Niresh Jayarajan, PhD – Senior Assistant Professor, Department of Automobile Engineering, PSG College of Technology, Coimbatore, India
S. Neelakrishnan, PhD – Professor & Head, Department of Automobile Engineering, PSG College of Technology, Coimbatore, India
Archana Naganthan, PhD – Assistant Professor, Department of Automobile Engineering, PSG College of Technology, Coimbatore, India
Tan Wei Hong, PhD – Professor, Mechanical Engineering Programme, Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia
Tamilselvan Ganesan, PhD – Assistant Professor, Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India

Series: Business, Technology and Finance
BISAC: TEC009090; COM004000; TEC037000
DOI: https://doi.org/10.52305/NLEG1833

Driving Innovation with AI, Data Analytics, and Digitization in the Automotive Industry examines a rapidly evolving landscape filled with challenges and opportunities that are reshaping the automotive sector. Advancements in artificial intelligence and data-driven technologies are transforming how vehicles are designed, manufactured, and operated, with the potential to significantly enhance safety, performance, and efficiency. These innovations enable breakthroughs in autonomous driving systems, predictive maintenance, and optimized supply chain logistics, all of which contribute to improved vehicle reliability and customer satisfaction. As the industry embraces this digital transformation, a profound paradigm shift is underway—one that emphasizes sustainability, energy efficiency, and environmentally responsible practices. This shift not only supports global sustainability goals but also provides automotive companies with a competitive advantage in an increasingly connected and technology-driven market. The integration of cutting-edge technologies such as blockchain and the Internet of Things (IoT) further strengthens data security, accelerates real-time analytics, and enriches user experiences. Meanwhile, quantum algorithms enhance the capabilities of Advanced Driver Assistance Systems (ADAS), improving critical safety features and elevating the overall driving experience. Machine learning models also play a crucial role by enhancing predictive maintenance strategies through early detection of potential issues, thereby reducing downtime and optimizing vehicle performance. Collectively, these advancements signal the emergence of a more intelligent, sustainable, and interconnected transportation ecosystem—one defined by continuous innovation and the seamless integration of digital technologies to meet the evolving demands of modern mobility.

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