Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios

This paper presents a hierarchical and game-theoretic decision-making strategy for connected and automated vehicles (CAVs). A CAV can receive preview information using vehicle-to-everything (V2X) communication systems, and the optimal short- and long-term trajectory can be planned using this information. Specifically, in this study, the aggressiveness of all preceding vehicles in the car-following scenario can be estimated globally by monitoring the history of their time-series behaviors, before the CAV initiates a particular action, which is performed at the upper layer of the proposed decision-making structure. If it is determined that initiating a specific action is advantageous, the action is initiated, and the CAV then interacts with the vehicles locally to achieve its driving goal in a game-theoretical manner at the lower layer. In multiple test scenarios, we demonstrate the usefulness of our approach compared to the conventional decision-making approaches, and it shows a significant improvement in terms of success rates.