Underbrake Fault Detection in Automotive Systems using Advanced Neural Network Architectures

Enhancing driving safety systems is a critical objective in the automotive industry, with advanced driver assistance systems and fallback strategies playing essential roles in accident prevention. This paper uses machine learning techniques to investigate underbrake anomalies, i.e., scenarios where braking performance is unexpectedly diminished. A dataset specifically simulating underbrake scenarios was created through Hardware-in-the-Loop (HiL) simulations, replicating real-world driving conditions, including both hydraulic and non-hydraulic braking systems. A systematic comparison of the performance of several neural network architectures, including Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, was performed on this dataset. Results indicate that the LSTM architecture achieves the highest accuracy and F1 scores in detecting anomalous braking behavior, outperforming baseline models. However, further improvements in accuracy, computational efficiency, and generalization are needed for real-world deployment. This study establishes a robust benchmark for underbrake anomaly detection and highlights future directions, including enhanced feature extraction techniques and real-time implementation considerations.