Autonomous or self-driving vehicles represent one of the most transformative applications of artificial intelligence in the modern transportation sector. By combining computer vision, deep learning, sensor fusion, and real-time decision-making algorithms, self-driving cars aim to reduce human error, improve road safety, ease traffic congestion, and provide mobility solutions for people who are unable to drive. This paper presents a comprehensive review of the artificial intelligence techniques that enable autonomous driving, including perception, localization, path planning, and control. The architecture of a typical autonomous vehicle is described layer by layer, followed by a discussion of the levels of driving automation defined by the Society of Automotive Engineers (SAE). Industry approaches adopted by prominent developers of autonomous driving technology are summarized, and the technical, ethical, legal, and cybersecurity challenges that continue to limit large-scale deployment are examined..