Hierarchical control for Connected and Automated Vehicles: From safe vehicle navigation to network congestion alleviation

Connected and automated vehicles (CAVs) have the potential to revolutionize traffic optimization and significant research efforts are dedicated to ensuring these advancements enhance safety and reduce costs. The capabilities introduced by CAVs are unprecedented and are expected to play a crucial role in coordinating CAV trajectories, promoting cooperation, and improving overall urban network performance. Some new measures are needed, e.g. reducing the amount of exchanged data to simplify protection policies and reduce communication link overload. Despite advancements in computational power within CAVs, it is essential to ensure new algorithms can operate in real-time. This thesis proposes a novel hierarchical framework for managing urban traffic and delves into its foundational components addressing the above-mentioned issues. The focus is devoted to ensuring real-time collision avoidance, optimal time allocation and coordination at intersections, and congestion alleviation at network level. The findings aim to advance our understanding and implementation of CAV systems within urban environments for a safer, smoother, and more sustainable urban future.