The integration of edge computing with autonomous vehicles (AVs) has emerged as a pivotal technological advancement to address the computational, latency, and data privacy challenges inherent in autonomous navigation. This review examines the critical need for data offloading in AV systems, providing an in-depth analysis of different data offloading strategies, including full, partial, and adaptive offloading. We explore various types of edge computing nodes, ranging from onboard embedded systems to roadside and cloudlet-based architectures, highlighting their roles and trade-offs in AV ecosystems. Furthermore, the paper compares multiple Radio Access Technologies (RATs), such as 5G, mm Wave, Wi-Fi, and DSRC, emphasizing their effectiveness in supporting low-latency and high-bandwidth data offloading for various applications. The ongoing innovations in docker and containerization that has revolutionized edge computing for AVs by enabling lightweight, portable, and scalable deployment of services, reducing dependency on specific hardware platforms and improving the overall flexibility and efficiency of the system have also been discussed. A comparative analysis of different edge computing architectures is presented, focusing on centralized, decentralized, and fog computing models tailored for AVs. The review also addresses key challenges in the deployment and scaling of edge computing for AVs, including network reliability, data management, interoperability, and security concerns. Recent trends and future directions are also highlighted. This paper aims to provide a comprehensive end-to-end understanding of the role of edge computing in autonomous vehicles, offering a roadmap for future research and development in this dynamic domain.