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Aviation industry and certification authorities have recognized the need
for new methods and tools that provide a systematic, efficient and
repeatable approach to the design of future flight deck technologies
(FAA, 1996), including identifying and addressing flight deck
human-automation interaction issues early in the design phase. The need
for a predictive capability has become increasingly important as we
forecast human performance concerns associated with NextGen operations.
The development of design tools is still in a nascent stage, requiring
translation from established theories and experimental practices into
structures supporting design. Research conducted in this area serves a
three-fold purpose: (1) by developing new methods and tools for the
design of human-automation systems, information management and display
systems, and operations needed to support Nextgen, (2) by validating
such methods and tools through their application both to the far-term
Level 3 concepts developed within the project, and to the near-term
concepts developed within the community at large; and (3) by using
developed methods and tools to improve design quality though predictive
methods that allow for a more rigorous exploration of the design space a
priori.
Research under this element does not directly produce new functional
capabilities for the flight deck system. In contrast, this research
results in methods and tools that designers can utilize to clearly
define requirements, understand subsystem relationships and
dependencies, and diagnose or prognosticate flight deck system
vulnerabilities that would otherwise remain unknown. The research in
this element applies, integrates, and validates theoretical approaches
developed into methods and tools that can be used by the design
community. The focus is to embed validated, repeatable analysis methods
into existing and novel design tools, and to allow flight deck designers
to quickly and easily assess designs. In addition, these tools should
allow the domain experts to focus on answering specialized complex
questions.
Associate Principal Investigator: Michael Feary
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