Evaluating pilot situation awareness using multi-agent systems

  • Authors:

  • M. Khazab;S. Lo;K. Kilingaru;J. W. Tweedale;L. C. Jain;S. Thatcher;L. Ding

  • Affiliations:

  • School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Center, University of South Australia, Mawson Lakes, SA, Australia;Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, China;School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Center, University of South Australia, Mawson Lakes, SA, Australia;School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Center, University of South Australia, Mawson Lakes, SA, Australia;School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Center, University of South Australia, Mawson Lakes, SA, Australia;School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Center, University of South Australia, Mawson Lakes, SA, Australia;Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, China

  • Venue:
  • Intelligent Decision Technologies
  • Year:
  • 2013

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Abstract

This paper presents the outcomes of collaborative research between three PhD students working in the Multi-Agent System MAS, Knowledge-Based System KBS, and aviation Situation Awareness SA domains. The aim of this research was to create a MAS that could be used to monitor pilot SA during flight. SA is a cognitive activity that is a critical function conducted by pilots to maintain knowledge of their environment during flight. Good SA ultimately enhances the safety of passengers by reducing the possibility of pilots contributing to a number of documented catastrophic incidents. A controlled experiment has been devised to enable these students to capture and analyse pilot behaviour in an attempt to passively monitor SA monitoring activities using a camera. The MAS consists of multiple agent capabilities that capture the pilots visual acuity and eye movements. This data is used to assess the perceived cognitive activity in real-time. All agents can communicate and share the knowledge captured in order to analyse the activity based on pattern-matching rules using an embedded KBS. The experiments confirmed it is possible to identify at least three specific behaviours. The agents were able to post-process the acquired data to distinguish significant differences between an expert pilot and trained volunteers.