用量子博弈模型让自动驾驶更懂车流互动，减少误判。
Quantum game models for interaction-aware decision-making in automated driving
- 将量子叠加与纠缠引入博弈模型，模拟车辆交互复杂性。
- 在汇入与环岛场景中，碰撞率更低，成功率更高。
- 无需量子设备，可实时运行，适合智能驾驶决策系统。
自动驾驶决策需考虑周围车辆的交互行为才能有效。但传统方法常忽略或过度简化这些交互，导致主车行为过于保守。为此，我们提出两种量子博弈模型：QG-U1（量子博弈-幺正1）和QG-G4（量子博弈-门4），通过引入量子力学中的叠加、干涉与纠缠原理扩展经典博弈论。两模型针对双玩家、每方两个策略的博弈设计，可在普通计算机上实时运行，无需量子硬件。我们在汇入与环岛场景中评估其表现，对比经典博弈方法及基准模型（IDM、MOBIL、基于效用的方法）。结果表明，QG-G4相比基线方法碰撞率更低、成功率更高；在特定参数下，两类量子模型的期望收益均高于经典博弈方法。
原文摘要 · Abstract (English)
Decision-making in automated driving must consider interactions with surrounding agents to be effective. However, traditional methods often neglect or oversimplify these interactions because they are difficult to model and solve, which can lead to overly conservative behavior of the ego vehicle. To address this gap, we propose two quantum game models, QG-U1 (Quantum Game - Unitary 1) and QG-G4 (Quantum Game - Gates 4), for interaction-aware decision-making. These models extend classical game theory by incorporating principles of quantum mechanics, such as superposition, interference, and entanglement. Specifically, QG-U1 and QG-G4 are designed for two-player games with two strategies per player and can be executed in real time on a standard computer without requiring quantum hardware. We evaluate both models in merging and roundabout scenarios and compare them with classical game-theoretic methods and baseline approaches (IDM, MOBIL, and a utility-based technique). Results show that QG-G4 achieves lower collision rates and higher success rates compared to baseline methods, while both quantum models yield higher expected payoffs than classical game approaches under certain parameter settings.
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