Lane-changing decision method based Nash Q-learning with considering the interaction of surrounding vehicles

In order to ensure a safer and more reliable trajectory during the lane change process, the motion decision algorithm needs to predict the possibility of different interaction behaviours with surrounding vehicles and then makes an advantageous decision. For this purpose, a motion decision method of considering the interaction of surrounding vehicles is proposed. Firstly, this study builds the payoff functions to determine the driving revenue of autonomous driving vehicles. Then, an interactive motion prediction method based on game theory is established to predict the interaction behaviours possibility and future local trajectories of surrounding vehicles. Based on this, a motion decision algorithm based on Nash Q-learning for an autonomous driving vehicle is established. With externalising the main behaviours predicted by the interactive game and the greedy optimisation method, the autonomous vehicle can determine the optimal sequence of actions and take into account the interaction of the surrounding vehicles. Finally, the motion decision in this study is validated by MATLAB in the merging lane scene, and compared with the existing rule-based lane change decision algorithm. The results show that the decision method in this study not only has superiority in safety and efficiency but also can effectively predict the interaction of surrounding vehicles.