Wheel Hop Detection in High-Torque Vehicles: A Machine Learning Approach with Focus on Data Coverage and Model Generalization

Wheel hop is a vibration phenomenon that occurs in cars with a high torque during high accelerations from low speed. Drivers perceive wheel hop as vertical oscillations in the vehicle frame. In severe cases, it can lead to component damage or deformation. Therefore, the affected vehicles must be safeguarded against these vibrations through safe component design and additional software-based functions. Conventional software-based solutions such as the traction control system (TCS) are not explicitly designed to prevent wheel hop. These systems often intervene too late and apply abrupt torque adjustments that compromise driving comfort. Based on observations on real measurements the TCS intervention in some cases leads to an amplification of the wheel hop effect. To address these challenges, we propose a novel approach based on machine learning (ML) for the early detection of wheel hop by leveraging real-time measurements from a rear-wheel drive vehicle. Unlike conventional methods, our ML model precisely detects the start of the wheel hop, enabling proactive torque adjustments and achieving a better balance between performance and comfort. In accordance with recent AI standardization efforts in the automotive domain, we take into account concepts provided in ISO PAS 8800. This includes defining clear and measurable requirements for the ML model, such as robustness and accuracy. Furthermore, we focus on the dataset design by considering safety-related properties and edge cases to support reliable model training and validation. To enhance data coverage, we employ a distance metric to quantify the similarity between multiple multivariate time series.