Machine learning applications in automotive engineering: Enhancing vehicle safety and performance

Abstract

In recent years, the automotive industry has witnessed a significant paradigm shift with the integration of Machine Learning (ML) techniques into various aspects of vehicle design and operation. This paper explores the burgeoning field of ML applications in automotive engineering, particularly focusing on its role in augmenting vehicle safety and performance. ML algorithms, powered by advancements in data analytics and computational capabilities, offer unprecedented opportunities to enhance traditional automotive systems. From predictive maintenance to autonomous driving, ML techniques enable vehicles to perceive, interpret, and respond to complex real-world scenarios with remarkable precision and efficiency. This paper provides an overview of key ML applications in automotive safety, including collision avoidance systems, adaptive cruise control, and driver monitoring. Furthermore, it examines how ML algorithms contribute to optimizing vehicle performance through predictive modeling, fuel efficiency optimization, and dynamic vehicle control. Moreover, the challenges and future prospects of integrating ML into automotive engineering are discussed. These include issues related to data quality, model interpretability, and regulatory standards. Despite these challenges, the rapid advancements in ML technology hold immense promise for revolutionizing the automotive industry, paving the way for safer, more efficient, and intelligent vehicles of the future.