Universal Learning Approach of an Intelligent Algorithm for Non-GNSS Assisted Beamsteering in V2I Systems

Abstract

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.

A Survey on Video Streaming for Next-Generation Vehicular Networks

“…The authors suggested a systematic approach to structure and tailor the IoT protocol stack, making it easier to identify areas for adjustment and optimization. In the work by Lopukhova et al [24], a smart beam-steering algorithm was presented, leveraging vehicle positioning data. This algorithm enhances the generalization capability of the employed machine learning (ML) algorithm, mitigating the impact of the received signal power parameters on system performance and bolstering the system's resilience against multipath propagation.…”

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

“…SUMO offers capabilities for constructing, adjusting, and modeling custom traffic flow architectures and supporting tools for importing network and ODMs of existing route sections. SUMO's versatility enables the study of traffic control strategies and vehicular communication systems [27]. The high-detail simulation in SUMO is essential for analyzing and optimizing traffic management strategies, evaluating the impact of infrastructure modifications, and examining vehicle interactions in complex urban environments [28].…”

“…It required us to develop an approach to reducing this discrepancy in datasets, which is the topic of this paper. In our previous work [27], we showcased an application of ML in ITSs focusing on specific traffic flow data, based on which the location of the "connected car" was determined to generate a dedicated radio channel, tuning the phased antenna array radiation pattern. The approach involved generating synthetic data to train the ML algorithm within the V2I system, leveraging the SUMO simulation model and the Longley-Rice propagation model [87].…”

“…In the context of the previous work [27], the selected parameters for addressing the vehicle tracking issue were sensor data that reflect the internal attributes of vehicles while they are in motion. Since these vehicle sensor parameters exhibit a spatial and temporal distribution that aligns with the traffic ODM, analyzing these data offers valuable preliminary insights into the modeled area.…”

“…During the results analysis [27], a deviation in the ODM was specifically noted, resulting in fluctuations in the frequency of vehicle presence along different segments of the route overall and over specific time intervals. We divided the simulated area into sectors using the Geohash method [96] of GNSS coordinate space, encoding with a code sequence length of 8.…”