## 2 Literature Review  
Each positioning system uses different sensors to implement the navigation functions. The vehicles deploy many sensors that provide the autonomous vehicle strategy center with information required to plan control strategies. For example, real-time kinematic (RTK) positioning, inertial measurement units (IMU), 2D lidar, 3D lidar, radars, and cameras are the sensors of the applications. These sensors can also provide useful information for different positioning methods. RTK provides coordinate information to end applications. There are various studies regarding the development of technology based on this system [3; 4; 5; 6; 7; 8]. Some methods integrate many sensors, while others use more RTK systems to reduce data loss. Researchers try to improve the usefulness and accuracy of RTK information, which is the goal. These methods require many application systems to support them to correct coordinate information. Each application system has a damage probability. In this paper, we propose a system fault-finding mechanism, and the coordinate accuracy is verified based on this mechanism. This mechanism references some related application systems' information for implementation. The reference system is part of WinBus, so there is no additional cost of the sensor in this method. Simultaneous localization and mapping (SLAM) is another currently applied technology in autonomous vehicle positioning. Some studies have discussed this technology. This technology is based on the 3D lidar for implementation [9; 10; 11; 12; 13; 14; 15]. There are many types of research that discuss the 3D lidar SLAM. The 3D lidar provides scan information of the surrounding environment to support the SLAM calculation. SLAM uses some deep learning methods for implementation, such as convolutional neural networks (CNN). This method provides some position information for the applications. There is autonomous vehicle research based on this method for implementation. This method supports autonomous vehicles in a scenario where there is no GPS signal support. This is one of the advantages of the autonomous vehicle application described in this study. Systems based on 3D lidar applications require many hardware computing resources. SLAM alone cannot achieve autonomous vehicles according to the previous application experience and literature. Similarly, when the system responsible for SLAM fails, how the decision-making control system of the self-driving car is informed is relatively less discussed. The fault detection mechanism proposed in this paper is a possible solution for autonomous vehicle applications. Communication is an indispensable element in autonomous vehicle applications. The communication interface includes wired communication and wireless communication. Control area networks (CAN) and the eEthernet are the wired communication interfaces [16; 17; 18]. The CAN bus is used for message transmission between the power chain, chassis, and body. The International Standards Organization also defines the corresponding communication specifications, but the information on the positioning system applications is only partially defined. The CAN bus bandwidth cannot transmit a lot of information from the sensor. The autonomous vehicle requires the communication interface to be the Ethernet. In recent years, there has been much research on positioning systems in the Internet of vehicles (IoV). Communication interfaces used in the Internet of vehicles, such as dedicated short-range communication (DSRC), cellular-vehicle-to-everything (C-V2X), and fifth-generation mobile networks (5G) have positioning-related information [19; 20; 21]. Most of the positioning-related information follows the specifications of SAE J2735 [22]. A part of the communication interface applied by IoV belongs to the wireless communication interface. The IoV method is based on RSU and vehicle coordinates for implementation. There are also studies implementing positioning systems using the SLAM and IoV techniques discussed earlier.