  To address the safety and efficiency issues of vehicles at multi-lane merging
zones, a cooperative decision-making framework is designed for connected
automated vehicles (CAVs) using a coalitional game approach. Firstly, a motion
prediction module is established based on the simplified single-track vehicle
model for enhancing the accuracy and reliability of the decision-making
algorithm. Then, the cost function and constraints of the decision making are
designed considering multiple performance indexes, i.e. the safety, comfort and
efficiency. Besides, in order to realize human-like and personalized smart
mobility, different driving characteristics are considered and embedded in the
modeling process. Furthermore, four typical coalition models are defined for
CAVS at the scenario of a multi-lane merging zone. Then, the coalitional game
approach is formulated with model predictive control (MPC) to deal with
decision making of CAVs at the defined scenario. Finally, testings are carried
out in two cases considering different driving characteristics to evaluate the
performance of the developed approach. The testing results show that the
proposed coalitional game based method is able to make reasonable decisions and
adapt to different driving characteristics for CAVs at the multi-lane merging
zone. It guarantees the safety and efficiency of CAVs at the complex dynamic
traffic condition, and simultaneously accommodates the objectives of individual
vehicles, demonstrating the feasibility and effectiveness of the proposed
approach.
