Tire End-of-Life Prediction for Connected Vehicles

Tire wear prediction plays a critical role in ensuring vehicle safety, optimizing tire lifespan, and reducing environmental impact. Traditional methods often rely on specialized hardware sensors or monitoring devices. While effective, these approaches are costly and vendor-locked. This research proposes a purely data-driven approach, leveraging existing sensors of connected vehicles and their historical readouts. By utilizing a machine learning model, the framework identifies patterns and correlations in highly multidimensional data space to estimate tire wear progression and yield end-of-life alerts while requiring only a single initial tread depth measurement. In addition, adopting explainable AI techniques makes it possible to score vehicle users with respect to how adverse to tire longevity their behaviors are and thus open prospects for tire health coaching. The proposed methodology is being integrated into existing connected vehicle platforms to enable proactive maintenance.