Concurrent optimization of safety and traffic flow using deep reinforcement learning for autonomous intersection management

Abstract

With increasing connectivity and autonomy in traffic eco-systems, Autonomous Intersection Management (AIM) has attracted strong attention from the research community. AIM helps optimize traffic by coordinating the trajectory of connected vehicles around intersections. Most of the existing AIM solutions are developed for single-objective optimization problems that are focused on improving traffic flow. A complete AIM solution needs to perform bi-objective optimization that considers both traffic flow and safety. However, the computational complexity for achieving both objectives is significantly high with the existing solutions, especially when traffic demand is stochastic. We address the limitations of the existing solutions using deep reinforcement learning (deep RL) that helps solve complex problems efficiently. Our solution uses two types of RL agents. The first type is intersection-level agents, which generate theoretically sound trajectory plans for individual vehicles approaching intersections. The second type is vehicle-level agents that control vehicles' actual trajectories around the intersections based on the plans. Both agents incorporate traffic flow and safety constraints into their decision making. Our experimental results show that our solution achieves a high safety level with a minimum impact on travel time.