Acquisition of Cooperative Control of Multiple Vehicles Through Reinforcement Learning Utilizing Vehicle-to-Vehicle Communication and Map Information

Abstract

In recent years, extensive research has been conducted on the practical applications of autonomous driving. Much of this research relies on existing road infrastructure and aims to replace and automate human drivers. Concurrently, studies on zero-based control optimization focus on the effective use of road resources without assuming the presence of car lanes. These studies often overlook the physical constraints of vehicles in their control optimization based on reinforcement learning, leading to the learning of unrealistic control behaviors while simplifying the implementation of ranging sensors and vehicle-to-vehicle communication. Additionally, these studies do not use map information, which is widely employed in autonomous driving research. To address these issues, we constructed a simulation environment that incorporates physics simulations, realistically implements ranging sensors and vehicle-to-vehicle communication, and actively employs map information. Using this environment, we evaluated the effect of vehicle-to-vehicle communication and map information on vehicle control learning. Our experimental results show that vehicle-to-vehicle communication reduces collisions, while the use of map information improves the average vehicle speed and reduces the average lap time.