基于人类有限理性的车辆-行人交互动态博弈决策框架
Dynamic Game-Theoretical Decision-Making Framework for Vehicle-Pedestrian Interaction with Human Bounded Rationality
AI总结：
本文提出一种结合部分可观测马尔可夫决策过程与行为博弈论的新颖框架，通过动态信念诱导的量子认知层次模型及动态信念更新机制，在无信号交叉口动态建模有限理性下的车辆-行人交互，有效提升了自动驾驶决策的安全性、效率与流畅性。
摘要：
人类参与的交互环境由于人类行为的复杂性和不确定性，对自动驾驶车辆决策过程提出了重大挑战。为与行人交互的自动驾驶车辆开发可解释且可信赖的决策系统至关重要。以往的研究常使用传统博弈论来描述交互过程，因其具有可解释性。然而，该理论假设人类具备完全理性和无限的推理能力，这并不符合现实。为解决该局限性并提升模型准确性，本文提出了一种将部分可观测马尔可夫决策过程与行为博弈论相集成的新颖框架，以动态建模无信号交叉口的自动驾驶车辆-行人交互。通过考虑决策过程中人类的推理局限性和有限理性，自动驾驶车辆和行人均被建模为动态信念诱导的量子认知层次（DB-QCH）模型。此外，动态信念更新机制使自动驾驶车辆能够基于观察到的行为实时更新对对手理性程度的理解，并相应调整其策略。分析结果表明，我们的模型有效模拟了车辆-行人交互，且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。它紧密 resembles 真实驾驶行为，甚至与我们之前的虚拟现实实验数据相比，实现了更舒适的驾驶导航。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
