Leveraging Machine Learning and Deep Neural Networks for Autonomous Vehicle Navigation in Dynamic Environments

Abstract

The autonomous vehicle (AV) sector has witnessed exponential growth due to advancements in machine learning (ML) and deep neural networks (DNNs). These technologies have the potential to revolutionize vehicle navigation, especially in dynamic and unpredictable environments. This paper explores the application of machine learning and deep learning algorithms in enhancing the performance of autonomous vehicles in real-world scenarios. We discuss the core challenges, including environmental perception, decision-making, and real-time path planning. A detailed methodology incorporating sensor fusion and reinforcement learning for dynamic navigation is presented. The effectiveness of various ML and DNN-based approaches is compared, demonstrating the potential of these technologies in achieving safe and efficient autonomous driving. The paper concludes with an assessment of future directions in AV technology, emphasizing the importance of robust algorithm development and the integration of new sensor technologies.