This document summarizes the current state of the art (SOTA) in autonomous vehicle technology. It discusses the key sensors used like LIDAR, cameras, and radar. Deep learning is now widely used for tasks like object detection, segmentation, and localization. Map-based localization requires large datasets as maps are built up. Simulation is also important for developing and testing systems. The document outlines the differences between technologies needed for higher automation levels like 4-5 versus lower levels for production vehicles today. Long-range autonomous driving in rural areas poses unique challenges around map coverage, unexpected changes, and interacting with wildlife. The presenter is working on applying these techniques to autonomously navigate the remote Pilbara region of Australia.