Predictive Maintenance in Autonomous Vehicles Using Machine Learning Techniques

Predictive maintenance is revolutionizing the management of autonomous vehicles by proactively addressing potential component failures before they occur. This paper presents a predictive maintenance approach using machine learning algorithms to anticipate potential vehicle component failures. By analyzing sensor data, this study aims to reduce vehicle downtime and maintenance costs. The implemented methods include data preprocessing, feature selection, and model training through machine learning techniques such as neural networks and regression models. The results indicate significant accuracy in predicting component failures, supporting proactive maintenance strategies. This study’s findings highlight the model’s potential to enhance operational efficiency in autonomous vehicle systems. Future work may focus on optimizing predictive algorithms to accommodate real-time data processing in dynamic driving environments. These advancements promise to further enhance the capabilities of predictive maintenance systems, ensuring the continued reliability and efficiency of autonomous vehicles.