Towards Autonomous Vehicle Decision-Making in Heterogeneous Traffic: A Virtual Game Approach with Interaction Influence Weight Quantification

Autonomous vehicles (AVs) often display overly conservative behavior in mixed traffic environments with human-driven vehicles (HVs). This is primarily due to the complexity of interactions, diverse conflict points, and the heterogeneity of human driving behaviors. These factors pose significant challenges to AVs' decision-making. To effectively solve the problem, this paper proposes a virtual game-theoretic framework based on the interaction influence weight quantification for multi-vehicle interaction. The upper layer of the framework employs an attention mechanism based on Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) networks to capture the interaction effects between vehicles. The lower layer estimates the Social Value Orientation (SVO) of each interactive vehicle according to the historical trajectories to identify heterogeneity and models the interaction process as a virtual game among vehicles to solve the optimal decisions. Finally, simulation experiments show that the proposed algorithm outperforms existing benchmark decision-making algorithms in terms of safety and efficiency, showing good robustness in interactions with surrounding vehicles of different driving styles.