Human-involved interactive environments pose significant challenges for autonomous vehicle decision-making processes due to the complexity and uncertainty of human behavior. It is crucial to develop an explainable and trustworthy decision-making system for autonomous vehicles interacting with pedestrians. Previous studies often used traditional game theory to describe interactions for its interpretability. However, it assumes complete human rationality and unlimited reasoning abilities, which is unrealistic. To solve this limitation and improve model accuracy, this paper proposes a novel framework that integrates the partially observable markov decision process with behavioral game theory to dynamically model AV-pedestrian interactions at the unsignalized intersection. Both the AV and the pedestrian are modeled as dynamic-belief-induced quantal cognitive hierarchy (DB-QCH) models, considering human reasoning limitations and bounded rationality in the decision-making process. In addition, a dynamic belief updating mechanism allows the AV to update its understanding of the opponent's rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that our models effectively simulate vehicle-pedestrian interactions and our proposed AV decision-making approach performs well in safety, efficiency, and smoothness. It closely resembles real-world driving behavior and even achieves more comfortable driving navigation compared to our previous virtual reality experimental data.

翻译：人类参与的交互环境因其行为的复杂性和不确定性，给自动驾驶车辆的决策过程带来了重大挑战。开发一个可解释且值得信赖的自动驾驶车辆与行人交互决策系统至关重要。以往研究常利用传统博弈论描述交互，因其具有良好的可解释性。然而，传统博弈论假设人类具有完全理性与无限的推理能力，这并不符合现实。为解决这一局限性并提高模型精度，本文提出了一种新颖的框架，将部分可观测马尔可夫决策过程与行为博弈论相结合，以动态建模无信号交叉口处的自动驾驶车辆与行人交互。自动驾驶车辆和行人均被建模为动态信念诱导的量化认知层次模型，该模型考虑了人类在决策过程中的推理局限性和有限理性。此外，通过一个动态信念更新机制，自动驾驶车辆能够基于观察到的行为实时更新其对对手理性程度的理解，并相应地调整自身策略。分析结果表明，我们的模型能有效模拟车辆-行人交互，并且所提出的自动驾驶车辆决策方法在安全性、效率和流畅性方面表现良好。其行为与现实世界的驾驶行为高度相似，甚至与我们之前的虚拟现实实验数据相比，能实现更舒适的驾驶导航。