Application of Machine Learning Algorithms in Improving the Performance of Autonomous Vehicles

Abstract

With the rapid development of intelligent transportation systems, autonomous driving technology relying on machine learning algorithms has received widespread attention. Although autonomous driving technology has made significant improvements, how to utilize advanced machine learning algorithms to further enhance its performance remains a core issue. This study aims to analyze the role of machine learning in enhancing the performance of autonomous vehicles and discuss how algorithms such as deep neural networks and deep reinforcement learning can effectively solve key technical bottlenecks. A series of innovative strategies based on machine learning have been proposed to address the challenges currently faced by technology, such as insufficient sensor perception accuracy, the contradiction between safety and efficiency in route planning, and real-time constraints in decision-making and control. The goal of these strategies is to improve the perception, planning efficiency and operational reliability of the auto drive system.