## 3 Results and Discussion  
### Experimental Environment  
In this study, the proximal policy optimization (PPO) algorithm is employed to optimize the decision-making behavior of autonomous vehicles at unsignalized roundabouts. The simulation environment consists of a 64-bit Ubuntu 20.04 operating system, Carla 0.9.6 simulation platform, and Roadrunner R2020b software for creating autonomous driving scenarios. The simulated training scenarios encompass urban roads, including unsignalized roundabouts and signalized intersections. Specifically, the decision scenario involves a straight road segment of 100 meters in both front and rear directions, as well as an unsignalized roundabout with a diameter of 20 meters. The simulated scene includes dual lanes for the entrance lane, the inner lane of the roundabout, and the exit lane. At the entrance of the unsignalized roundabout, appropriate measures such as speed reduction signs, speed limits, and traffic signs indicating a roundabout should be implemented; moreover, sensors on autonomous vehicles should respond accordingly upon detecting these relevant traffic signs [30]. The designed scene aims to evaluate various vehicle action decisions such as turning movements, avoidance maneuvers, and acceleration/deceleration behaviors during following or overtaking other vehicles. The experimental groups utilized the proximal policy optimization (PPO) algorithm, the coordinate convolution multi-reward function under PPO (PPO+CCMR), and an optimized version of the PPO algorithm. It is important to note that the deep reinforcement learning PPO algorithm requires a relatively long time for establishing the automatic driving model. Therefore, in order to ensure a fair comparison, all other factors were kept constant throughout the experiment. Furthermore, it is crucial to optimize the training efficiency of deep reinforcement learning; thus, each of the three experimental groups were given equal training time for accurate comparison. The independent variables in our experiments included traffic flow at unsignalized roundabouts with four different conditions as follows: (1) a scenario with 20 social vehicles where there are minimal other vehicles present; (2) a scenario with 100 social vehicles causing interference at unsignalized roundabouts; (3) a scenario with 200 social vehicles resulting in increased interference at unsignalized roundabouts without signals; and finally, (4) a scenario with heavy traffic congestion at unsignalized roundabouts due to the presence of 300 social vehicles. Figure 3 illustrates the program execution process. The stability evaluation criteria for autonomous vehicles encompass attitude stability, lateral and longitudinal stability, driving safety, and obstacle avoidance capability. In the simulation results, the one-step reward value function is employed as the evaluation metric to compare the one-step reward function curves in identical environments. A higher value indicates better stability. During the simulation experiment, the driving efficiency of autonomous vehicles is evaluated based on their travel time. The simulation scenarios include linear roads and unsignalized roundabouts where travel time is recorded to calculate time reduction efficiency that directly reflects traffic efficiency. Figure 3: Simulator running diagram. The safety evaluation of autonomous vehicles encompasses vehicle control accuracy, sensor performance, road adaptability, emergency handling capability, and compliance with traffic regulations. In the simulation experiment, apart from the decision-making algorithm of the autonomous vehicle, all other vehicles exhibit identical performance characteristics. Additionally, the perception system comprising cameras and lidar is configured uniformly. The simulation environment is trained using urban roads and unsignalized roundabouts. The safety gap in autonomous vehicles primarily manifests in the control accuracy of their decision-making algorithms and their ability to handle emergency situations effectively.