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

--Cooperative decision-making of Connected Autonomous V ehicles (CA Vs) 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 CA Vs 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 CA Vs 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. Index T erms --Autonomous driving, multi-agent systems, connected autonomous vehicles, cooperative decision-making, non-convex optimization, mixed-integer linear programming (MILP). The rapid developments of information technology and artificial intelligence prompt the emergency of connected autonomous vehicles (CA Vs), which enable autonomous driving in a cooperative manner. Widely recognized as a promising direction within future transportation systems, CA Vs are capable of communicating their driving intentions in real-time with other CA Vs, road infrastructures, and cloud devices through V ehicle-to-everything (V2X) [1], [2]. As a result, swarm intelligence is enabled and important driving decisions can be made cooperatively to enhance safety, traffic efficiency, and passenger comfort. Zhenmin Huang, Shaojie Shen, and Jun Ma are with the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China (e-mail: zhuangdf@connect.ust.hk; This work has been submitted to the IEEE for possible publication.