Decision‐Making and Planning Methods for Autonomous Vehicles Based on Multistate Estimations and Game Theory

Abstract

A core issue inherent to decision‐making and path‐planning tasks is managing the uncertainties in the motion of dynamic obstacles. Therefore, this article proposes a new decision‐making and path‐planning framework, based on game theory, that considers the multistate future actions of surrounding vehicles. First, multistate future actions of neighboring vehicles, whose driving styles vary, are estimated and fed into a decision‐making module for risk assessment. Then, based on the Stackelberg game theory, the ego vehicle and the rear object vehicle are modeled as two players in the game, and their optimal decisions are obtained. In addition, the path‐planning model incorporates a potential‐field model that utilizes several potential functions to explain the varied styles and physical limitations of the surrounding vehicles. Finally, the trajectory of the ego vehicle is obtained through model predictive control that is based on the outputs of the decision‐making and constructed potential‐field models. The results of simulation experiments that used designed scenarios demonstrate that the proposed method effectively manages various social interactions and generates safe and appropriate trajectories for autonomous vehicles. In addition, the simulation results demonstrate that considering multistate trajectories caused the decision‐making and path‐planning modules to be appropriate for unpredictable environmental conditions.

Keywords

• autonomous vehicles
• model predictive control
• multistate estimations
• Stackelberg game theory