e-pilots

New technologies have allowed pilots to assume more responsibilities reducing the number of some mechanic and repetitive tasks through automation. The flight deck transition to digital computer-based flight management system has shown a reduction of the workload across a variety of tasks. However, there are still several open issues that requires further research and a deeper understanding of pilot cognitive decision-making process to guide next steps on aircraft control task sharing between human pilots and machines.

 

Present project proposal will analyse the benefits of cognitive computing assistant and the challenges in key areas such as the relationship between pilot with the surrounding traffic and the aircraft state evolution. These challenges will be evaluated in the context of operational factors such as task responsibilities, situational awareness, and the performance objectives of the mission among others. The dynamics of these interdependencies will be analysed by means of machine learning techniques identifying the different thresholds that activates a cognitive task. These thresholds will be the baseline to elaborate new symbiotic dynamic structures to improve human-machine information sharing mechanisms.

Human-in-the-mesh modelling paradigm relying on cognitive computing technologies will drive the research and innovation efforts. E-pilots aims to identify the potential changes in pilot roles and task responsibilities that will improve aircraft system reliability and performance. Additionally, the project will identify the pathway to automation in the flight deck to simplify the crew interaction with the systems and convert pilots’ tacit knowledge into explicit knowledge that can be accessed and utilised by automated, intelligent systems.

This proposal considers flight deck operations for “human-computer symbiosis” as a metaphor for designing decision support systems that enhance human cognitive performance. Furthermore, the project will explore the application of Strong Cognitive Symbiosis approach which analyses true interdependence rather than simply cooperation between human and machines.

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