Abstract
Goal-oriented human-machine situation-awareness systems focus on the challenges related to perception of the elements of an environment and their state, within a time-space window, the comprehension of their meaning and the estimation of their state in the future. Present computer-supported situation awareness systems provide real-time information fusion from different sources, basic data analysis and recognition, and presentation of the corresponding data using some augmented reality principles. However, a still open research challenge is to develop advanced supervisory systems, platforms and frameworks that support higher-level cognitive activities, integrate domain specific associated knowledge, learning capabilities and decision support. To address these challenges, a novel cognitive architecture framework is presented in this paper, which emphasizes the role of the Associated Reality as a new cognitive layer to improve the perception, understanding and prediction of the corresponding cognitive agent. As a proof of concept, a particular application for railways safety is shown, which uses data fusion and a semantic video infrastructure.
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This work was funded by the Spanish Ministry of Economy and Competitiveness under grant number TIN2014-57458-R.
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Fernandez, F., Sanchez, A., Velez, J.F., Moreno, B. (2017). A Cognitive Architecture Framework for Critical Situation Awareness Systems. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_6
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