## 3 Results and Discussion
### Simulation and Analysis
Consequently, self-driving vehicles exhibit a more significant increase in their average speeds. To compare the passing efficiency of autonomous vehicles, our focus lies on two specific periods: when the total step length of the training model reaches 300,000 and 600,000. We then proceed to analyze and compare the average passing time in various simulation scenarios, as presented in Table 2.  
The average passing time does not take into account the PPO algorithm, which exhibits poor training performance in unsignalized roundabouts due to limited simulation experiments and lack of reference for the average passing time. As shown in Table 2, with 300,000  
Figure 8: The average reward value of a single step in a 200 vehicle scene. Figure 9: The average speed value graph in 200 vehicles scene. training steps, the optimized PPO algorithm reduces the passing time by 2.32%, 2.61%, and 12.67% compared to the PPO+CCMR algorithm when social vehicles are increased on the scene. With 600,000 training steps, the optimized PPO algorithm further reduces the passing time by 2.37%, 2.62%, and 13.96% compared to the PPO+CCMR algorithm under similar conditions of increasing social vehicles on the scene. The enhanced efficiency of traffic achieved by optimizing the PPO algorithm is positively correlated with both simulation step length and vehicle density within a given scenario at an unsignalized roundabout where autonomous vehicles navigate through inner loop lanes or outer loop lanes with varying route selection strategies as indicated in Table 3.  
As presented in Table 3, when employing an optimized PPO algorithm for the simulation training of autonomous vehicles, the average passing time of inner ring vehicles is observed to be lower than that of outer ring vehicles. Moreover, as the training duration increases, autonomous vehicles tend to prefer traveling within the inner ring. The path selection of autonomous driving vehicles in unsignalized roundabout scenarios is influenced by the number of social vehicles present, with this influence becoming less pronounced as the number of social vehicles increases. To assess the safety performance of autonomous vehicles, particular attention is given to two specific periods during which the total step size reaches 300,000 and 600,000 in the training model. Around these periods, data from 20 experimental groups are collected to determine whether or not successful simulations were achieved. The primary shortcomings observed in the simulation experiments were vehicle collisions and off-lane misalignment, with 20 scenarios primarily involving off-lane misalignment and 200 scenarios mainly focusing on vehicle collisions. Table 4 presents the success rate of these simulation experiments. For both training the PPO+CCMR algorithm and optimizing the PPO algorithm over 300,000 steps, as well as for training over 600,000 steps, the success rates ranged between 35% and 45%, and between 65% and 80%, respectively. However, when synchronizing the PPO+CCMR algorithm with the optimized PPO algorithm for an extended period of time, there was a decrease in the success rate of simulation experiments as scene vehicles increased; this decrease was significantly higher than that observed when only using the PPO algorithm. In scenarios involving identical vehicles, increasing the length of training steps led to an improvement in simulation experiment success rates while enhancing autonomous vehicle safety.