## 6 Conclusions  
Autonomous vehicles use relatively accurate positioning information to formulate control strategies and improve vehicle safety. Positioning systems have the problem of positioning errors. There are many methods implemented using only a single positioning method. There are certain risks in autonomous vehicles that rely on a single positioning system. Therefore, scholars have discussed a variety of options. Coordinate information fusion is one possible solutions. There are still some problems to be solved in positioning systems, such as fault problems.  
In this paper, we have proposed a fault detection method to support coordinate fusion and compared the differences between the errors of various positioning methods and the proposed method. In the application, the concept of data fusion was used for error correction so that the autonomous vehicle had relatively accurate coordinate values as a reference. The coordinate information obtained by the original fusion algorithm still had an error problem. This was due to the fact that the original algorithm only focused on the weight allocation of the coordinate information. It did not consider the impact of system failures on information fusion. The fault detection mechanism referenced a prediction coordinate. SLAM was found to have more errors in all the positioning systems after comparing this coordinate. After observing these phenomena, we changed part of the algorithm. The adjusted coordinate fusion algorithm corrected 89.8% of errors. The experiments demonstrated that the method was able to detect errors when the positioning system had failed. The fault detection and correction mechanism was used to correct the coordinate. The results showed that this mechanism was able to support the positioning system in certain conditions and was able to correct the coordinate data.  
We used the WinBus as the experimental platform. The scope of the WinBus experiment was mainly limited to Chang Hua Coastal Industrial Park. The use of sensors configured in the vehicle and the application equipment configured in the experiment field were limited. Not every vehicle had the same sensors. In this method, there will be gaps in the parameters of the vehicle dynamics, and there will be differences in the threshold values of the positioning system's fault detection. Artificial intelligence (AI) may be used to support the proposed fault detection and correction methods in the future. There are AI tools available that can be used to determine the most suitable setting value for each car.  
Conceptualization, W.-H.C.; methodology, W.-H.C.; software, W.-H.C. and Y.-S.L.; validation, W.-H.C. and Y.-S.L.; formal analysis, W.-H.C. and R.-T.J.; investigation, Y.-S.L.; resources, W.-H.C. and Y.-S.L.; data curation, Y.-S.L.; writing--original draft preparation, W.-H.C.; writing--review and editing, W.-H.C. and R.-T.J.; visualization, W.-H.C.; supervision, R.-T.J.; project administration, W.-H.C.; funding acquisition, W.-H.C. and Y.-S.L. All authors have read and agreed to the published version of the manuscript.  
This work cooperated with the Automotive Research and Testing Center under grant number 111-EC-17-A-25-1588 in the Intelligent Vehicle Technology and Autonomous Driving System Development Project (1/4) of the Ministry of Economic Affairs, Taiwan ROC.  
The authors declare no conflict of interest.