Unmanned Aerial Vehicles (UAV) operators must maintain high levels of situation awareness on their area of operation. To achieve this, they use the Command and control (C2) map, which are shared among forces, and is regularly overloaded with data that is irrelevant to their mission. UAV operators’ missions require distilled information at the right timing. Yet, the existing filtering mechanisms of 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-tier artificial intelligence (AI)-based algorithm (GiCoMAF), 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 other operators in time-critical spatial-temporal problems.
|Journal||CEUR Workshop Proceedings|
|State||Published - 1 Jan 2020|
|Event||2020 AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice, AAAI-MAKE 2020 - Palo Alto, United States|
Duration: 23 Mar 2020 → 25 Mar 2020
ASJC Scopus subject areas
- Computer Science (all)