## 4 Conclusions  
This paper investigates the decision-making process of autonomous driving in unsignalized roundabouts and proposes a proximal policy optimization (PPO) learning method based on the PPO algorithm, which is trained using the Carla simulation environment. The proposed algorithmic framework utilizes an aerial view and an automatic driving perception system to acquire road environment information, while the CoordConv network extracts and integrates feature data into the decision-making network. A dynamic multi-objective reward mechanism and dynamic behavioral decision-making are employed to adjust the weight of autonomous vehicle states during training, enabling dynamic adjustment of important indices at different stages.  
The results demonstrate that the optimized algorithm trained with this approach exhibits no significant improvement compared to the PPO+CCMR algorithm; however, it outperforms the PPO algorithm when there are fewer vehicles present. As other vehicles increase on the road, both the improved optimization algorithm's dynamic model multi-objective reward mechanism and unsignalized roundabout decision-making optimization module exhibit noticeable effects, leading to better decisions regarding path selection and effectively enhancing training efficiency as well as vehicle stability and safety. In scenarios involving 20, 100, and 200 social vehicles, respectively, compared with the PPO+CCMR algorithm, our optimized PPO algorithm shows improvements in reward value function by 2.85%, 7.17%, and 19.58%; simulation training efficiency improves by 11.1%, 13.8%, and 7.4%; traversal time is reduced by 2.37%, 2.62%, and 13.96%.  
While our proposed algorithms have achieved certain results in unsignalized roundabout experiments, further research can be combined with intelligent transportation systems considering actual road conditions along with increasingly complex road environments.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and J.Z.; formal analysis, [PERSON]; investigation, [PERSON]; resources, J.Z. and Y.L.; data curation, J.G.; writing--original draft preparation, [PERSON]; writing--review and editing, J.G., [PERSON] and Y.L.; visualization, [PERSON]; supervision, [PERSON] and Y.L.; project administration, [PERSON] All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training the Total Step Size** & \multicolumn{3}{c}{**300,000**} & \multicolumn{3}{c}{**600,000**} \\ \hline
**Scenes** & **20** & **100** & **200** & **20** & **100** & **200** \\ \hline PPO & Successes & 2 & 2 & 1 & 4 & 4 & 3 \\ Algorithm & Failures & 18 & 18 & 19 & 16 & 16 & 17 \\  & Success rate & 10\% & 10\% & 5\% & 20\% & 20\% & 15\% \\ \hline PPO+CCMR & Successes & 9 & 9 & 7 & 16 & 15 & 13 \\  & Failures & 11 & 11 & 13 & 4 & 5 & 7 \\ Algorithm & Success rate & 45\% & 45\% & 35\% & 80\% & 75\% & 65\% \\ \hline Optimized & Successes & 9 & 9 & 8 & 16 & 15 & 14 \\ PPO & Failures & 11 & 11 & 12 & 4 & 5 & 6 \\ Algorithm & Success rate & 45\% & 45\% & 40\% & 80\% & 75\% & 70\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation success rate table.  
Funding: This work is supported in part by the National Key R&D Program under Grant 2021 YFC3001300, in part by the National Natural Science Foundation of China Grant 62371013, in part by the National Natural Science Foundation of China Key Project Collaboration Grant 61931012, and in part by the Academic Research Projects of Beijing Union University under Grant ZK1020208. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest.