Unmanned Aerial Vehicle (UAV) operators must maintain high levels of situation awareness on their area of operation. To achieve this, they use the a Command and Control (C2) map, which is shared among forces, and regularly overloaded with data that is irrelevant to their operational mission. Operators require distilled information at the right timing. Yet, the existing filtering mechanisms for C2 maps are layer-based and insufficient. We propose a new approach to automatically and dynamically filter information items on the map based on environmental and mission context. To achieve this, we introduce a three-tiers artificial intelligence (AI) based algorithm (GiCo-MAF), where we delineate the use of machine learning (ML) models to support UAV missions. For the GiCoMAF development, tagged data was collected in simulated experimental runs with professional UAS operators. Different types of ML models were evaluated and fitted into the algorithm. The models achieved a relatively high accuracy at modeling human preference and area of interest. The approach presented in this study can be further implemented to support time-critical spatial-temporal operational problems.
An Artificial Intelligence Algorithm to Automate Situation Management for Operators of Unmanned Aerial Vehicles
01.08.2020
10102946 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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