Classification of the Condition of Pavement with the Use of Machine Learning Methods

Abstract The publication includes a review of information on the methods of pavement condition recognition using various methods. Measurement system has been presented that allows to determine the condition of the pavement using the Inertial Measurement Unit (IMU) and machine learning methods. Three machine learning methods were considered: random forest, gradient boosted tree and custom architecture neural network (roadNet). Due to the developed system the set of learning and validation data was created on 3 vehicles: Opel Corsa, Honda Accord, Volkswagen Passat. All of the listed vehicles have front wheel drive. The presented machine learning methods have been compared with each other. The best accuracy on the validation set was achieved by the artificial neural network (ANN). The study showed that asphalt condition classification is possible and the developed system fulfils its task.