The key motivation of this paper lies in the development of a high-level decision-making framework for autonomous overtaking maneuvers on two-lane country roads with dynamic oncoming traffic. To generate an optimal and safe decision sequence for such scenario, an innovative high-level decision-making framework that combines model predictive control (MPC) and switching control methodologies is introduced. Specifically, the autonomous vehicle is abstracted and modelled as a switched system. This abstraction allows vehicle to operate in different modes corresponding to different high-level decisions. It establishes a crucial connection between high-level decision-making and low-level behaviour of the autonomous vehicle. Furthermore, barrier functions and predictive models that account for the relationship between the autonomous vehicle and oncoming traffic are incorporated. This technique enables us to guarantee the satisfaction of constraints, while also assessing performance within a prediction horizon. By repeatedly solving the online constrained optimization problems, we not only generate an optimal decision sequence for overtaking safely and efficiently but also enhance the adaptability and robustness. This adaptability allows the system to respond effectively to potential changes and unexpected events. Finally, the performance of the proposed MPC framework is demonstrated via simulations of four driving scenarios, which shows that it can handle multiple behaviours.

中文翻译：

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自主超车高层决策：基于MPC的切换控制方法

本文的主要动机在于开发一个高层决策框架，用于在动态迎面交通的双车道乡村道路上进行自主超车操作。为了针对这种情况生成最佳且安全的决策序列，引入了一种创新的高级决策框架，该框架结合了模型预测控制（MPC）和切换控制方法。具体来说，自动驾驶车辆被抽象并建模为交换系统。这种抽象允许车辆根据不同的高层决策以不同的模式运行。它在自动驾驶车辆的高层决策和低层行为之间建立了至关重要的联系。此外，还纳入了考虑自动驾驶车辆与迎面而来的交通之间关系的障碍功能和预测模型。这种技术使我们能够保证约束的满足，同时还评估预测范围内的性能。通过反复求解在线约束优化问题，不仅生成安全高效超车的最优决策序列，而且增强了适应性和鲁棒性。这种适应性使系统能够有效地响应潜在的变化和意外事件。最后，通过四个驾驶场景的模拟证明了所提出的 MPC 框架的性能，这表明它可以处理多种行为。