## 7 Conclusions and Future Work  
This study presented a literature review of vibration-based techniques for detecting and classifying road surface anomalies. This work's findings show that vibration-based road surface anomaly detection and classification methods can be classified into three main approaches: threshold, feature extraction, and deep learning. In general, the problem of detecting and recognizing road surface anomalies has achieved relatively high performance by employing each of the three methods. However, a lack of homogeneity between the datasets, the types of anomalies analyzed, and the road scenarios complicate realizing a homogeneous comparison between the approaches.  
The feature extraction techniques used in road anomaly classification were also surveyed. It was observed that common analysis techniques employed for feature engineering are time-domain, frequency-domain, and time-frequency representations. However, from these feature extraction approaches, there is no exact preference for a particular method or standardization of features that assures adequate performance to detect or classify specific road anomalies.  
Considering the above, the following points are identified as potential future research developments for vibration-based methods used in road surface anomaly detection and classification:  
* The generation of datasets that are publicly available could facilitate the reproduction of the studies and allow for the creation of benchmark metrics that could be used for the comparison and testing of different feature extraction methods or machine learning algorithms. The above could also facilitate a homogeneous comparison of the literature results.
* The Transfer Learning framework could potentially avoid requiring a large sample size and take advantage of deep learning processing capabilities, such as CNNs for signal classification (i.e., accelerometer and gyroscope data categorization into road surface anomalies) [73].
* An analysis and comparison could be performed to determine the set of features computed through either the time or frequency-domain associated with each surface road anomaly, such as potholes, speed bumps, metal bumps, cracks, road joints, or manholes. This could lead to a standardization of features that could help developers generate these road anomaly recognition and classification systems.
* Time-frequency methods, despite the fact that they have already been used in state of the art for inertial sensor signals representations and feature extraction, future developments could explore testing different wavelets families, parametrizations of time-frequency representations, or different sets of time-frequency analysis techniques, such as the wavelet transform, Wigner-Ville distribution, or Hilbert-Huang transform [84].
* Characterization of road anomalies, such as the speed bumps' state or the potholes' depth, has not been performed extensively as suggested by [PERSON] et al. [17]. Hence, the opportunity to test algorithms that can estimate the depth of potholes through regression algorithms or classify the quality of speed bumps through statistical or machine learning techniques remains to be explored.  
Conceptualization, E.A.M.-R.; methodology, E.A.M.-R; validation, E.A.M.-R; formal analysis, E.A.M.-R.; investigation, E.A.M.-R.; resources, M.R.B.-B; data curation, E.A.M.-R.; writing--original draft preparation, E.A.M.-R; writing--review and editing, E.A.M.-R. and L.A.A.-S.; visualization, E.A.M.-R.; supervision, M.R.B.-B.; project administration, E.A.M.-R. and M.R.B.-B.; funding acquisition, M.R.B.-B. All authors have read and agreed to the published version of the manuscript.  
Tecnologico de Monterrey (Grant No. A01331212) and the National Council for Science and Technology (CONACYT) grant number 1010770 funded this research.  
Not applicable.  
The work of [PERSON] was supported by a scholarship awarded by Tecnologico de Monterrey and Consejo Nacional de Ciencia y Tecnologia (CVU: 1010770).  
The authors declare no conflict of interest.