CNN-Based Road Event Detection Using Multiaxial Vibration and Acceleration Signals

Road event detection plays a key role in tasks such as monitoring, anomaly identification, and urban traffic optimization. Conventional methods often rely on feature extraction and classification or classical machine learning models, which may struggle when processing high-frequency signals in real time. In this work, we propose a CNN-based classification approach designed to handle multi-axial acceleration and vibration signals captured from road scenarios. Instead of relying on static feature sets, our method leverages a convolutional neural network architecture capable of automatically learning discriminative patterns from raw sensor data. We structure the time-series input into temporal windows and use it to train models that can identify different event categories, including “Speed Bumps”, “Potholes”, and “Sudden Braking” events. The experimental results show that our approach achieves an accuracy of 93.51%, with a precision of 93.56% and a recall of 93.51%. Further, the average AUC score of 0.9855 confirms the strong discriminative power of our proposal. In contrast to rule-based methods, which require frequent tuning to adapt to new datasets, our approach generalizes better across different road conditions and offers a practical alternative for real-time deployment in dynamic environments, outperforming rule-based approaches by over 10% in F1-score, while preserving deployment efficiency on embedded hardware.