Intersection Management

Intersection management research aims to optimize traffic flow and safety at intersections, particularly in the context of increasing autonomous vehicles. Current efforts focus on developing real-time control systems using various approaches, including deep learning models (e.g., UNet variations, recurrent neural networks) for perception and prediction, reinforcement learning algorithms for decision-making, and game-theoretic methods (e.g., correlated equilibrium) for multi-agent coordination. These advancements hold significant potential for improving traffic efficiency, reducing congestion, and enhancing safety in both simulated and real-world environments.