Abstract
Accurate road surface monitoring is essential for ensuring vehicle and pedestrian safety, and it relies on robust data acquisition and analysis methods. This study examines the classification of road surface conditions using single- and multi-head deep learning architectures, specifically Convolutional Neural Networks (CNNs) and CNNs combined with Long Short-Term Memory (LSTM) layers, applied to data from Inertial Measurement Units (IMUs) mounted on vehicle's sprung and unsprung masses. Various model architectures were tested, incorporating IMU data from different positions and utilizing both acceleration and angular velocity features. A grid search was conducted to fine-tune the architectures' hyperparameters, including the number of filters, kernel sizes, and LSTM units. Results show that CNN+LSTM models generally outperformed CNN-only models. The highest-performing model, which used data from three IMUs in a single-head architecture, achieved a macro F1-score of 0.9338. The study highlights the effectiveness of combining IMU data in a single-head architecture and suggests that further improvements in classification accuracy can be achieved by refining the architectures and enhancing the dataset, particularly for more challenging road surface classes.