## 3 Results and Discussion
### Simulation and Analysis  
The simulation scenario includes a total of 20 social vehicles, and the automatic driving vehicle obtained simulation data after undergoing 600,000 steps of training in an unsignalized roundabout. The results are presented in Figure 4, where it can be observed that the reward value function of the optimized PPO algorithm stabilizes around 500,000 steps, while the reward value function of the PPO+CCMR algorithm stabilizes at approximately 540,000 steps. Notably, compared to the PPO+CCMR algorithm, there is a significant increase in reward value of approximately 2.8%, indicating an improvement in simulation training efficiency by about 11.1% for the optimized PPO algorithm. As depicted in Figure 5, the optimized PPO algorithm for vehicle average speed optimization exhibits a gradual increase with the simulation step size, surpassing both the PPO+CCMR curve and the standard PPO algorithm. The initial average training speed of the optimized PPO algorithm is relatively low but improves progressively during simulation experiment training, demonstrating smoother and more stable performance compared to the PPO+CCMR algorithm. Figure 4: The average reward value of a single step in a 20 vehicle scene. Figure 5: The average speed value graph in 20 vehicles scene. The simulation scenario includes 100 social vehicles, and the automatic driving vehicle obtained simulation data after 600,000 training steps in an unsignalized roundabout. As depicted in Figure 6, the reward value function of the optimized PPO algorithm tends to stabilize at around 500,000 steps, while the reward value function of the PPO+CCMR algorithm stabilizes at approximately 560,000 steps. Compared to the PPO+CCMR algorithm, there is a significant increase of 7.17% in the reward value achieved by utilizing only the optimized PPO algorithm. Moreover, this improvement results in a remarkable enhancement of training efficiency by approximately 13.8%. The average vehicle speed of the optimized PPO algorithm and PPO+CCMR curve is demonstrated as being in close proximity, yet significantly higher than that of the conventional PPO algorithm, as depicted in Figure 7. Moreover, it is observed that the average speed growth curve of the optimized PPO algorithm exhibits enhanced smoothness and stability compared to that of the PPO+CCMR algorithm. The simulation scenario involves 200 social vehicles, and the automatic driving vehicle obtained simulation data after undergoing 600,000 steps of training in an unsignalized roundabout. As depicted in Figure 8, within this environment of 200 social vehicles, autonomous vehicles are influenced by their own driving status as well as surrounding vehicles. The average reward value achieved by autonomous vehicles utilizing the optimized PPO algorithm surpasses that of the PPO+CCMR algorithm in an unsignalized roundabout. Moreover, decision-making performed by the optimized PPO algorithm is more rational  
Figure 6: The average reward value of a single step in a 100 vehicle scene. Figure 7: The average speed value graph in a 100 vehicle scene. and yields higher rewards. The reward value function of the optimized PPO algorithm tends to stabilize at approximately 540,000 steps, while for the PPO+CCMR algorithm it stabilizes around 560,000 steps. Compared to the PPO+CCMR algorithm, there is a significant increase of 19.58% in the reward value when employing the optimized PPO algorithm; additionally, simulation training efficiency improves by approximately 7.4%. The initial average speed of the optimized PPO algorithm is observed to be lower than that of the PPO+CCMR algorithm, as depicted in Figure 9. However, with the progressive increment of the speed weight during training, a steady rise in average speed is witnessed, leading to continuous enhancement in autonomous vehicles' efficiency while navigating unsignalized roundabouts. Upon reaching 600,000 steps, it becomes evident that the average speed surpasses that achieved by training under the PPO+CCMR algorithm.