## 1 Introduction  
With the advances in information technology, networking, digitalization, and artificial intelligence (AI), research on autonomous vehicles has gained significant attention in the public domain. It is anticipated that autonomous vehicles will gradually replace conventional vehicles in the future. In addition to performing traditional vehicle functions, such as acceleration, deceleration, braking, and motion control, autonomous vehicles also exhibit AI-driven behaviors including environment perception, task planning, path planning, vehicle control mechanisms, and intelligent obstacle avoidance [1, 2, 3, 4]. The exploration and implementation of autonomous vehicles have the potential to alleviate road traffic congestion issues while reducing fuel consumption and carbon dioxide emissions, and thereby enhances the overall road efficiency. Although there are numerous advantages associated with autonomous vehicles' application across various scenarios involving people or objects encountered during travel journeys, roundabouts still pose a substantial challenge [5]. The decision-making strategy of the target vehicle in a social vehicle environment is investigated as a scenario [6, 7]. [PERSON] et al. focused on lane waiting time to address the issue of optimal lane selection and studied the method for selecting the best lane in a three-lane roundabout without signaling [8]. [PERSON] et al. examined the impact of different penetration rates for autonomous vehicles on roundabout operation and improved traffic operations under low saturation and half-saturation flow [9]. [PERSON] et al. proposed a comprehensive general method for controlling the laneless path of autonomous vehicles to tackle large non-lane roundabouts [10]. [PERSON] introduced a layered control scheme based on an intelligent network environment to enhance traffic efficiency at circular crossings without traffic lights [11]. In summary, the previous research on the roundabout scenario has primarily focused on assessing its overall efficiency and simulating autonomous vehicles in an ideal state, assuming a perfect intelligent system network [12]. However, there has been limited investigation into the decision-making problems faced by individual autonomous vehicle entities at a roundabout. This study addresses this gap by considering separate autonomous vehicles operating at a penetration rate of 0, meaning that when all other vehicles are non-autonomous, the safety and stability of these vehicles remain unaffected. The optimization of decision-making for autonomous vehicles at unsignalized roundabouts and selection of routes to enhance bicycle passage efficiency through such intersections are explored [13]. Autonomous driving decision-making encompasses both rule-based and data-driven strategies [14]. While rule-based approaches face challenges in addressing problems encountered by autonomous vehicles due to the complexity and dynamic nature of road conditions, data-driven strategies can optimize vehicle decision-making through the interaction between target action behaviors and the environment. The proximal policy optimization algorithm is a data-driven decision algorithm, which plays an important role in decision-making research [15]. [PERSON] et al. proposed a near-end strategy optimization algorithm based on dual observation and matching reward, in order to study air combat autonomous decision-making, effectively solving the problems of high information redundancy and slow convergence of traditional reinforcement-learning air combat autonomous decision-making algorithms [16]. Based on the traditional PPO algorithm, [PERSON] et al. introduced the network structure of a long- and short-term memory network and used the PPO-LSTM algorithm to solve the problem of online trajectory planning in the ascending phase of high-speed aircraft [17]. [PERSON] et al. proposed an intersection signal phase and timing optimization method based on the hybrid proximal policy optimization algorithm to solve the signal control problem [18]. [PERSON] et al.