## 5 Simulation Experiments
### Discussion  
This paper proposes a solution to the problem of collaborative merging of CAVs and MDVs on urban expressways, which is mainly reflected in two aspects. Firstly, a decision-making method combining DRL and game theory is proposed. This method refers to the previous research and takes the equilibrium in game theory as the right-of-way allocation mechanism. However, previous studies have not considered how vehicles should act without pure-strategy equilibrium. Therefore, this study simulates the decision-making of MDVs according to the acceleration distribution of different driving styles. Similarly, CAVs randomly select actions from the action space. From the micro and macro simulation experiments herein, it can be clearly observed that without the guidance of strategy, it is difficult for game participants to achieve equilibrium. Therefore, this study provides a new solution for researchers, using DRL methods to guide the game. Because some parameters in previous studies are difficult to obtain, it is difficult to reproduce previous studies when designing comparative experiments. Therefore, this paper compares the pure game method with the proposed method. Although the game payoff functions are different from others, the core components are similar, including timeliness, safety, comfort, and economic benefits. To a certain extent, it also simulates the collaborative import method based on the static game. Table 6 counts the merging success rate of on-ramp vehicles (without waiting at the end of the acceleration lane), average speed, average minimum car following distance, and speed fluctuation under different control methods. Compared with no control, the merging method based on game theory has a higher success rate, which is mainly due to the estimation of MDVs' driving intentions. It clearly reflects that the proposed method can quickly determine the cooperation degree of the MDVs and avoid the discomfort caused by too many game attempts. At the same time, combined with driving style recognition technology, it can make corresponding decisions for manual driving vehicles with different driving styles.  
As for driving style recognition, the biggest innovation of this paper is to explore the relationship between style and vehicle position, which was completely ignored in previous studies. The differences in driving styles in different regions can be clearly seen from both monitoring video and clustering results. In addition, another type of research on individual driving style modeling is not applicable to this study. For example, reference [13] uses inverse reinforcement learning to model a special manually driven vehicle. Although this can better simulate the behavior of each driver, the style of each driver is different. And,  
Figure 17: Average driving speed on this roadway under different traffic flows.  
the decisions made by drivers in the same scene are similar but not exactly the same. It is also difficult for roadside units to obtain historical information on the same vehicle passing through the road many times. Therefore, there are still many problems to be solved in the decision-making modeling for specific drivers. But, style clustering methods can greatly simplify this process.