Stochastic Hierarchical Game (SHG) for Multi‐Agent Autonomous Driving With Uncertain Driver Intentions

ABSTRACT Safe and efficient decision‐making is crucial for autonomous vehicles (AVs) to operate in complex traffic scenarios. The uncertain intentions of surrounding agents (e.g., AVs, human‐driven vehicles, and pedestrians) and their complex interactions pose significant challenges to the decision‐making process. In this work, we propose a novel stochastic hierarchical game (SHG) framework to address the challenges of decision‐making under uncertain intentions of agents in complex multi‐vehicle settings. The hierarchical game approach addresses the scalability of multi‐agent interactions. In the stochastic game framework, the uncertain intentions of traffic agents are captured using random mobility models (RMMs), the parameters of which can be estimated using data‐driven methods. We propose a novel decision‐making method for the ego vehicle based on the expected optimal actions under uncertain intentions. To solve the SHG efficiently, an uncertainty quantification method called the multivariate probabilistic collocation method with an orthogonal fractional factorial design (MPCM‐OFFD) is deployed. Comparative simulation studies are conducted to verify the effectiveness of the proposed framework.