## 1 Introduction  
With the swift advancement of artificial intelligence technology, reinforcement learning, a significant subset of machine learning, has exhibited remarkable applicability in numerous domains, including automotive systems categorization [1], object detection [2], and scene parsing [3]. Presently, the approaches to behavioral decision-making in autonomous driving are primarily categorized into two types: rule-based decision-making and decision-making based on reinforcement learning [4]. The finite state machine approach, introduced by [PERSON] et al. [5], is a typical rule-based decision-making method with high stability. Numerous teams participating in the US DARPA Urban Challenge utilized it as the decision-making control system for autonomous vehicles. The rule-based method relies on preset conditions and logic, which can achieve rapid response to specific traffic conditions, but this method has limited flexibility and adaptability when dealing with unknown or complex environments. [PERSON] et al. [6] proposed a decision-making algorithm based on deep learning, which simplifies the complexity of traditional autonomous driving systems by directly predicting control commands from input images and realizes end-to-end control of autonomous vehicles. Conversely, methods grounded in reinforcement learning, particularly those utilizing deep reinforcement learning, can enhance decision-making strategies through continuous environmental learning, demonstrating superior generalization and the capacity to handle intricate driving scenarios. Among various reinforcement learning algorithms, the Q-learning algorithm [7] is frequently employed to address a wide range of sequential decision-making problems because of its straightforwardness and efficacy. Nevertheless, issues such as its slow convergence and poor sample efficiency pose limitations on its applicability in real-world complex environments. In recent years, to enhance the efficiency and effectiveness of the algorithm, scholars have introduced the deep Q-network (DQN) [8], which builds upon the Q-learning framework. By incorporating deep neural networks, DQN markedly improves performance and generalization. However, issues such as inadequate sample utilization and suboptimal training stability persist in the DQN algorithm. Drawing from this, scholars have suggested several enhancement techniques, such as the double deep Q-network (DDQN) [9], dueling deep Q-network (Dueling DQN) [10], and Noisy Deep Q-Network (Noisy DQN) [11]. These improvement methods have made some progress in improving sample efficiency and algorithm stability, but they are still not safe and stable enough in complex scenarios and have not shown a higher upper limit of decision efficiency. Therefore, improving learning efficiency and decision-making quality remains a key focus of the current research. To address issues such as slow convergence and unstable training, this paper utilizes the dueling double deep Q-network (D3 QN) [12], which integrates DDQN and Dueling DQN, as the foundational model for enhancement to achieve an algorithm that converges more rapidly and with greater stability. Prioritized experience replay (PER) [13] is a method employed in reinforcement learning (RL) to enhance the efficiency of experience replay [14]. Experience replay involves accumulating the agent's experiences--comprising states, actions, rewards, and subsequent states--from its interaction with the environment and subsequently sampling these experiences randomly for training purposes during the training process. Although algorithms such as DQN, DDQN, and Dueling DQN use uniform random sampling for experience replay, the importance of experience samples can vary significantly. Since the samples in the experience replay unit are continuously updated, if uniform random sampling is used to extract a small number of samples from the experience replay unit as model input, some highly important experience samples may not be fully utilized, thereby reducing the training efficiency of the model.