Improved Intent Sharing for Energy-Efficient Vehicle Platooning

We explore the advantages of using deep learning-based intent sharing for platooning of connected automated vehicles (CAVs). Unlike traditional platooning algorithms that rely on status-sharing - exchanging current position, speed, and acceleration-our approach focuses on intent-sharing, where CAVs share predicted future trajectories with other CAVs. We introduce a deep learning model to generate the intent for each CAV and integrate it into a receding horizon control framework. Our approach aims to minimize spacing errors with the leading vehicle while improving energy efficiency and maintaining string stability. Through microscopic simulations using real-world highway data, we demonstrate that our intent messages significantly enhance energy efficiency compared to conventional status and intent-based platooning algorithms. Moreover, we show that this improvement is particularly pronounced when reducing the frequency of intent message transmission.