Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN

Tire-road friction coefficient information is an essential factor in the driving stability and safety of a vehicle. In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. However, since tire vibration characteristics vary depending on conditions such as tire pressure, load, and driving status, it is still difficult to develop a road surface classification algorithm that is robust to various situations. To overcome this limitation, this paper proposes a road surface classification algorithm using a one-dimensional convolutional neural network (CNN) based on acceleration signals obtained through an intelligent tire sensor attached inside the tire. Moreover, a time series data augmentation method is applied to ensure that the learning network has the robustness to perform well under different tires and driving conditions than that in the training dataset. A road surface classification algorithm is trained using a dataset of accelerations measured on dry asphalt, wet asphalt, and basalt tile roads, and the performance of the trained algorithm is validated through test scenarios considering different tire conditions and vehicle types. Furthermore, the performance of different CNN architectures is compared and the algorithm with the best performance is suggested. The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.