Driving policies of V2X autonomous vehicles based on reinforcement learning methods

Abstract

Autonomous driving has been achieving great progress since last several years. However, the autonomous vehicles always ignore the important traffic information on the road because of the uncertainties of driving environment and the limitations of onboard sensors. This might cause serious safety problem in autonomous driving. This study argues that the connected vehicles could share much more environmental information with each other. Therefore, a decision-making method based on reinforcement learning is proposed for V2X autonomous vehicles. First, the V2X autonomous driving architecture with three subsystems is designed. By V2V communication, an autonomous vehicle could obtain much more environmental information. Second, a reinforcement learning based model is applied to learn from the V2V observation data. A simulation environment is setup based on OpenAI reinforcement learning framework. The experimental results demonstrate the effectiveness of the V2X in autonomous driving.