ODD-based Health Monitoring and Predictive Maintenance of Degrading Vehicle Functionality

Abstract

The shock absorber of a vehicle is not only needed for a comfortable driving experience, but it is also essential for vehicle safety. Especially in autonomous driving, vehicles must monitor themselves and schedule maintenance before a component fails. In this work, we develop a methodology to predict the degradation of a shock absorber using machine learning methods and perform predictive maintenance recommendations. In the first step, we learn the damping coefficient from acceleration data using a neural network. Afterward, we extrapolate this value to predict future behavior. For this, we use the concept of operational design domains to formalize the point up until vehicle functionality is unrestricted and there is no risk to vehicle safety.