Multi-vehicle dynamic interaction in autonomous driving: integrating game theory and the potential field method

Abstract

Lane-changing behaviour is more complex and riskier than lane-keeping, posing challenges for autonomous vehicle decision-making. To address this issue, this study modelled the decision-making process of lane-changing behaviour by combining Stackelberg game theory with the potential field method. The potential field method analyzes dynamic interactions between multiple vehicles, while game theory quantifies the efficiency and safety benefits of lane-changing. Subsequently, a decision-making model for multi-vehicle interaction was established by integrating game theory and the potential field method. Finally, the lane-changing trajectory was optimised for maximum driving benefit. A simulation of lane-changing behaviour was used to validate the performance of the proposed model. The results indicated that the proposed method leads to a higher average velocity (20.07 m/s), faster lane changing (3.14 s) and lower computational costs (0.075 s) than the rule-based method (19.21 m/s, 4.04 and 0.17 s, respectively). This finding can provide a reference for reasonable decision-making for autonomous vehicles.