Optimizing Traffic Flow with Autonomous Vehicles

This chapter explores the application of deep reinforcement learning in traffic management, particularly addressing bottlenecks like the San Francisco-Oakland Bay Bridge. It evaluates the effectiveness of decentralized versus centralized control systems and highlights the potential of autonomous vehicles in optimizing traffic flow through cooperative behaviors. The discussion focuses on the challenges of integration and communication among vehicles, illustrating how these technologies can transform current traffic management practices.