## 1 Introduction
However, as pointed out previously, some disadvantages mitigate its use, such as different sensor properties, smartphone or sensor placement within the vehicle, and diverse vehicle mechanical characteristics [11]. Hence, the above drawbacks have the potential to be addressed with further research. Previous studies have made literature reviews or surveys focused on road surface anomaly detection. For example, [PERSON] et al. [16] reviewed pot \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{p{113.8 pt}}{**Road Anomaly Detection Method**} & \multicolumn{1}{c}{**Advantages**} & \multicolumn{1}{c}{**Disadvantages**} \\ \hline \multirow{5}{*}{Vision-based} & \(\bullet\) Can be useful to determine the dimension of the anomaly. \(\bullet\) Can be useful to determine the number of anomalies. \(\bullet\) It is less expensive compared to 3D reconstruction. & \(\bullet\) It is affected by light and shadows. \(\bullet\) It cannot determine precisely the depth and shape of the anomaly compared to 3D reconstruction. & \(\bullet\) It can be affected by the position and type of vehicle used. \(\bullet\) It is complicated to determine the shape and depth of the anomaly. \(\bullet\) It is necessary to pass over the anomaly. \\ \hline \multirow{3}{*}{Vibration-based} & \(\bullet\) It can measure the shape and depth of the anomaly more precisely than the other techniques. & \(\bullet\) It can be affected by the position and type of vehicle used. \(\bullet\) It is complicated to determine the shape and depth of the anomaly. \(\bullet\) It is necessary to pass over the anomaly. \\ \cline{2-3}  & \(\bullet\) It can measure the shape and depth of the anomaly more precisely than the other techniques. & \(\bullet\) Expensive method compared to vibration and vision techniques. \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of road surface anomaly detection and classification techniques [11; 16]. which vibration, vision, and 3D reconstruction methods were discussed and compared. [PERSON] et al. [20] also presented a similar review in which the strengths and limitations of deep learning techniques and non-deep learning techniques for detecting damaged road surfaces are presented. Furthermore, in [PERSON]'s study, vision-based procedures are extensively reviewed using deep learning techniques and non-deep learning technologies. However, vibration-based technologies have been reviewed to a lesser extent. [PERSON] et al. [10] present a literature review focused on detecting anomalies through smartphone sensors, such as accelerometer and gyroscope data, with particular attention to the threshold, machine learning, and dynamic time warping methods. However, no review has been conducted that presents a deeper explanation of vibration-based techniques for road surface anomaly detection and classification that expands on the feature engineering methods that have been used and the areas of opportunity that can be fulfilled to improve the performance of learning-based techniques. This narrative literature review aims to provide a detailed presentation and discussion of approaches based on the vibration-based methods used to detect and classify anomalies on the road surface. The proposed categorization of vibration-based techniques is based on threshold, feature extraction, and deep learning techniques. A review of the datasets, sensors, preprocessing steps, and feature engineering methods (i.e., time analysis, frequency analysis, and time-frequency analysis) is also provided. The remainder of this study is organized as follows. Section 2 presents the search strategy used in the present narrative literature review. Section 3 presents the background of road anomaly detection and classification based on vibration-based techniques. Section 4 presents the datasets, sensors, and the preprocessing steps used for road surface anomaly detection and classification.