## 3 Road Anomaly Detection and Classification Approaches through Vibration-Based Techniques
### Threshold-Based Methods  
Threshold-based methods try to detect and classify road anomalies when a change in the amplitude, root mean square, or crest factor of the signal acquired from inertial sensors exceeds a certain predefined value [26]. Regarding this type of methodology, early studies, such as that of [PERSON] et al. [27] proposed to detect potholes and speed bumps by analyzing the extreme peaks of the \(z\)-axis of accelerometer data where the accuracy in detecting the speed bump was 90%, and the detection rate of potholes was 65%. [PERSON] et al. [28] proposed a threshold approach based on the analysis of the \(z\)-axis of the accelerometer data to measure the severity of bumps and potholes into three levels average severity, high severity, and very high severity. The thresholds were set empirically, and the method reported an accuracy of 93.75%.  
In addition, [PERSON] et al. [22] applied the Grubbs test on a sliding window to improve the threshold methods initially proposed by [PERSON] et al. [29]. These algorithms were the Z-THRESH, Z-DIFF, STDEV(Z), and G-ZERO. Z-THRESH aims to identify the anomaly if the amplitude of the \(z\)-axis of the accelerometer exceeds a specific value. Z-DIFF detects the anomaly if the difference between two successive measurements is more significant than a specific value. Furthermore, STDEV(Z) is related to the standard deviation of the sliding window; if the standard deviation surpasses a specific threshold, the anomaly is recognized. Finally, G-ZERO identifies the anomaly if the values in the three axes of the accelerometer are below a particular value [22; 29]. [PERSON] et al. [14] also evaluated the thresholds proposed by [PERSON]'s study; his analysis showed that STDEV(Z) achieved the best results compared to G-ZERO, Z-DIFF, Z-THRESH, and support vector machines in terms of sensitivity, precision, and F1-score.  
Other studies have also explored the combination of threshold-based techniques with learning-based techniques. For instance, [PERSON] et al. [21] proposed a threshold technique to identify where there might be an anomaly with a sliding window method. From this first detection, a random forest was used to filter out the window segments been actually normal from the segments that had anomalies. Finally, dynamic time wrapping classified the set of identified anomalies into potholes, speed bumps, and metal pumps. [PERSON] et al. [11] presented a similar approach, which consisted of employing the threshold method proposed by [PERSON] et al. [30] and a Gaussian Mixture Model (GMM) to detect the road surface anomaly.  
Finally, in Ref. [23], a querying-based road anomaly detection algorithm is proposed that takes advantage of self-similarity. This algorithm consists of two stages: first, the road anomaly is extracted by matching it with existing labeled anomalies; second, a re-comparison is made on suspicious road anomalies to classify the type of road anomaly (i.e., potholes, speed bumps, and metal pumps). The query algorithm is based on threshold values. A summary of the reference studies that have used threshold algorithms and their performance is presented in Table 3.  
Based on the referred studies, it is appreciated that threshold-based techniques can achieve relatively high accuracy. In addition, it can be possible to combine threshold and machine learning techniques to improve or make a more robust algorithm. However, this method may require calibration since the threshold values are set empirically and may lack reproducibility, as pointed out by [PERSON] et al. [31]. In addition, thresholds are susceptible to noise and can only detect a single anomaly [22; 32]. Therefore, the technique's usefulness in different road scenarios could lead to underperformance in the detection and classification capability of the algorithms. However, the above could potentially be countered with dynamic thresholds instead of only a static threshold, as suggested in Ref. [10].