## 1 Introduction  
Despite significant advances in transportation infrastructure engineering and stricter traffic laws enforcement, the road safety problem remains unsolved. Poor visibility and slow driver reaction time are major contributing factors to the increase in traffic accidents (TA), especially for accidents involving vehicles and pedestrians or cyclists [1]. The intelligent transportation system (ITS) is a promising method to increase situational awareness of the driver or an autonomous vehicle, enabling highway infrastructure objects and road users to exchange heterogeneous information with each other [2]. It uses various machine learning algorithms to assess the probability of potential TA. ITS implementation can be considered in several variations: within the systems of an ego car with only its sensor data and within the network of road users, where the vision of the traffic conditions is based on various sensor sets. Electronic stability control (ESC) is one of the conventional ITS applications [3] related to the concept of the ego-car system. Efficiency analysis of ESC in preventing accidents from 2011 to 2016 in the USA showed that single-vehicle crashes and head-on collisions related to loss of control reduced by about 40% and 13%, respectively [4; 5], which illustrates the benefits of adaptation techniques for vehicle behavior based on situational road awareness. However, the functionality of these systems is limited to information from the vehicle itself. It does not take into account the peculiarities of the transport infrastructure and the behavior of other road users. In turn, the second type represents a comprehensive solution that provides interconnections between users in the road environment [6]. Such a unified technology of communication between vehicles and multiple objects (vehicle-to-everything, V2X) [7] includes various interaction scenarios between vehicles (vehicle-to-vehicle, V2V), vehicles and infrastructure (vehicle-to-infrastructure, V2I), and vehicles and pedestrians (vehicle-to-device, V2D, vehicle-to-pedestrian, V2P). Using such ITS can influence a significant number of factors, both in particular and in terms of effects on an overall increase in road safety. Gigabit data rates and ultra-low latency are critical requirements of ITS applications [8], which is especially important for the second type of ITS with a large number of information sources. Unmanned vehicles with built-in radar and lidar are predicted to generate more than a gigabit per second of sensor data [9]. In this regard, such a system has two implementation challenges: the collection, systematization, and storing of information and establishing required delays and throughput capacity. The first problem could be solved using smart objects of infrastructure [10; 11]. Within the V2I system, the infrastructure object can act as an informer of a priori information about the road section within its service area and act as an intermediary for data exchange between remote vehicles without connection through the V2V system. To solve the second problem, related to establishing and maintaining a high-speed channel, a significant part of research conducted is devoted to the beamsteering of the phased antenna arrays (PAA) technology [12; 13] by concentrating the maximum radiated power in a limited area where the vehicle should be located. The 3D beamsteering concept, where the radio beam is controlled both in the vertical and horizontal planes, enables the transmission of high-speed traffic [14]. However, for V2I systems, the range of which is at most 600 m [15], the arrangement of antennas for intelligent infrastructure objects occurs on road signs or road arch structures with appropriate suspension height standards. Consequently, the degrees of freedom for controlling the beam in the vertical plane become significantly less, which makes it possible to simplify the task to 2D beamsteering.