A Review on Development of ML for Tire Life Detection

Abstract

This study explores the application of Machine Learning (ML) to predict vehicle tire life by analyzing tire tread wear and key influencing factors. The goal is to develop an ML model that can accurately forecast tire wear based on data from sensors that track tire usage, driving behavior, road conditions, tire pressure, and weather. By using advanced ML techniques, such as neural networks and ensemble methods, the model aims to identify patterns in complex datasets and predict when tires will require maintenance or replacement. This approach offers a more proactive way to manage tire life, reducing the risk of unexpected failures and improving vehicle safety. In addition, it can help optimize tire performance, reduce maintenance costs, and extend the lifespan of tires. The research focuses on creating a reliable tool for the automotive industry to monitor tire conditions, make data-driven decisions, and enhance overall vehicle efficiency.