# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
## 7 Conclusions  
In this article, we proposed and analyzed the concept of situational awareness as applied to the problem of behavior control for highly autonomous UAVs. We have structured situational awareness for this class of aircraft, and analyzed its levels and types. We have demonstrated the specifics of situational awareness formation for HA-UAVs and analyzed its differences compared to situational awareness for manned aircraft and remotely piloted HA-UAVs. We showed the importance of using machine learning methods and tools to forming elements of situational awareness in HA-UAV behavior control tasks. We have also considered ways of developing situational awareness research to take into account the specifics of HA-UAVs.  
As examples, we highlighted and discussed the two essential elements of situational awareness for HA-UAVs in more detail. The first one is related to the analysis and prediction of the behavior of various objects in the environment in which the HA-UAV operates. The solution to this problem allows us to track objects in the vicinity of the HA-UAV, whose behavior should be taken into account by the control system of the HA-UAV. The problem is solved by machine learning using a recurrent neural network, which contains a combination of LSTM blocks with a fully connected layer.  
The second problem is related to the formation of visual components of situational awareness. The problem of semantic segmentation of scenes coming from video surveillance tools is solved. The approach to the solution for this problem was illustrated by the example of the identification of objects on the ground surface when selecting a landing site for a HA-UAV in unfamiliar terrain.  
The task of semantic segmentation, in this case, was solved using convolutional neural networks. Architectures Unet, PSPNet, and DeepLabV3 were considered. A comparative analysis of these architectures in terms of solving the problem of selecting a suitable landing site in an urban environment was performed.  
To improve the accuracy of the relatively lightweight semantic segmentation network based on the PSPNet-MobileNetV2 architecture, the knowledge distillation approach was applied based on the teacher's network responses. Thanks to this approach, it was possible to bring the student's network model closer to the teacher's model in terms of the accuracy of the work.  
The essential requirement for the tools that form the considered part of situational awareness is their cost-effectiveness. That is, they should solve the problem with the least possible consumption of computing resources. One possible approach to meet these requirements is the use of the neural architecture search (NAS). This approach is aimed at selecting the optimal neural network architecture for solving perception problems, and its development represents a direction of further research development in the field under consideration.  
Conceptualization, Y.V.T.; methodology, Y.V.T.; software, D.M.I. and P.A.K.; validation, P.A.K., D.M.I. and Y.V.T.; formal analysis, P.A.K. and Y.V.T.; investigation, Y.V.T. and P.A.K.; resources, P.A.K.; data curation, P.A.K.; writing--original draft preparation, P.A.K.; writing--review and editing, Y.V.T.; visualization, P.A.K.; supervision, Y.V.T.; project administration, Y.V.T.; funding acquisition, Y.V.T. All authors have read and agreed to the published version of the manuscript.  
The publication has been prepared within the framework of the Program of foundation and development of world-class research center \"Supersonic\" for 2020-2025 with the financial support of the Ministry of Education and Science of Russian Federation (Agreement of 16 November 2020, number 075-15-2020-924).  
Not applicable.  
The authors declare no conflict of interest.