Real-Time Intelligent Autonomous Intersection Management Using Reinforcement Learning

Autonomous intersection management has the ability to reduce congestion at intersections significantly, compared to classical traffic signal control in the era of connected autonomous vehicles. Autonomous intersection management requires time and speed adjustment for vehicles arriving at an intersection for collision-free passing through the intersection. Due to its computational complexity, this problem has been studied only when vehicle arrival times towards the vicinity of the intersection are known beforehand or with other simplifying scenarios which limits the applicability of these solutions for real-time settings. To solve the real-time autonomous traffic intersection management problem, we propose a reinforcement learning (RL) based multiagent architecture and a novel RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we introduce a simple yet effective way to solve a Markov Decision Process by preserving both short-term and long-term goals, which is crucial for collision-free speed control. Our experimental results using microscopic simulations show that our RL-based multiagent solution can achieve near-optimal performance efficiently when minimizing the travel time through an intersection.