Prediction of Power to Autonomous Vehicles using Machine Learning techniques

The integration of machine learning (ML) techniques has catalyzed significant advancements in the realm of autonomous vehicle technology, particularly in the domain of Intelligent Transport Systems (ITS) and the evolution of Connected and Automated Vehicles (CAVs). This study focuses on a downlink communication network characterized by a single-antenna Base Transceiver Station (BTS) and autonomous vehicles, with the BTS transmitting information at varying power levels. The primary objective is to predict optimal transmit power for vehicles across diverse channel conditions using machine learning methodologies, aimed at mitigating interference within the system. This interdisciplinary research endeavors to optimize transmit power from the BTS to vehicles through the synergy of machine learning and optimization techniques. By addressing this imperative, we aim to enhance vehicle safety, efficiency, and reliability within modern transportation networks. Leveraging advanced ML models, including Long Short-Term Memory (LSTM) and Feedforward Neural Network (FNN), our investigation reveals promising insights into the efficacy of these algorithms in advancing autonomous driving technologies. The paper presents comparative analyses of two prominent machine learning models, with the Mean Square Error (MSE) computed at 17.2516 for LSTM and 13.8562 for the Feedforward Model. These results underscore the potential of ML-driven approaches in optimizing transmit power for autonomous vehicle communication networks, thereby contributing to the ongoing evolution of intelligent transportation systems.