Artificial Intelligence (AI) plays an integral role in the progression of self-driving vehicles on public roads. Each autonomous vehicle is outfitted with advanced tools to gather information, including long-range radar, LIDAR, cameras, short/medium-range radar, and ultrasound. Each of these technologies is used in different capacities, and each collects different information. However, this information is useless unless it is processed and some form of action is taken based on the gathered information. This is where AI comes into play and can be compared to the human brain, and the actual goal of AI is for a self-driving car to conduct in-depth learning. At its core, AI is a complex algorithm that mimics how the human brain learns. Instead of hard-coding an autonomous car with thousands of "If-Then" statements, software engineers create an algorithm that outlines to the car's onboard computers various examples of what is right, wrong, safe, and unsafe for the car to perform. The real power of this approach is realized because autonomous cars have one advantage that human drivers don't have; self-driving cars have the ability to share their experiences and readings with other cars instantaneously. This type of shared experience and active learning creates a situation where autonomous cars, through AI algorithms, can improve their ability to react to situations on the road without actually having to experience those situations first-hand. This means that not only does the future of autonomous cars depend on advanced AI algorithms, self-driving cars also rely on the standardization of that algorithm across all autonomous cars. Without this shared technology, we can't expect our society or policy makers to accept autonomous cars on public roads on a wide-scale.