AI-Driven Energy Solutions and Optimization for Autonomous Vehicles in 5G-Enabled Consumer and Edge Networks

This article presents an innovative approach to energy optimization in autonomous vehicles utilizing AI, 5G, and edge computing technologies. The primary objective is to enhance energy efficiency and system reliability through the integration of Autoencoders for data dimensionality reduction and Simulated Annealing (SA) for optimization. A comprehensive experimental design was employed, involving data collection from multiple sources, preprocessing, and feature extraction from the Autoencoder’s latent space representation. Results demonstrate a 12.5% improvement in average speed (from 40 km/h to 45 km/h) and an increase in average acceleration from 2 m/s2 to 3 m/s2. Additionally, system downtime was significantly reduced from 10% to 5%, and error rates decreased from 8% to 3%. The findings indicate that the proposed AI-driven framework effectively optimizes energy consumption in autonomous systems. The study concludes with recommendations for further research into real-world applications and the scalability of the approach across various vehicular environments, underscoring the transformative potential of integrating advanced technologies for sustainable transportation solutions.