Operator State Estimation to Enable Adaptive Assistance in Manned-Unmanned-Teaming |
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| Institution: | 1. Federal University of the Valleys of Jequitinhonha and Mucury, Diamantina, MG, Brazil;2. University of Sao Paulo, Brazil;1. Zander Laboratories B.V., Amsterdam, The Netherlands;2. Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany;3. Airbus Central Research & Technology, Hamburg, Germany;4. Airbus Defence & Space, Manching, Germany;5. Cognitive Modeling in Dynamic Human-Machine Systems, Technische Universität Berlin, Berlin, Germany;1. Laboratory of Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS), Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Barcelona, Spain;2. Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain;1. Human Factor Institute, University of South China, Hengyang 421001, Hunan, People’s Republic of China;2. Systems Engineering & Engineering Management, University of North Carolina at Charlotte, 28223 Charlotte, USA;3. School of Nuclear Science and Technology, University of South China, Hengyang 421001, Hunan, People’s Republic of China;4. Institute of Human Factor and Safety Management, Hunan Institute of Technology, Hengyang 421002, Hunan, People’s Republic of China |
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| Abstract: | With the continued development of unmanned aerial vehicle (UAV) technologies, the UAV on-board automation is increasingly more capable of performing tasks formerly done by human operators. Thereby, the role of UAVs is changing from being mere tools to become members of integrated manned-unmanned systems. However, the high automation necessary to achieve this cooperation, introduces a new set of negative effects for the human operator such as complacency or automation bias. Adaptive assistance is one approach to counteract these negative effects seen in human-automation-interaction. To enable adaptive assistance, we present a cognitive state estimation framework for a MUM-T aircraft application. The goal of this approach is to assess attention allocation and SA of a pilot in real-time and identify possible breakdowns in the situational picture that could cause performance decrements and errors. The design of a MUM-T cockpit simulator is presented to describe how this cognitive state estimation framework is integrated into a human-autonomy-teaming environment. The results of initial simulator experiments are presented and areas of further research are identified. |
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| Keywords: | Attention Situation awareness assessment Eye-tracking Adaptive assistance Human-autonomy-teaming |
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