## 4 Positioning Fault Detection Methods  
How to increase positioning accuracy is a problem that researchers and developers wish to solve. This positioning system combines RTK and SLAM to aid the autonomous vehicle's strategy-building process. The positioning fusion algorithm references the RTK and SLAM information to provide the location data. For precision, these two systems work together as a team. However, every application system has the possibility of failure. In this research, we propose a strategy by which to determine the fault detection mechanism and discuss the relationship between coordinate data fusion and faults. Here, we take advantage of the interdependence of the data to achieve this. Finding faults requires some mechanisms and a relatively stable device to support them. Taking speed information as an example, there are devices in the vehicle that provide speed information, such as the ABS (antilock braking system), IMU, and GPS. The ABS is a more stable provider of speed signals than the IMU and GPS. Based on this concept, the speed of the fault detection method references the ABS speed information. The detection method integrates the IMU and ABS to help the system to find the faults. A prediction algorithm with heading and velocity information can provide reference information to the positioning fusion algorithm. This information is a reference message that helps the fusion algorithm determine the correctness of the positioning data from the RTK. The SLM fault detection method references the RTK information to detect the fault. Figure 3 shows the functional blocks of the positioning system and the fault detection method. The time \(t_{n}\) is the current time, and \(t_{n+1}\) is the following time. The predicted coordinates reference the previous credible coordinates for the fault detection method of RTK and SLAM and are used as reference sources. The prediction data reference the vehicle speed and heading. Coordinate data, which must be reliable information derived from a prediction method, are referred to by the positioning system fault detection algorithm. The speed and heading information are from the ABS and IMU. The application of these two  
Figure 2: Positioning system. devices on a vehicle results in a relatively stable device. This method is suitable for use as a reference for estimating messages in positioning system fault detection. The fault detection function block is shown in Figure 4. This method of obtaining reference data must be chosen following the positioning system's features, for example, whether the fault detection algorithm used in the RTK uses the input coordinate data from the RTK or whether the method used in SLAM and the input coordinate data will be replaced with SLAM coordinate data. The reference coordinates for SLAM and RTK are both from the prediction coordinates. The positioning error condition is the error level condition. This condition defines the allowable error range of the positioning system. The data output are fault information and positioning data that the positioning fusion algorithm will reference. The coordinate information allows the system to have a reference position. The positioning system of the WinBus fuses the RTK and SLM information to generate location information for the autonomous application. These data are used by the vehicle to determine its location. The original fusion algorithm refers to Equation (1), where \(P_{f}\) is the position data that can be latitude or longitude information. This position information is calculated from the fusion algorithm. _Prk_ is the position information from RTK. _Pslam_ is the position information from SLAM. _Prk_ and _Pslam_ can be the latitude or longitude  
Figure 4: Fault detection method function block. Figure 3: Functional blocks of the positioning system. information. _Wrtk_ is the weight for the RTK. _Wslam_ is the weight for SLAM. The weight parameter can adjusted by the application. RTK has a large weight in this system. Each application has the primary trust information.