Intersections are some of the most dangerous and congested parts of roadways, despite making up only a small portion of the entire transportation network. As a result of recent breakthroughs in sensors, vehicle communication, and small but powerful computers, society is moving toward a future in which traffic can be managed by using connected and automated vehicles (CAVs). The central goal of this project is to replace traditional traffic controls, such as stoplights and signs, with computer systems that help vehicles move more safely and smoothly through intersections — reducing accidents, shortening delays, and lowering fuel use and emissions. At the center of this paradigm is a system called the Autonomous Intersection Manager (AIM). AIM acts like the brain of a smart intersection, constantly analyzing incoming vehicle data and deciding how each vehicle should move to avoid collisions and to keep traffic flowing. To do this effectively, AIM needs fast and reliable decision-making tools that can work with complex and changing traffic patterns. This project develops new computer algorithms that allow AIM to make these decisions quickly, even when facing limited time and computing resources. The work combines ideas from computing, scheduling theory, and traffic control to create transportation technologies that are safe, efficient, and practical for real-world use. In parallel with its technical contributions, the project fosters educational and societal impact by engaging st