Game Theory-Based Lane Change Decision-Making Considering Vehicle’s Social Value Orientation

Abstract

<div class="section abstract"><div class="htmlview paragraph">Decision-making of lane-change for autonomous vehicles faces challenges due to the behavioral differences among human drivers in dynamic traffic environments. To enhance the performances of autonomous vehicles, this paper proposes a game theoretic decision-making method that considers the diverse Social Value Orientations (SVO) of drivers. To begin with, trajectory features are extracted from the NGSIM dataset, followed by the application of Inverse Reinforcement Learning (IRL) to determine the reward preferences exhibited by drivers with distinct Social Value Orientation (SVO) during their decision-making process. Subsequently, a reward function is formulated, considering the factors of safety, efficiency, and comfort. To tackle the challenges associated with interaction, a Stackelberg game model is employed. Finally, the effectiveness of this approach is validated in diverse testing scenarios involving obstacle vehicles characterized by different SVO types, namely Altruistic, Prosocial, Egoistic, and Competitive. The simulation results indicate that this approach can address behavioral differences introduced by different drivers in lane change interactions and making more safe and efficient driving decisions at appropriate times.</div></div>