Autonomous vehicle technology is transforming the transportation sector with improvements in perception, control, and motion planning. This paper discusses the hardware and software components required for autonomous driving, such as sensor fusion, artificial intelligence-based interpretation, and real-time control systems. The main topics are state estimation with Kalman filters, motion planning with predictive control models, and visual perception methods like object detection, semantic segmentation, and lane detection. The research also touches on vehicle localization challenges, trajectory planning, and decision-making under dynamic scenarios. Through the application of solid methodologies and using state-of-the-art algorithms, the research targets increased safety, reliability, and efficiency in self-driving systems. The research helps expand the wider area of robotics and artificial intelligence and opens up new avenues for more autonomous mobility solutions.