# Situational Awareness and Problems of Its Formation in the Tasks of UAV Behavior Control
###### Abstract
In all these cases, the problem of situational awareness is interpreted as the need to provide system control processes with information that makes it possible to produce sufficiently effective decisions. In addition, it is necessary to reduce the risk of making incorrect decisions, which is especially important when it comes to the safe operation of the systems involved, e.g., aircraft. As for aircraft, the vast majority of publications on situational awareness for them relate to manned aviation. Only a relatively small number of these publications are concerned with unmanned aerial vehicles [34; 35; 36]. The key point in solving the problem of situational awareness for manned aviation is that the human operator is an active element of the control system, perceiving the current situation and forming control actions based on the results of this perception. It is assumed that the human operator is able to perceive information in various forms. In particular, visual data play an essential role--for example, what the pilot sees through the aircraft canopy, and through the head-up display or with the help of other display devices. A radical difference in the case of autonomous UAVs is that much deeper processing of incoming information is required. Where it is sufficient for a human operator to \"show a picture\" in an autonomous UAV, the tasks of pattern recognition and scene analysis must be solved. At the same time, it is necessary to manage the attention of the perceptual system according to the goals of the flight operation performed. Attention management makes it possible to identify meaningful elements of situational awareness and eliminate insignificant ones. It should be emphasized that this will be only the first stage of the mentioned deep processing of incoming data. Next, it will be necessary to identify the relationships between the scene objects, both static and dynamic, to estimate the evolution of the observed situation, etc. For a highly autonomous UAV, all these actions have to be performed exclusively by the onboard equipment of the UAV, with minimal or no human involvement. As noted above, relatively few works have been devoted to the problem of situational awareness for UAVs compared to manned aviation. Moreover, among these publications, the overwhelming majority are related to UAVs. This is a significant circumstance because, in principle, the situational awareness problem for UAVs differs little from the same problem for human-crewed aircraft. In both of these cases, there is a human operator in the control loop of the aircraft. The only difference is that less information is available to the UAV operator due to the limited bandwidth of the communication channel between the operator and the UAV. However, just like an aircraft pilot, the UAV operator \"sees a picture\"--only now it is not a view through the aircraft canopy, but images from one or more video cameras with which the UAV is equipped. There have been very few papers on situational awareness for highly autonomous UAVs, as opposed to crewed aircraft and remotely piloted UAVs--see [34; 35; 36; 39; 40; 41; 42; 43; 47; 49; 51; 52; 53; 56]. They focus mainly on the formation of individual elements that make up situational awareness. One of the critical tasks that has to be solved when controlling the behavior of an autonomous UAV is the formation of situational awareness of the threats to it. Such problems are solved in [34; 36]. In particular, the article [34] considers the flight of an autonomous UAV over hilly terrain. The data obtained from the radar are used to prevent collision with an obstacle. An appropriate element of situational awareness formed based on these data allows predicting changes in the terrain and performing an evasive maneuver to avoid the identified obstacle. In [36], a similar problem is solved for a broader class of obstacle objects using visual data and based on reasoning about the relationships between the objects of the observed scene.