.cja.2014.03.016

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…The evaluation of this parameter is included in the histogram in Figure 5. The most frequently occurring value of this parameter reaches about 0.5 s, a level typical of such human (pilot) activities [26].…”

Section: Statistical Evaluation Of the Selected Parameters Of The Behavioral Modelmentioning

confidence: 96%

Statistical Evaluation of Pilot’s Behavior Models Parameters Connected to Military Flight Training

2020

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The paper discusses the possibilities of objective assessment of military flight training quality based on statistical evaluation of pilot’s behavior models parameters. For these purposes, the pilots’ responses to non-standard flight situations were measured by using a fixed-base and a moving-base engineering flight simulator. Tens of military pilots at different training stages were tested. By exploiting real-life tests, we established that the given pilot models provide sufficiently accurate approximation of realistic human responses. Importantly, the models are relatively easy to use, and the individual parameters can be unambiguously interpreted, i.e., the time constants of the pilot behavior model are obtainable, representing the pilot’s current psychological and physiological state of mind. The parameters lay in the defined ranges, and they characterize the ability of the human/pilot to adapt to a controlled dynamic system. Consequently, a fundamental statistical analysis based on pilot’s behavioral model parameters was conducted, using the acquired test data representing the pilot’s behavior during repeated measuring. The initial results indicate the possibility to use the results for objective assessment the military flight training level.

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Section: Statistical Evaluation Of the Selected Parameters Of The Behavioral Modelmentioning

confidence: 96%

Statistical Evaluation of Pilot’s Behavior Models Parameters Connected to Military Flight Training

2020

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“…[15,18,22,25,36]. In the pilots' physical characteristics, some scholars have studied the hand movements [26,27]. Previous studies on the influencing factors of the pilot's microbehavior have focused on the impact of a single factor on it.…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that with the pilot's psychological load increasing, the cognitive ability has also been declining. Tangwena et al [26,27] solved the cognitive problems of pilots in the process of human-aircraft interaction and studied the microscopic behavior of pilots from the physiological, psychological, and behavioral aspects. Nandiganahalli et al [6,7,28] and Lyu et al [29] proposed a pattern confusion detection framework based on pilot's intention for UI (User Interface) verification.…”

Section: Mode Of Thinkingmentioning

confidence: 99%

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Research on Influencing Factor Selection of Pilot’s Intention

Wang

Pan

et al. 2020

International Journal of Aerospace Engineering

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The physiological, psychological, and physical characteristics of the pilot will have an impact on flight safety, mainly in the pilot’s intention. In another word, this means the pilot’s psychological experience of flight status under the influence of various factors and the preference for decision-making or behavioral value that is displayed. The pilot’s intention is to reflect the cognitive state that the pilot showed during the maneuvering of the aircraft. The exploration of intention is very important for the study of automatic pilot and flight control active safety system. Also, it is an important concept often involved in the study of human factors in flight, especially the microbehavior of pilots. Pilot’s intention is taken as the study object in this paper; physiological-psychological-physical parameters are obtained through analyzing their influencing factors from the simulating flight experiments designed. The random forest analysis method is used to rank the main influencing factors affecting the pilot’s intention, and the factor sequence is formed. The results provide a good foundation for further research on the pilot’s intention identification.

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“…A thorough review of available literature on HFs was performed, and this review was mainly focused on cars [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], planes [30][31][32][33][34][35][36][37][38][39][40][41][42], and NPP operators [43][44][45][46][47][48][49]. The process started with drawing out all possible HFs and listing them up to avoid redundancy and duplicity of information.…”

Section: Human Factors (Hfs) In Control Take-oversmentioning

confidence: 99%

A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

Cárdenas¹,

Shin

Kim

2020

Sustainability

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The purpose of this study is to develop a framework that can identify critical human factors (HFs) that can generate human errors and, consequently, accidents in autonomous driving level 3 situations. Although much emphasis has been placed on developing hardware and software components for self-driving cars, interactions between a human driver and an autonomous car have not been examined. Because user acceptance and trust are substantial for the further and sustainable development of autonomous driving technology, considering factors that will influence user satisfaction is crucial. As autonomous driving is a new field of research, the literature review in other established fields was performed to draw out these probable HFs. Herein, interrelationship matrices were deployed to identify critical HFs and analyze the associations between these HFs and their impact on performance. Age, focus, multitasking capabilities, intelligence, and learning speed are selected as the most critical HFs in autonomous driving technology. Considering these factors in designing interactions between drivers and automated driving systems will enhance users' acceptance of the technology and its sustainability by securing good usability and user experiences.

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Human factors quantification via boundary identification of flight performance margin

Cited by 14 publications

References 21 publications

Statistical Evaluation of Pilot’s Behavior Models Parameters Connected to Military Flight Training

Statistical Evaluation of Pilot’s Behavior Models Parameters Connected to Military Flight Training

Research on Influencing Factor Selection of Pilot’s Intention

A Few Critical Human Factors for Developing Sustainable Autonomous Driving Technology

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