# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
###### Abstract
Tasks similar in nature are also considered in [39; 40; 42; 43], where possible means of forming situational awareness elements concerning various objects on the Earth's surface are shown. Several studies devoted to forming situational awareness for autonomous UAVs demonstrate the high efficiency of machine learning technologies for solving this problem. In particular, the article [35] considers a quadcopter designed to perform search and rescue operations. Among the tasks solved by its onboard equipment is the formation of situational awareness, based on visual data processing to assess the surrounding environment. In addition, the building of the situational awareness element, which evaluates the UAV's state based on the data from different sensors (multi-sensor fusion), is considered. The capabilities of deep learning technologies and reinforcement learning to solve the problems arising in situational awareness formation for an autonomous UAV are shown. A similar problem is addressed in [49], where the main goal is to detect and locate people and recognize their actions in near real-time. That method could play a crucial role in preparing an effective response during rescue operations. Another task related to rescue operations is discussed in [52]. Although not directly related to UAVs, it provides an example of how situational awareness in a complex emergency can be generated using convolutional neural networks of several types. This approach can be very useful for autonomous UAVs involved in search and rescue operations as well. Deep learning technologies are a powerful tool for forming situational awareness elements, primarily based on the processing of visual data from video surveillance systems. An example of the use of this technology is contained in [53]. Therein, an autonomous UAV is used to address the detection, localization, and classification of elements of the ground environment. In addition to controlling the behavior of individual autonomous UAVs, the task of their group behavior is also essential. Accordingly, the problem of forming situational awareness that provides a solution to this problem also arises. One possible option is considered in [47], which describes a method for autonomous planning of UAV swarm missions. This planning essentially depends on the current situation in which the mission is carried out. As the situation changes, e.g., as the target moves, the planning for swarm behavior is updated. One valuable and effective tool for solving UAV swarm behavior control tasks is multi-agent technology. The article [56] gives an example of situational awareness generation for multi-agent systems. Although this example is not directly related to UAVs, the proposed approach can help solve group behavior problems of autonomous UAVs. The problem of formalizing the concept of situational awareness is considered in [41]. Those authors proposed a statistical model for quantifying situational awareness as applied to the operation of small civilian autonomous UAVs. Our approach is based on other assumptions. Namely, we interpret autonomous UAVs as a class of controlled dynamic systems. It seems exciting and promising to synthesize these two approaches to obtain a formal model of situational awareness suitable for solving a wide range of problems related to the behavior control of autonomous UAVs. The results mentioned above are associated with forming individual elements of situational awareness concerning autonomous UAVs. They do not allow one to consider the problem as a whole, although there are separate works that consider situational awareness for autonomous UAVs from a more general [41; 56] perspective. At the same time, in our opinion, the problem of such complexity as the formation of situational awareness for \"smart\" UAVs cannot be solved without considering this problem as a whole from as general a position as possible.