## 1 Introduction  
Creating a smoother and safer road network is a crucial purpose of traffic management agencies and researchers that has led to various studies about different transport aspects. According to a road safety report in Korea, urban roads made up 51.1% of traffic-related deaths in 2019. In addition, intersection collision is a complicated type of road accident, comprising 49.8% of junction collisions in Korea in 2019 [1]. Furthermore, the number of collisions at an un-signalized junction is higher than that at a signalized intersection due to a higher collision rate and more complex interactions. The traffic rules of signalized intersections are usually disrupted by careless drivers. Autonomous vehicles (AVs) can operate with less human intervention or without human drivers through integrated sensors--namely, radar, lidar, and three-dimensional (3D) cameras, etc. They have become a promising approach to prevent human error and enhance traffic quality, and full automation vehicles are expected as quickly as 2050 [2]. Intersections are also the main issue in applying autonomous driving technologies, especially for non-signalized intersections. The autonomous vehicle (AV) classification defines six levels ranging from level zero (no automation) to level five (full automation) according to the Society of Automotive Engineers [3]. To push AVs into the real world, the Cooperative Intelligent Transport Systems (C-ITS) were designed to communicate information between transportation means and road infrastructures. Furthermore, advanced driver assistance systems (ADAS) that can help vehicles detect numerous dangerous situations, activate autonomous driving mode,or alert drivers have been applied to connected and automated vehicles (CAVs). The ultimate purpose of ADAS is the full automation of technology. For example, [PERSON] et al. [4] applied vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to the improvement of intersection movement assistance. Additionally, [PERSON] et al. [5] applied the internet-of-things (IoT) to smart traffic control. This helped AVs automatically collaborate with the roadside unit and independently decide their speeds. Soon, AVs will share the road with human-driven vehicles (HVs). Hence, this study focuses on the mixed-traffic environment that interacts between HVs and AVs in an urban network with multiple un-signalized intersections. Motivated by the challenges of the self-driving controller, the training and validation of autonomous driving has become the most complex issue. A promising approach is the simulation of autonomous driving in a physical environment. Numerous simulation programs have been introduced to represent AVs in the real world--namely, an open racing car simulator (TORCS), car learning to act (CARLA), and a simulation of urban mobility (SUMO). [PERSON] et al. [6] used reinforcement learning (RL)-based image semantic segmentation, adopting the TORCS simulator. Nevertheless, TORCS did not support the factors of urban simulation, such as intersections and traffic rules. The CARLA simulator was applied to train and evaluate the autonomous driving model with respect to perception and control [7]. However, CARLA only focused on an individual autonomous agent. SUMO, which was introduced by the German Aerospace Center, is capable of simulating multi-agents for an urban-scale network [8]. The SUMO simulator can integrate with the third program (i.e., Python, MATLAB) by adopting a traffic control interface (TraCI). Furthermore, Flow, which is a Python-based tool, integrates a simulator (i.e., SUMO, Aimsum) and RL library (i.e., RLlib, Rllab) [9]. Thus, the integration of a SUMO and Flow has become a hopeful approach to control multi-autonomous agents in mixed-traffic environments. Various studies have applied the integration of Flow and a SUMO to mixed-traffic conditions. For example, [PERSON] et al. [10] applied a SUMO and Flow to figure eights and roundabouts. [PERSON] et al. [11] used a SUMO and Flow for highway simulation.