Advancing Autonomous Vehicle Intelligence: An Integrated Analysis of Modern Perception and Localization Systems

Abstract

This comprehensive article examines recent advancements in perception and localization technologies for autonomous vehicles, highlighting the transition from conventional GPS-IMU systems to sophisticated multi-modal approaches. This article analyzes the integration of high-definition mapping with sensor fusion architectures, emphasizing their role in achieving precise environmental awareness and robust localization. This article explores how edge computing implementations have revolutionized real-time processing capabilities, enabling more responsive and reliable autonomous navigation. It encompasses machine learning-driven perception systems, focusing on their contribution to object detection, trajecitwe demonstrates how these technological convergences are advancing the industry toward higher levels of autonomy. Drawing from recent developments and industry implementations, this article discusses the remaining technical challenges and potential solutions for achieving fully autonomous transportation systems. This article builds upon previous studies while providing new insights into the practical implications of integrated perception-localization systems for autonomous vehicle deployment.