Enhancing Autonomous Vehicle Navigation Using Deep Reinforcement Learning

Abstract

This article delves into the utilization of deep reinforcement learning to improve autonomous vehicle navigation. We explore the integration of advanced neural networks to enhance decision-making processes in real-time traffic scenarios. The proposed method significantly reduces the computational cost while maintaining high accuracy levels. Results from simulation tests demonstrate superior performance over traditional methods, particularly in dynamic environments with unpredictable obstacles. This study provides a foundational understanding for implementing AI-driven navigation systems in future autonomous vehicles, ensuring safer and more efficient transportation solutions.
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