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Awareness in Aviation

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Enhanced flight training program for monitoring aircraft automation

The knowledge and skills required to track and anticipate the behavior of an aircraft’s automated systems, or “mode awareness,” is a topic that has preoccupied the industry and the research community for more than two decades. Issues of “automation surprise” attributed, at least partly, to inadequate monitoring continue to figure prominently in accidents. As the capabilities of automated systems increase, the manner in which humans interact with them changes. At the same time, flight path management concepts and tools continue to increase in complexity, pushing ever more against known pilots’ cognitive limitations and without the requisite shift in training concepts and tools.

A common shortcoming of past and current pilot training in highly automated aircraft is that it rarely, if ever, includes specific instructions in monitoring, especially pertaining to the proper visual scan (sequence and timing of eye movements) of flight instruments and the specific indications critical for obtaining and maintaining awareness of the automation and the performance of the aircraft.

Researchers have explored the use of eye position-based feedback for pilot training, and in recent years the relatively new field of augmented cognition is increasingly looking to further apply the real-time capabilities of evaluating the cognitive state of a user (e.g., using eye tracking, electroencephalography, etc.) to generate better training methods and tools. These projects have mainly focused on instructor feedback, during or after the training. Less is known about whether scanning patterns can be taught ahead of a training event.

This project will investigate the development of strategies for monitoring modern automated systems in commercial aircraft and determine whether some strategies are more effective than others under normal and abnormal operating conditions. The results will then be used to determine what type of monitoring instructions and guidance to include in training and how to effectively implement such instruction in a training measure that will benefit the aviation industry.



Chair of Geoinformation Engineering

  • Prof. Dr. Martin Raubal (PhD student Supervisor, ETH Project Lead)
  • Dr. Peter Kiefer (Substitute ETH Project Lead)
  • David Rudi (PhD student)


SWISS International Airlines Ltd.

  • Christoph Ammann
  • Benedikt Wagner
  • Loukia Loukopoulou

Department of Psychology (University of Oregon)

  • Prof. Dr. Robert Mauro

NASA Ames Research Center, Human Systems Integration Division (NASA)

  • Dr. Immanuel Barshi



  • Start Date: 01.07.2015
  • End Date: 31.12.2017