Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodologies are only applicable to a single and specific scenario, predicated on scenario-specific assumptions. Consequently, their application in real-world environments is restricted by the innumerable nature of traffic scenarios. In this study, we propose a unified optimization approach that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies. This development is grounded in the premise that the topologies of various traffic scenarios can be universally represented as Directed Acyclic Graphs (DAGs). Particularly, the reference paths and time profiles for all involved CAVs are determined in a fully cooperative manner, taking into account factors such as velocities, accelerations, conflict resolutions, and overall traffic efficiency. The cooperative decision-making of CAVs is approximated as a mixed-integer linear programming (MILP) problem building on the DAGs of road topologies. This favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization. Case studies corresponding to different multi-lane traffic scenarios featuring diverse topologies are scheduled as the test itineraries, and the efficacy of our proposed methodology is corroborated.

翻译：网联自动驾驶车辆（CAVs）的协同决策因其固有的非线性、非凸性及离散特性，加之真实交通场景中多样化的道路拓扑结构，始终是一项长期挑战。当前大多数方法仅适用于基于特定场景假设的单一场景，因此受限于交通场景的无限多样性，难以在实际环境中应用。本研究提出一种统一的优化方法，有望解决具有通用道路拓扑结构的交通场景下的协同决策问题。该方法的理论基础在于：各类交通场景的拓扑结构均可统一表示为有向无环图（DAGs）。具体而言，所有涉事CAVs的参考路径与时间剖面均以完全协同的方式确定，同时综合考虑速度、加速度、冲突消解及整体交通效率等因素。基于道路拓扑的DAGs，CAVs的协同决策被近似为混合整数线性规划（MILP）问题，这有利于使用标准数值求解器，并通过优化实现全局最优解。针对不同多车道交通场景（具有多样拓扑结构）的案例研究被设定为测试路线，实验结果验证了所提方法的有效性。