Avionics Human-Machine Interfaces and Interactions for manned and unmanned aircraft

  • Yixiang Lim, Alessandro Gardi, Roberto Sabatini, Subramanian Ramasamy, Trevor Kistan, Neta Ezer, Julian Vince, Robert Bolia
  • Progress in Aerospace Sciences, August 2018, Elsevier
  • DOI: 10.1016/j.paerosci.2018.05.002

Avionics Human-Machine Interfaces and Interactions for Manned and Unmanned Aircraft

What is it about?

Technological advances in avionics systems and components have facilitated the introduction of progressively more integrated and automated Human-Machine Interfaces and Interactions (HMI²) on-board civil and military aircraft. A detailed review of these HMI² evolutions is presented, addressing both manned aircraft (fixed and rotary wing) and Remotely Piloted Aircraft System (RPAS) specificities for the most fundamental flight tasks: aviate, navigate, communicate and manage. Due to the large variability in mission requirements, greater emphasis is given to safety-critical displays, command and control functions as well as associated technology developments. Additionally, a top-level definition of RPAS mission-essential functionalities is provided, addressing planning and real-time decision support for single and multi-aircraft operations.

Why is it important?

While current displays are able to integrate and fuse information from several sources to perform a range of different functions, these displays have limited adaptability. Further development to increase HMI² adaptiveness has significant potential to enhance the human operator's effectiveness, thereby contributing to safer and more efficient operations. The adaptive HMI² concepts in the literature contain three common elements. These elements comprise the ability to assess the system and environmental states; the ability to assess the operator states; and the ability to adapt the HMI² according to the first two elements. While still an emerging area of research, HMI² adaptation driven by human performance and cognition has the potential to greatly enhance human-machine teaming through varying the system support according to the user's needs. However, one of the outstanding challenges in the design of such adaptive systems is the development of suitable models and algorithms to describe human performance and cognitive states based on real-time sensor measurements. After reviewing the state-of-research in human performance assessment and adaptation techniques, detailed recommendations are provided to support the integration of such techniques in the HMI² of future Communications, Navigations, Surveillance (CNS), Air Traffic Management (CNS/ATM) and Avionics (CNS + A) systems.

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http://dx.doi.org/10.1016/j.paerosci.2018.05.002

The following have contributed to this page: Prof. Roberto Sabatini