###### Abstract  
With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period until the Market Penetration Rate reaches 100%. During this phase, AVs and HVs will interact and coexist on the roads. Such an environment can cause unpredictable and dynamic traffic conditions due to HVs, which results in traffic problems including traffic congestion. Therefore, the routes of AVs must be controlled in a mixed traffic environment. This study proposes a multi-objective vehicle routing control method using a deep Q-network to control the driving direction at intersections in a mixed traffic environment. The objective is to distribute the traffic flow and control the routes safely and efficiently to their destination. Simulation results showed that the proposed method outperformed existing methods in terms of the driving distance, time, and waiting time of AVs, particularly in more dynamic traffic environments. Consequently, the traffic became smooth as it moved along optimal routes. vehicle routing control; deep reinforcement learning; deep Q-network; autonomous vehicle; traffic flow +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
20241422141  
environment has the potential to induce unstable traffic flow, thus negatively impacting safety by increasing the frequency and severity of traffic accidents [5]. Therefore, to optimize traffic flow in a mixed traffic system, high-level decision making in AVs is essential as it can ensure that both AVs and HVs reach their destinations safely and efficiently. One of the major challenges in the high-level decision making of AVs is the vehicle routing problem (VRP) [6; 7], which significantly influences traffic safety and efficiency. Research on VRP, which aims to find efficient routes to destinations, has been extensively conducted; however, these studies have been insufficient in considering mixed traffic systems. In a mixed traffic system, HVs are likely to cause unpredictable and dynamic traffic conditions because they are driven by humans. Consequently, as HVs bring unknown/uncertain behaviors, planning, and control in such mixed traffic systems, achieving safe and efficient maneuvers is challenging [8]. Therefore, efficient methods for controlling the routing of AVs while considering mixed traffic environments of AVs and HVs are needed. However, there is a limit to AV routing control when using deep learning in a dynamic environment that changes in real time. In other words, a large amount of labeled data is required to achieve good results with deep learning. However, it is difficult to generate such data for the complex decision-making process of autonomous driving when using deep learning. Therefore, it is crucial to demonstrate how to process these decisions without using explicitly labeled data or predefined rules. Deep reinforcement learning (DRL) is a potential machine learning method that can address this problem. Therefore, we propose a DRL method that utilizes a deep Q-network (DQN) [9] for AV route control. This method features a novel local reward design that incorporates safety and efficiency. Additionally, the AVs learn policies to alleviate traffic congestion by reflecting local information such as real-time traffic. The AVs aim to make necessary route control for efficiency improvement while ensuring safe traffic movement in a mixed traffic system. The contributions of this study can be summarized as follows:  
* We formulate a model of vehicle routing control that reflects real-time local information in mixed traffic as a decentralized problem, where agents learn a safe and efficient driving policy to distribute traffic flow and improve efficiency. * We proposed a novel, efficient, and scalable routing control model by introducing an effective reward function design.