## 2 Related Works
This reduces traffic congestion and allows the vehicles to reach their destination quickly and efficiently. For instance, in the case of a four-way intersection, it is to determine the driving direction for a left turn, a right turn, or a straight one by considering the real-time local traffic situation of the intersection. In [17], the authors proposed a method using Q-learning, which is a type of RL that considers the location change information and vehicle kinematic constraints to optimize the path of a vehicle by changing the driving direction in a dynamic environment. Furthermore, the study in [18] proposed a Q-learning-based method to avoid congestion paths and to optimize the routes of vehicles. The authors modeled it as a scoring system based on the edge when controlling the driving direction after the vehicle has entered. However, Q-learning suffers from the curse of dimensionality due to storing states and actions in a table format, thus making it impractical for dealing with high-dimensional states and actions. In dynamic and complex environments like traffic scenarios, Q-learning can lead to the curse of dimensionality and reduced efficiency. Therefore, to address these issues, applying DRL techniques such as DQN is more suitable. In [19], a DQN-based method was proposed to optimize the routes of vehicles and minimize their travel time. The reward function was modeled as the total travel time with a single objective. In [20], a DQN-based method was proposed to optimize the routes to destinations by determining the driving directions at intersections. Factors such as driving distance, driving time, and the design of reward functions with a single objective were compared and analyzed for their strengths, weaknesses, and performance. However, there is no advantage in minimizing a single objective such as driving time when using DRL. These methods may be simplistic and overly focused on a single goal, thereby making it difficult to adapt to various situations and potentially leading to performance degradation. Therefore, this study aims to introduce effective reward function designs with multiple objectives to facilitate smooth traffic flow. Considering multiple objectives allows the system to adapt flexibly to various situations and develop optimal strategies. This approach is expected to harmonize conflicting objectives and improve future predictions, thereby enhancing the exploration of the agent. Therefore, unlike existing studies, this paper proposes a vehicle routing control method with multiple goals rather than a single goal. Overall, the studies on vehicle routing control have not considered mixed traffic environments significantly. The methods considering mixed traffic systems have been mainly implemented to ensure that AVs move safely in facing the uncertainty of HVs. The study in [21] proposed a trajectory planning and control method for AVs that allows AVs to move safely while avoiding static vehicles, whereas in [22], the authors proposed a motion planning method to plan a route that allows AVs to move safely in mixed traffic environments. In addition, in [23], the authors proposed an adaptive optimal control method that considers HV interaction and heterogeneous driver behavior for a platoon mixed with HVs and an AV. Therefore, in considering an uncertain and dynamic mixed traffic environment, we aim to reflect a more realistic scenario. Based on local information such as AVs and HVs, we propose a direction changing method to ensure the safe and efficient operation of AVs. Therefore, in this paper, we proposed a DRL-based DQN method to distribute traffic flow by controlling the driving direction at intersections in real time, thereby reflecting local information such as traffic conditions and the state of AVs and HVs. We formulate the local decision making of AVs on direction changing in mixed traffic environments, where a multi-objective reward function is proposed to improve safety and efficiency simultaneously.