Utilizing Multi-Agent Deep Reinforcement Learning for Autonomous Intersection Management Systems: A Promising Approach

In modern cities, intersections are vital pieces of road infrastructure, but they also have the potential to snarl traffic owing to accidents or a lack of traffic coordination systems like traffic signals. As traffic eco-systems become more connected and autonomous, researchers have been increasingly interested in the concept of autonomous intersection management (AIM). AIM improves traffic flow by coordinating the motion of connected vehicles through intersections. This study proposes an AIM system consisting of a two-layer hierarchal control architecture consisting of a vehicle controller layer utilizing Model Predictive Control (MPC) that is tuned to maintain the designated trajectory for each vehicle while keeping the passengers comfortable by delivering a smooth acceleration profile. And an Intersection Manager layer utilizing Multi-Agent Reinforcement Learning using centralized training decentralized execution scheme to find the optimal strategy to prevent collisions between vehicles at an intersection by assigning a trajectory for each vehicle entering the intersection. A longitudinal vehicle dynamic model was used to mimic the motion of vehicles in response to the controller’s commands. The proposed AIM system noticeably improved throughput and boosted efficiency without sacrificing safety or passenger comfort.