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To address the safety and efficiency issues of vehicles at multi-lane mergingzones, a cooperative decision-making framework is designed for connectedautomated vehicles (CAVs) using a coalitional game approach. Firstly, a motionprediction module is established based on the simplified single-track vehiclemodel for enhancing the accuracy and reliability of the decision-makingalgorithm. Then, the cost function and constraints of the decision making aredesigned considering multiple performance indexes, i.e. the safety, comfort andefficiency. Besides, in order to realize human-like and personalized smartmobility, different driving characteristics are considered and embedded in themodeling process. Furthermore, four typical coalition models are defined forCAVS at the scenario of a multi-lane merging zone. Then, the coalitional gameapproach is formulated with model predictive control (MPC) to deal withdecision making of CAVs at the defined scenario. Finally, testings are carriedout in two cases considering different driving characteristics to evaluate theperformance of the developed approach. The testing results show that theproposed coalitional game based method is able to make reasonable decisions andadapt to different driving characteristics for CAVs at the multi-lane mergingzone. It guarantees the safety and efficiency of CAVs at the complex dynamictraffic condition, and simultaneously accommodates the objectives of individualvehicles, demonstrating the feasibility and effectiveness of the proposedapproach.

Cooperative Decision Making of Connected Automated Vehicles at Multi-lane Merging Zone: A Coalitional Game Approach