To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours. Within the decision-making framework, a motion prediction module is designed and optimized using model predictive control (MPC) to enhance the effectiveness and accuracy of the decision-making algorithm. Besides, the payoff function of decision making is defined with the consideration of vehicle safety, ride comfort and travel efficiency. Additionally, the constraints of the decision-making problem are constructed. Based on the established decision-making model, Stackelberg game and grand coalition game approaches are adopted to address the decision making of CAVs at an unsignalized roundabout. Three testing cases considering personalized driving behaviours are carried out to verify the performance of the developed decision-making algorithms. The testing results show that the proposed game theoretic decision-making framework is able to make safe and reasonable decisions for CAVs in the complex urban scenarios, validating its feasibility and effectiveness. Stackelberg game approach shows its advantage in guaranteeing personalized driving objectives of individuals, while the grand coalition game approach is advantageous regarding the efficiency improvement of the transportation system.

中文翻译：

---

考虑个性化驾驶行为的无信号环岛联网自动驾驶汽车决策


为了提高智能交通系统的安全性和效率，特别是在复杂的城市场景中，本文针对无信号环岛的联网自动驾驶车辆（CAV）设计了一个博弈论决策框架，考虑到其个性化驾驶行为。在决策框架内，使用模型预测控制（MPC）设计和优化运动预测模块，以提高决策算法的有效性和准确性。此外，综合考虑车辆安全性、乘坐舒适性和出行效率，定义了决策的回报函数。此外，还构建了决策问题的约束条件。基于建立的决策模型，采用Stackelberg博弈和大联盟博弈方法来解决CAV在无信号环岛的决策问题。进行了三个考虑个性化驾驶行为的测试案例来验证所开发的决策算法的性能。测试结果表明，所提出的博弈论决策框架能够在复杂的城市场景中为CAV做出安全合理的决策，验证了其可行性和有效性。 Stackelberg博弈方法在保证个人个性化驾驶目标方面显示出优势，而大联盟博弈方法则在交通系统效率提升方面具有优势。