Abstract
The integration of geolocation, big data and cognitive agents has become one of the most boosting business tools of the digital era. By definition, geolocation represents the use of different technologies in a variety of applications to help locate humans and objects. To really achieve smart services, companies also require accessing huge volumes of related information to draw meaningful conclusions. With big data, it is possible to establish connections between a wide range of associated information, and use it to improve available services or create new ones. Today, the influence of geolocation, cloud data science and involved cognitive agents impacts many application fields, which include: safety and security, marketing, beacon technology, geofencing, location-sensitive services, transportation and logistics, healthcare, urban governance, intelligent buildings and smart cities, intelligent transport systems, advanced driver assistance systems, and autonomous and semi-autonomous vehicles. To address these challenges, this paper presents a general associative-cognitive architecture framework to develop goal-oriented hybrid human-machine situation-awareness systems focused on the perception and comprehension of the elements of an environment and the estimation of their future state for decision-making activities. The architecture framework presented emphasizes the role of the associated reality as a novel cognitive agent and the involved semantic structures, to improve the capabilities of the corresponding system, processes and services. As a proof of concept, a particular situation awareness agent for geolocation of vehicles in tunnels is shown, that uses cloud data association, vision-based detection of traffic signs and landmarks, and semantic roadmaps.
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This work has been supported by the Spanish Ministry of Economy y Competitiveness under project number TIN2014-57458-R.
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Fernández, F., Sánchez, Á., Suárez, A., Vélez, J.F. (2018). Situation Awareness Cognitive Agent for Vehicle Geolocation in Tunnels. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_35
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