Revolutionizing Mobility: The Integration of Machine Learning and Artificial Intelligence in the Automotive Industry

Abstract

With the use of Artificial Intelligence (AI) technology, the automotive sector is seeing a revolution in vehicle operation, safety, and user experience. A comprehensive architecture outlining the key elements and phases required for integrating AI and ML into automotive systems has been presented in this study. As AI or ML can completely transform vehicle manufacturing, operations, and user experience, the automotive industry stands to benefit greatly from their integration. AI and ML can help automakers create vehicles that are safer, smarter, and more efficient. Data collection using radar, sensors, LiDAR, cameras, and V2X communication is the foundation, followed by data labeling for feature design, guided learning tasks, and even data preprocessing. In this research, predictive maintenance, self-driving vehicles, and improved user experience are just some of the uses for which ML models rooted in CNN and SVM are being built and validated. High­performance metrics demonstrate the value of AI models, such as 95.00% accuracy in automatic control and 0.05 mean absolute error in predictive maintenance. Artificial intelligence has a positive effect on driving, as evidenced by user satisfaction ratings of 4.7 for voice recognition and 4.8 for adaptive cruise control. The findings demonstrate the importance of rigorous data management and model training techniques to ensure the reliability and effectiveness of AI applications in practical contexts. This article highlights the revolutionary potential of AI or ML technologies in improving vehicle performance and customer satisfaction while offering useful insights into their implementation and further development in the automotive sector.