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Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presentsa longstanding challenge due to its inherent nonlinearity, non-convexity, anddiscrete characteristics, compounded by the diverse road topologies encounteredin real-world traffic scenarios. The majority of current methodologies are onlyapplicable to a single and specific scenario, predicated on scenario-specificassumptions. Consequently, their application in real-world environments isrestricted by the innumerable nature of traffic scenarios. In this study, wepropose a unified optimization approach that exhibits the potential to addresscooperative decision-making problems related to traffic scenarios with genericroad topologies. This development is grounded in the premise that thetopologies of various traffic scenarios can be universally represented asDirected Acyclic Graphs (DAGs). Particularly, the reference paths and timeprofiles for all involved CAVs are determined in a fully cooperative manner,taking into account factors such as velocities, accelerations, conflictresolutions, and overall traffic efficiency. The cooperative decision-making ofCAVs is approximated as a mixed-integer linear programming (MILP) problembuilding on the DAGs of road topologies. This favorably facilitates the use ofstandard numerical solvers and the global optimality can be attained throughthe optimization. Case studies corresponding to different multi-lane trafficscenarios featuring diverse topologies are scheduled as the test itineraries,and the efficacy of our proposed methodology is corroborated.

A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles with Generic Road Topologies