## 3 The Learning Approach of the Intelligent Algorithm
### Dataset Generation and Simulation of the Signal Propagation Environment  
The positioning of vehicles and their classification by sub-sector is based on the data included in the request message. The intelligent algorithm is pre-trained before being put into operation using a model of the V2I system for a selected service area where the smart infrastructure object will be located. The ML algorithm executed on the V2I transceiver's controller, taking into account a priori data, reveals the relationship between the vehicle sensors' data and the received signal parameters and determines the road user location within the scanning sector. The ML algorithm must be trained on that environment model before being implemented in real-life conditions. If the road structure changes or new buildings arise in the service area, it is possible to introduce additional training for the algorithm based on new data from the vehicle. During training, the algorithm acted as an agent, providing the environment with location and vehicle-type data. As the response  
Figure 3: The RP of the PAA for generated binary sequences controlling phase at the emitters inputs.  
from the model, it received a matrix of terrain points under the vehicle and the signal power at the V2I control node transceiver.  
During the beamsteering system deployment at the V2I control node, the proposed ML algorithm is retrained on the data received from the entities in the service area. In this regard, switching the system's location is accompanied by retraining on the generated dataset for each new site of the V2I transmitter. The information contained in the request message includes vehicle type and speed (accelerometer data), gyroscopic sensor data (determines the relief features), timestamps and the signal level (determined by the distance to the user and the path profile). A priori data includes the coordinates of the V2I control node transceiver, a three-dimensional map of the service area, and road features in the service area (e.g., speed limit, route shape). These data were taken into account to design a model of the signal propagation environment in the service area. The simulated V2I system receives sensor parameters and the vehicle's location based on the dynamic SUMO traffic model with selected frequency. The one proposed in [44] was chosen as the ML algorithm. At the same time, the data generation method proposed in the approach makes it possible to eliminate the disadvantage of the algorithm, which was the predominance of the received signal power for dividing the input space into disjunctive sub-sectors. This shortcoming negatively affected the efficiency of the system in conditions of multipath propagation. Including more information about the traffic stream and the road user with different behavior in the dataset would reduce the importance of this parameter and increase reliability.  
An example of the dataset content is shown in the Table 2.  
To simulate the signal propagation within the service area of the V2I system, we applied the Longley-Rice irregular terrain model. It provides sufficient accuracy in predicting radio signal attenuation [48]. Multipath propagation was included in the simulation model based on the recommendations [49; 50] for recalculating the signal level at the receiver of the smart infrastructure object and the vehicle. Moreover, it was upgraded with a program block considering the algorithm impact on the signal level at the vehicle's receiver (comparing it with the target, when the maximum of radiation is precisely aimed at the user).