## 2 Literature Review  
The DQN algorithm has been extensively utilized in intersection signal control. In an isolated intersection, [PERSON] et al. [16] defined states and actions using discretization grids and employed cumulative waiting time as a reward for training the DQN model to control agents. This method can dynamically adjust the signal-light switching time based on traffic conditions, aiming to reduce vehicle waiting time and alleviate traffic congestion. Subsequently, [PERSON] [17] and [PERSON] [18] made further improvements in obtaining real-time traffic information. [PERSON] [17] employed dedicated short-range communications (DSRC), while [PERSON] [18] used discretization states to define the respective states of vehicles. [PERSON] [19] and [PERSON] [20] adopted a comprehensive approach to achieve more effective traffic control by defining states based on sensor acquisition and a discretization grid, respectively. Both studies employed the DQN model for training, with actions to either maintain the current signal phase or switch to the next phase. In defining rewards, [PERSON] [19] utilizes the inverse of the product of the output and trade-off coefficient in the lane, as well as the cumulative queue length. On the other hand, [PERSON] [20] integrates vehicle waiting time, vehicle speed, number of vehicles, vehicle delay, and queue length to create a reward function. These methods can dynamically adjust the signal-light switching time based on traffic conditions, leading to more effective traffic control. Previous studies on arterial intersections have proposed a method to output appropriate phases based on the traffic-flow state in all directions at the intersection and dynamically adjust the phase length [21]. [PERSON] [22] employed the DQN model to predict local state information, including vehicle position, speed, intersection signal phase, and residual signal phase. However, the study did not explore methods to enhance the model's generalization ability and robustness. [PERSON] [23] proposed a method based on sub-region joint training to enhance the model's generalization ability and robustness. This approach employed the DQN model to predict queue length and current actions. Experimental results indicate that the sub-region joint training method significantly improves the prediction accuracy of the DQN model. Moreover, joint training of multiple sub-regions through the encoder further enhances the model's generalization ability. [PERSON] [24] introduced a method for implementing communication based on neural network sharing of information. This approach utilized the DQN model to predict local state information, including waiting time and the number of vehicles. While this method effectively improves prediction accuracy, the collaborative training approach of sharing parameters among agents may cause model instability and performance degradation. Notably, DQN has demonstrated promising results in the study of ATSP, effectively reducing the cumulative delay at intersections [25; 26]. The primary focus of this paper is to explore the application of deep reinforcement learning in addressing the real-time signal-priority problem faced by self-driving-busarterial lines. The central objective, based on the CACC model, is to reduce the average queue length and minimize delays. To facilitate coordination between agents [27] and mitigate learning challenges for the agents [28], the DQN algorithm is employed to devise a real-time signal-priority control method for self-driving-bus arterial lines. To enhance the algorithm's efficiency, a simplified state, encompassing vehicle and signal light information, is chosen as the input for the agent. In order to identify the most relevant factors that align with the agent's learning objectives and ensure efficient traffic flow control at intersections, reward and penalty functions are defined, encompassing delay penalty, queue length penalty, and equalization reward. These functions regulate vehicles' passage through intersections by modifying signal-light timings.