# On Training Road Surface Classifiers by Data Augmentation
## 5 Discussion and Conclusions  
It has been demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training road surface classifiers for automatic power-assisted steering. In the experiments included in this work, a reduction by an approximate factor of 1.5 has been shown when using the LDA classifier and GANSO for data augmentation. The reduction factor of \(TSS\) directly applies to the reduction of the signal acquisition time and of the number of captures. However, it is hard to quantify the impact of the \(TSS\) reduction on the overall time saving of the whole capture experiment. Several other factors, such as the number of different configurations and the time required to change the configuration setting, have to be considered, among others.  
Several issues are open in any of the basic blocks of the multisensory road surface classification system regarding the actual reduction that could be achieved. Thus, for  
Figure 5: Effective training set size (\(ETSS\)) for a given training set size (\(TSS\)), with data augmentation (RDF-GANSO, LDA-GANSO) and with no data augmentation.  
example, a different accuracy is obtained when using RDF instead of LDA, and other types of classifier could be tested. In any case, notice that the aim of this communication is to show that data augmentation can provide significant simplification of the tedious capture of real data, rather than to compare different options of the classification method. Definitely, given the practical focus of the work, LDA is the most attractive alternative.  
Other elements of the approach deserve further research. For example, the sensor configuration has a variety of possibilities. In particular, the inclusion of sensors (e.g., microphones) that are not standard elements of the car is an issue. Furthermore, other features could be tried, as well as different procedures for feature selection. Finally, different strategies of data augmentation are possible besides duplicating the original \(TS\).  
In conclusion, the presented communication motivates future research in both theoretical and experimental aspects of road surface classification to aid the driving assistance systems of the car by automatic power-assisted steering. The presented results should encourage researchers to keep on working on data augmentation to ease the tedious and costly capture of the training dataset.  
Conceptualization, A.S., A.R. and L.V.; methodology, A.S., A.R. and L.V.; software, A.S and N.V.; validation, A.S., A.R. and L.V.; formal analysis, A.S., A.R. and L.V.; investigation, A.S., A.R. and L.V.; resources, A.R.; data curation, N.V.; writing--original draft preparation, A.S. and L.V.; writing--review and editing, A.S., N.V. and L.V.; supervision, L.V.; project administration, L.V.; funding acquisition, L.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MCIN/AEI/10.13039/501100011033 and by the European Union, grant number TEC2017-84743-P.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.