Although significant progress has been made in decision-making for automated driving, challenges remain for deployment in the real world. One challenge lies in addressing interaction-awareness. Most existing approaches oversimplify interactions between the ego vehicle and surrounding agents, and often neglect interactions among the agents themselves. A common solution is to model these interactions using classical game theory. However, its formulation assumes rational players, whereas human behavior is frequently uncertain or irrational. To address these challenges, we propose the Quantum Game Decision-Making (QGDM) model, a novel framework that combines classical game theory with quantum mechanics principles (such as superposition, entanglement, and interference) to tackle multi-player, multi-strategy decision-making problems. To the best of our knowledge, this is one of the first studies to apply quantum game theory to decision-making for automated driving. QGDM runs in real time on a standard computer, without requiring quantum hardware. We evaluate QGDM in simulation across various scenarios, including roundabouts, merging, and highways, and compare its performance with multiple baseline methods. Results show that QGDM significantly improves success rates and reduces collision rates compared to classical approaches, particularly in scenarios with high interaction.

翻译：尽管自动驾驶决策已取得显著进展，但在实际部署中仍面临诸多挑战。其中一项挑战在于实现交互感知。现有方法大多过度简化自车与周围智能体间的交互，且常忽略智能体之间的相互作用。常见的解决方案是运用经典博弈论对这些交互进行建模。然而，该理论框架假设参与者完全理性，而人类行为往往具有不确定性或非理性特征。为应对这些挑战，我们提出量子博弈决策模型——一种融合经典博弈论与量子力学原理（如叠加、纠缠与干涉）的新型框架，用于解决多参与者、多策略的决策问题。据我们所知，这是将量子博弈论应用于自动驾驶决策领域的首批研究之一。QGDM可在标准计算机上实时运行，无需量子硬件支持。我们在仿真环境中对QGDM进行了多场景评估，包括环岛、汇流与高速公路场景，并将其性能与多种基准方法进行比较。结果表明，相较于经典方法，QGDM能显著提升成功率并降低碰撞率，在强交互场景中表现尤为突出。