Applying a validation framework to a working airport terminal model

Pitchforth, Jegar; Wu, Paul; Mengersen, Kerrie

doi:10.1016/j.eswa.2014.01.013

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Validation of BBN is challenging, simply applying a comparison to data or using experts to determine validity might overlook important aspects of model uncertainty. 84 Pitchforth and colleagues 84,85 propose a framework to validate BBNs structurally and quantitatively. This framework was chosen for this BBN, since data-driven validation is not possible.…”

Section: Development Of the Bbnmentioning

confidence: 99%

A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration

Thieme

Utne

2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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Autonomous Marine Systems, such as autonomous ships and autonomous underwater vehicles (AUVs), gain increased interest in industry and academia. Expected benefits of AMS in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. AUVs are applied in scientific, commercial and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. AUVs are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with AUVs, in order to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian Belief Network (BBN) to assess the Human Autonomy Collaboration Performance (HAC), as part of a risk model for AUV operation. HAC represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the HAC can be improved in two ways; (i) through better training and inclusion of experienced operators, and (ii) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the HAC BBN can improve AUV design and AUV operations by clarifying relationships between technical, human and organizational factors and their influence on mission risk. The article focuses on AUV, but the results should be applicable to other types of AMS.

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Section: Development Of the Bbnmentioning

confidence: 99%