Cooperative Adaptive Cruise Control Based on Intent Sharing Messages and Reinforcement Learning

This paper investigates the effectiveness of incorpo-rating intent sharing messages into Cooperative Adaptive Cruise Control (CACC) systems. In contrast to traditional information exchange limited to current state information, intent sharing involves providing details about the future trajectory of connected vehicles. This work employs a concise representation of the intent of a connected vehicle, encompassing anticipated speed and acceleration bounds. The proposed approach, referred to as intent sharing-based CACC (I-CACC), utilizes a reinforcement learning-based controller that leverages this additional intent information. Through an extensive simulation study using experimental datasets, we compare the performance of I-CACC to conventional CACC. The results reveal the superior performance of I-CACC across various metrics, encompassing safety, comfort, string stability, and gap-keeping.