A cognitive architecture framework for critical situation awareness systems

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.