Real-Time Autonomous Vehicle Automation With 5G-Based Edge Computing and Artificial Intelligence

Autonomous Vehicles (AV) are revolutionizing transportation, but real-time decision-making remains a challenge due to End-To-End Delay (EED introduced by Cloud Computing (CC) based processing. A 5G-enabled Edge Computing Model (5G-EECM) is proposed to address this problem by processing time-sensitive tasks at the network edge, closer to the AV, reducing EED and improving responsiveness. The architecture uses Machine Learning (ML) for Obstacle Detection (OD) and Reinforcement Learning (RL) for navigation, dynamically switching between Edge Computing (EC) EC and CC based on task demands. The study tested the system using a user-friendly AV on a controlled track, revealing increased response times, reduced average EED, reduced energy consumption, and improved OD accuracy. The results demonstrate that 5G-EECM significantly boosts AV systems' real-time safety and efficiency, making it reliable and scalable for next-generation AV systems.