## 6 Conclusions and Future Works  
This paper presents a cooperative flexible merging decision model grounded in incomplete information game theory, tailored to address the intricacies of mixed-traffic scenarios on urban freeways. It delves into the diverse driving styles of MDVs, their impact on the payoff function during the game process, and the spatial-temporal inconsistencies in driving styles that lead to varied responses from MDVs to CAV merging requests.  
A driving style clustering method leveraging the Gaussian Mixture Model (GMM) is established, alongside a Support Vector Machine (SVM) model that identifies driving styles and adjusts the gain function. This approach offers a decision support model for merging CAVs, trained on actual data. By integrating DRL algorithms, the model significantly reduces the number of gaming rounds and enhances the smoothness and comfort of the merging process. Simulation experiments demonstrate that the DRL-augmented game model swiftly reaches a decision equilibrium and efficiently adapts to the complexities of urban traffic, outperforming pure game models and those without control.  
Despite these advancements, the study acknowledges several limitations. Firstly, while the model captures the inherent uncertainty in drivers' decision-making, there are discrepancies between the actual decisions of specific drivers and the modeled distribution categories. Future research will delve deeper into individual behaviors. Secondly, the study's consideration of vehicle types is not exhaustive; upcoming research will focus on modeling the behavior of specific vehicle types, such as trucks. Lastly, the current study assumes the perfect execution of decisions by vehicles, overlooking their maneuverability. Future work will incorporate the design of high-precision controllers to address this aspect. By addressing these limitations, future research can further refine the merging decision model, leading to more robust and realistic applications in complex traffic environments.  
Conceptualization, Z.D., H.X., S.Y. and K.L.; methodology, Z.D., H.X., P.Z., S.Y. and J.W.([PERSON]); software, P.Z. and Y.L.; validation, Z.D., P.Z., S.Y. and K.L.; formal analysis, P.Z. and S.Y.; investigation, Z.D., P.Z. and K.L.; resources, Z.D., S.Y. and Y.L.; data curation, S.Y. and Y.L.; writing--original draft preparation, Z.D. and P.Z.; writing--review and editing, J.W. ([PERSON]) and K.L.; visualization, P.Z., S.Y. and Y.L.; supervision, Z.D. and K.L.; project administration, Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results received funding from the Major Science and Technology Projects of Tianjin under Grant Agreement No. 21 ZXJBGX00030.  
Not applicable.  
The data that support the findings of this study are included in the article and have been referenced accordingly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Success Rate** & \begin{tabular}{c} **Average Speed** \\ **(km/h)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Minimum** \\ **Distance (m)** \\ \end{tabular} &
\begin{tabular}{c} **Speed** \\ **Perturbation** \\ \end{tabular} \\ \hline Non-control & 37.6\% & 50.795 & 11.83 & gentle \\ Game-based & 52.4\% & 51.456 & 10.64 & severe \\ DRL+Game & 85.7\% & 51.935 & 11.23 & gentle \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation results of mixed traffic flow.  
Conflicts of Interest: Authors [PERSON], [PERSON] and [PERSON] were employed by the CATARC Intelligent and Connected Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.