A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews

Zhang, Y; Yao, K; Zhang, J; Jiang, F; Warren, M

A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews

Zhang, Y Yao, K Zhang, J Jiang, F

Warren, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Access, 2020, 8, pp. 28021-28032
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Yao, K
dc.contributor.author	Zhang, J
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Warren, M
dc.date.accessioned	2021-05-24T21:29:23Z
dc.date.available	2021-05-24T21:29:23Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Access, 2020, 8, pp. 28021-28032
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/149171
dc.description.abstract	In order to ensure the normal and stable flights in the aircraft, a variety of sensors and corresponding instrumentation systems have been applied on the aircraft to monitor/control the current flight status, and the resulted data ensure the flight safety with a heavy burden on the pilot. In views of this, nowadays, the aircraft cockpit automation assistance system has become a hot topic. This paper is based on the pilot's future operational behavior which can be predicted through different stages of flight operations after the automated assistance system is triggered, thus providing the pilot with assistance in accordance with his operating habits. We have established a MDP (Markov Decision Process) model via analyzing and modeling of pilot operational behavior and mission requirements for flight processes, and we also use value iterative algorithm to find the optimal prediction sequence, lastly, we verify the operability of the algorithm by flight operation simulation experiment. It provides a new solution for the safety of pilot operations and the intrusiveness of the cockpit adaptive automation assistance system.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2019.2961239
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	A New Markov Decision Process Based Behavioral Prediction System for Airborne Crews
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	open_access	*
dc.date.updated	2021-05-24T21:29:22Z
pubs.publication-status	Published
pubs.volume	8

Abstract:

In order to ensure the normal and stable flights in the aircraft, a variety of sensors and corresponding instrumentation systems have been applied on the aircraft to monitor/control the current flight status, and the resulted data ensure the flight safety with a heavy burden on the pilot. In views of this, nowadays, the aircraft cockpit automation assistance system has become a hot topic. This paper is based on the pilot's future operational behavior which can be predicted through different stages of flight operations after the automated assistance system is triggered, thus providing the pilot with assistance in accordance with his operating habits. We have established a MDP (Markov Decision Process) model via analyzing and modeling of pilot operational behavior and mission requirements for flight processes, and we also use value iterative algorithm to find the optimal prediction sequence, lastly, we verify the operability of the algorithm by flight operation simulation experiment. It provides a new solution for the safety of pilot operations and the intrusiveness of the cockpit adaptive automation assistance system.

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http://hdl.handle.net/10453/149171