### Motivation and Contribution  
To sum up, although the current merging decision-making methods of expressways consider the coordination rules of conflicting vehicles to a certain extent, the research at this stage seldom considers the complex interaction process. The partial decision algorithm assumes that the driving style of the target vehicle is uniform, that there is no significant difference in the decision objectives, and that it is also completely rational in the import process, not making any obvious irrational decisions. However, in real scenarios, there are different degrees of \"selfish driving\" among the conflict subjects in merging areas. And, the rational behavior of the drivers of manual driving vehicles is limited. There are differences in the core benefits of decision-making among different decision-makers. If these significant differences are ignored, the intention of other vehicles will be seriously misjudged, which will significantly affect the decision-making of CAVs. This study believes that manual driving style changes with the surrounding environment, attempts to explore the basic patterns and time-varying characteristics of MDVs' driving styles, and proposes a driving conflict decision-making solution considering the heterogeneity in driving styles. The synthesis of the research on merging decision-making methods yields several key insights. Model-based and learning-based approaches, lacking a cooperative framework, tend to be overly cautious when gaps do not meet insertion criteria, leading to a low success rate in merging. Objective optimization and rule-based methods struggle at low CAV penetration rates and assume uniform merging points on the main road. In contrast, game-based methods adeptly accommodate individual drivers' flexible preferences for merging timing, location, and maneuvers, aligning well with the varied choices of human drivers. The rational human assumption in game theory also resonates with drivers' self-interested behaviors. In summary, current merging decision algorithms, while accounting for the potential cooperation of main-road vehicles, often overlook the relationship between the degree of cooperation and the vehicles' spatial-temporal distribution. Most studies assume a fixed driving style for the target vehicle, ignoring the spatial-temporal variability inherent in drivers' styles. This oversight is evident in the varying cooperation levels of main-road vehicles and the behavioral patterns influenced by their positions in the merging zone. Ignoring this vehicle heterogeneity could lead to incorrect assessments of main-road vehicle intentions, resulting in overly conservative or aggressive decision-making outputs, which in turn affect the performance and generalizability of the decision-making system. This study introduces a novel, flexible game-based merging decision-making method that takes into account the heterogeneity of manual driving vehicles' (MDVs) driving styles to tackle the aforementioned challenges. We have developed an incomplete information heterogeneous vehicle merging game decision-making model that addresses incomplete information by identifying MDVs' driving styles and incorporates changes in these styles into the decision-making process. A pure-strategy Nash equilibrium serves as the mechanism for the deallocation of the right-of-way. The proposed method combines game theory with deep reinforcement learning (DRL) to expedite the process by which merging Connected Autonomous Vehicles (CAVs) achieve favorable game equilibrium outcomes. This research advances the field by offering a sophisticated approach to merging decisions that respects the complexity and variability of real-world driving behaviors, enhancing both traffic efficiency and safety. Real-world scenarios are faithfully recreated, and simulation experiments are conducted, demonstrating the effectiveness of the proposed algorithms. The main contributions of this paper are as follows:  
1.