**7. Conclusions**

In this paper, a method for abnormal road surface recognition using a smartphone acceleration sensor is proposed. The Gaussian background model is optimized by a fuzzy logic inference machine so that the road surface recognition algorithm can be applied to different types of vehicles. Vehicle acceleration, speed, and position data are collected by the built-in acceleration sensor and global positioning navigation system of the smartphone. The vibration acceleration caused by the abnormal road surface is extracted using the improved Gaussian background model and the Z-axis acceleration threshold condition. An adaptive adjustment mechanism based on vehicle speed is proposed to improve the recognition accuracy. The classification of the abnormal road surface is realized by utilizing the kNN classification algorithm. Multiple sets of samples are used to test the abnormal road surface identification method. Comparing the algorithm identification results with the artificial site survey results, it is found that the proposed method can effectively identify and classify abnormal road surfaces such as potholes and bumps.

It is worth noting that with the increase of the total mileage of the road, the intelligent transportation system will be more and more widely used in the transportation industry. In this paper, only the two main types of the abnormal road surface are identified. The next step would be studying the evaluation and identification methods of the degree and size of abnormal road surface damage.

**Author Contributions:** R.D. and G.Q. conceived the idea; K.G. and G.Q. designed the experiments and analyzed the data; G.Q. implemented the experiments and wrote the manuscript; L.H. and L.L. helped with the simulation. R.D. and K.G. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, gran<sup>t</sup> number 61973047, 51678077, 51875049, and also by the Science Fund for Distinguished Young Scholars of the Hunan Province (2019JJ20017). The project was supported by the Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (kfj190701) (Changsha University of Science & Technology).

**Acknowledgments:** The authors would like to thank Changsha Intelligent Driving Research Institute for their help in the experiments.

**Conflicts of Interest:** The authors declare no conflict of interest.
