The Role of Artificial Intelligence in Enhancing Autonomous Vehicle Navigation Systems

Abstract

Autonomous vehicles (AVs) rely heavily on Artificial Intelligence (AI) to navigate complex and dynamic environments. This study investigates how AI techniques — including deep learning, reinforcement learning, and large language models — improve perception, decision-making, and path planning in AV navigation systems. We analyze the roles of sensor fusion, predictive control, and explainable AI (XAI) in enhancing both safety and efficiency. Through simulation experiments and architectural proposals, we demonstrate trade offs between latency, interpretability, and robustness. Graphical results compare the  performance of traditional vs. AI-driven navigation and evaluate collision avoidance rates under adversarial conditions. We discuss security risks, regulatory implications, and future directions for AI enabled navigation in next-generation AVs.