Enhancing Autonomous Vehicle Navigation Using Deep Reinforcement Learning

Abstract

Autonomous vehicles (AVs) represent a transformative technology poised to revolutionize transportation. Effective navigation in dynamic and unpredictable environments remains a significant challenge. This study explores the application of deep reinforcement learning (DRL) to improve AV navigation, focusing on real-time decision-making and obstacle avoidance. We propose a novel DRL architecture integrating Convolutional Neural Networks (CNNs) for feature extraction with a Deep Q-Network (DQN) for action selection. The proposed system is evaluated in a simulated urban environment, demonstrating substantial improvements in navigation efficiency and safety compared to traditional algorithms. Our findings suggest that DRL can significantly enhance the performance and reliability of AVs, paving the way for safer and more efficient autonomous transportation systems.