A situation-driven adaptive pilot/vehicle interface

Mulgund, Sandeep; Zacharias, Greg L.

doi:10.1109/huics.1996.549515

Cited by 10 publications

(14 citation statements)

References 18 publications

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“…The suggested representation and management of the workload holds the qualities of sensitivity (each task or task type has its own description), being continuous, predictive, context-rich and non-intrusive. The qualities of continuous, predictive and context-rich can also be found at a cockpit levelin an adaptive associate system when it comes to monitoring the state of the pilot, predicting his/her future state, and smoothly reacting to adjust the pilot's level of workload by providing automated support or re-allocating tasks [11], [24], [25].…”

Section: Ii5 Workload Monitoringmentioning

confidence: 99%

Dynamic Workload Management for Multi-RPAS Pilots

Fas-Millán

Pastor

2019

IREASE

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This document describes a key aspect of NtoM, a concept of operations (ConOps) currently under development, which focuses on the awareness, productivity and safety of Remotely Piloted Aircraft System (RPAS) pilots controlling several flights at once in non-segregated airspace. An explanation will be given of how the ConOps suggests capturing, representing, managing and predicting the workload of the pilots. To illustrate some of the features of the concept, it was necessary to define a representation of the workload associated to the tasks. A synthetic task environment that used the NtoM prototype was built and used to evaluate the requirements of time and attention of pseudo-pilots based on their performance while executing the tasks and task overlaps, determine the top threshold of workload allowed for a pilot and detect incompatibilities among tasks. These values served as a reference to design demanding test scenarios, which helped to reveal weaknesses and inspire improvements that were addressed in the following stage of development.

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Section: Ii5 Workload Monitoringmentioning

confidence: 99%

Dynamic Workload Management for Multi-RPAS Pilots

Fas-Millán

Pastor

2019

IREASE

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“…In this effort, we analyzed a range of approaches we have taken to modeling meta-information in previous efforts. In exploring these methods, we drew upon extensive past research in which we modeled human behavior and indirectly included the impact of meta-information upon that behavior (Pfautz et al, 2005;Aykroyd et al, 2005;Guarino et al, 2004;Mulgund et al, 1996). Herein, we review these approaches, and provide analysis on their capability to address problems associated with modeling meta-information.…”

Section: Bayesian Approaches To Modeling Meta-informationmentioning

confidence: 99%

Modeling human reasoning about meta-information

Guarino

Pfautz

Cox

et al. 2009

International Journal of Approximate Reasoning

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Information, as well as its qualifiers, or metainformation, forms the basis of human decisionmaking. Modeling human reasoning therefore requires the development of representations of both information and meta-information. However, while existing models and modeling approaches may include computational technologies that support meta-information analysis, they generally neglect its role in human reasoning. Herein, we describe the application of Bayesian Belief Networks to model how humans calculate, aggregate, and reason about metainformation when making decisions.

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“…Results showed significantly faster response times when bimodal warnings were given as compared to either unimodal alert. This suggests that correlated bimodal presentation can be desirable for fimctions ^ere attention priority is not an issue (Mulgund and Zacharias, 1996).…”

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confidence: 99%