In intelligent transportation systems, an important task is to provide a highly efficient communication channel between vehicles and other infrastructure objects that meets energy efficiency requirements and involves low time delays. The paper presents a method for generating synthetic data of the “vehicle-to-infrastructure” system, capable of simulating many scenarios of traffic situations to increase the generalizing ability of an intelligent beamsteering algorithm. The beamsteering algorithm is based on gradient boosting and is designed to connect and track vehicles with minimal delays without relying on GNSS coordinates. The predictors for the applied machine learning algorithm were: the relief, vehicle type, direction of movement and speed, timestamps, and the received signal power level. The generated dataset included the traffic model based on the Lighthill–Whitham–Richards macroscopic model and SUMO software package simulations. Simulation results showed 94% accuracy in correctly identified positions for the main lobe according to vehicle behavior.

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