The demand for several sources of situational data from the traffic environment has intensified in recent years, through the development of applications in intelligent transport systems (ITS), such as autonomous vehicles and advanced driver assistance systems. Among these situational data, the road surface type classification is one of the most important and can be used throughout the ITS domain. However, in order to have a wide application, the development of a safe and reliable model is necessary. Therefore, in addition to the application of safe technology, the model developed must operate correctly in different vehicles, with different driving styles and in different environments in which vehicles can travel to. For this purpose, in this work we collect nine datasets with contextual variations using inertial sensors, represented by accelerometers and gyroscopes. These data were produced in three different vehicles, with three different drivers, in three different environments in which there are three different surface types, in addition to variations in conservation state and presence of obstacles and anomalies, such as speed bumps and potholes. After a pre-processing step, these data were used in 34 different computational models for road surface type classification, employing both Classical Machine Learning and Deep Learning techniques. Through several experiments, we analyze the learning and generalization capacity of each technique. The best model developed was a CNN-based deep neural network, which obtained validation accuracy of 93.17%, classifying surfaces between segments of dirt, cobblestone or asphalt roads.
