### Related Works
#### 1.2.3 Review of Research Status  
The above briefly introduces the core ideas of different merging methods. This section briefly reviews the above methods. The merging decision model of single-vehicle intelligence has achieved satisfactory results in terms of safety but there are still many strong assumptions, such as that of a complete perception, that decisions can be fully implemented, etc. Some model-based decision-making methods are sometimes too conservative. The local collaborative decision-making algorithm improves the conservative decision-making of single-vehicle intelligence due to the lack of information interaction to a certain extent and further improves the efficiency of traffic, but it is still not perfect. For example, the above methods assume that all autonomous vehicles on the scene have the same decision algorithm and no communication delay and that the main-road driving fleet is stable, which cannot be effectively applied in mixed-traffic scenarios. The method of global coordination can maximize the capacity of the merging area. The optimal traffic sequence can be planned based on the global optimal centralized decision, but the computational burden is the biggest obstacle to its application. Rule-based multi-vehicle scheduling decisions can reduce the computational burden, but these rules have shortcomings. In addition, such studies pay little attention to driving style, ignoring the recognition of human drivers for traffic sequences and the enforceability of vehicles. Based on the above discussion, we find that game-based decision-making is more suitable for the theme of this paper. Among the various methods, game-based merging decision algorithms are particularly well suited for mixed-traffic scenarios characterized by low CAV penetration, diverse vehicle preferences, and the need for cooperation from main-road vehicles. Consequently, this study opts for game theory as the merging decision-making approach. Some researchers have designed a variety of merging methods based on game theory. For example, one study [9] believes that sometimes drivers may choose poor strategies due to their misjudgment of the environment, assuming that manual drivers always make rational choices, but still make wrong decisions according to the probability distribution. From the number of rounds of the game, it can be divided into static games and dynamic games. Another study [10] built a game model between ramp vehicles and mainline vehicles based on a static game and determined the optimal action of each vehicle and planned its trajectory according to the revenue. A third study [11] uses the method of dynamic games to design the space-time trajectory of conflicting vehicle combinations and solves the dynamic game by enumerating various combinations. Especially when the initial gap is not enough to realize the immediate import, this method effectively induces the action of competitive vehicles through comprehensive action game theory and dynamic programming. In addition, study [12] believes that game decisions are not made at the same time, but that one party reacts after observing the actions of the other party. Therefore, based on the partially observable leader-follower game theory, this paper models the inflow problem. Study [13] proposes a data-driven method to model the behavior of MDVs and regards MDVs as the player of the game. The game payoff matrix is used as the data of the IRL learning reward function, and a DQN is used to simulate the behavior of MDVs. However, existing game-based methods have their shortcomings:  
1. Strategy Quantification Issue: The game's strategy space, often based on semantic information like \"yield or merge\", makes it challenging to select strategies that align with individual preferences. 2. Decision Implementation Issue: Pure-strategy Nash equilibria, if used as non-allocation mechanisms, may fail to produce an equilibrium in some game rounds. Mixed-strategy Nash equilibria yield probabilistic outcomes, posing a dilemma for decision-making under these conditions. 3.