With the crew assistant military aircraft (CAMA), a knowledge-based cockpit assistant system for future military transport aircraft is developed and tested to enhance situation awareness. Human-centered automation was the central principal for the development of CAMA. The CAMA-module pilot intent and error recognition evaluates the pilot's activities and mission events in order to interpret and understand the pilot's actions in the context of the flight situation. By monitoring pilot actions as well as the mission context, the system is able to compare the pilot's action with a set of behavioral hypotheses. To cope with the need of inferencing under uncertainty a fuzzy-logic approach is used. Self-organizing maps are applied to improve the fuzzy set data and rule base complying with observed pilot behavior. Hierarchical cluster analysis is used to locate clusters of similar patterns in the maps.
Intent and error recognition as part of a knowledge-based cockpit assistant
1998
13 Seiten, 16 Quellen
Conference paper
English
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