  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.
