# On Training Road Surface Classifiers by Data Augmentation
## 1 Introduction  
### Preliminaries  
Road surface automatic analysis has a long history of developments involving a variety of signal modalities and their corresponding sensors. Most previous works have been centered on estimating road roughness into the levels defined in ISO 8606/1995 or detecting anomalies in the road (potholes, bumps, cracks, etc.) for supervising and planning road maintenance. References [1, 2, 3, 4, 5] are some representative examples. However, during the last few years, the emergence of autonomous or semi-autonomous car technology has strongly increased the interest in on-board systems, which can perform real-time surface classification to aid the driving assistance systems of the car by automatic power-assisted steering. A variety of works have recently been reported in this very active research area [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Most of them propose machine learning approaches, where an automatic classifier is to be trained from labelled feature vectors derived from sensor signals. Several signal modalities, features, and classifiers have been proposed so far, but there is still a long way to reach efficient solutions demonstrating compatibility with commercial constraints of vehicle mass production.  
In the general area of automatic classification, the training set size has been demonstrated to have a large impact on the classifier performance [18, 19, 20, 21]; however, in many application domains, practical constraints prevent an arbitrarily large recording of signals. This is the case in road surface classification. Automatic road surface classifiers must be trained off-line before they can be incorporated into the car. Robust operation of the road surface classifier implies that training must take into account different possible conditions: worm-gear interface configuration, hands on or off the wheel, vehicle speed, kind of vehicle, and road surface classes. Moreover, this training could require updating after some period to account for some corrections or to extend the capabilities of the classifier. However,training is a costly process requiring prepared cars and dedicated closed circuits. Signals recorded during the driving of the car over different types of known surfaces can be labelled to obtain the required dataset for training and testing.  
One possible approach to alleviate the costly capture of real data is data augmentation. Different methods of general applicability have been proposed in the literature, which use synthetic data to augment the real dataset in an effort to achieve an effective increase in the training set size. Two representative reviews in this regard are references [22; 23] and examples of quite different applications are: patent analysis [24], speech processing [25], and medical image analysis [26]. There are a few works devoted to road surfaces where data augmentation has been studied: detection of road surface anomalies (bumps, potholes, etc.) based on the sensor data collected with smartphones [27; 28] and image-based road surface classification [29]. Both works are constrained to the imbalance scenario, where only one class is deficient while the others are abundant.