Behavior analysis of emergent rule discovery for cooperative automated driving using deep reinforcement learning

With the improvements in AI technology and sensor performance, research on automated driving has become increasingly popular. However, most studies are based on human driving styles. In this study, we consider an environment in which only autonomous vehicles are present. In such an environment, it is essential to develop an appropriate control method that actively utilizes the characteristics of autonomous vehicles, such as dense information exchange and highly accurate vehicle control. To address this issue, we investigated the emergence of automatic driving rules using reinforcement learning based on information from surrounding vehicles using inter-vehicle communication. We evaluated whether reinforcement learning converges in a situation where distance sensor information can be shared in real-time using vehicle-to-vehicle communication and whether reinforcement learning can learn a rational driving method. The simulation results show a positive trend in the cumulative rewards value, and it indicates that the proposed multi-agent learning method with an extended own-vehicle environment has the potential to learn automated vehicle control with cooperative behavior automatically. Furthermore, we analyzed whether a rational driving method (action selection) can be learned by reinforcement learning. The simulation results showed that reinforcement learning achieves rational control of the overtaking behavior.