Revolutionizing Mobility: The Integration of Machine Learning and Artificial Intelligence in the Automotive Industry
| International Journal of Electrical and Electronics Engineering |
| © 2025 by SSRG - IJEEE Journal |
| Volume 12 Issue 5 |
| Year of Publication : 2025 |
| Authors : Mahendra Deore, Ravikant Suryawanshi, Sandeep Musale |
10.14445/23488379/IJEEE-V12I5P126
How to Cite?
Mahendra Deore, Ravikant Suryawanshi, Sandeep Musale, "Revolutionizing Mobility: The Integration of Machine Learning and Artificial Intelligence in the Automotive Industry," SSRG International Journal of Electrical and Electronics Engineering, vol. 12, no. 5, pp. 342-352, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I5P126
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. High performance 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.
Keywords:
Adaptive cruise control, Autonomous driving, Machine learning, Sensor data, V2X communication.
References:
[1] E.S. Soegoto, R.D. Utami, and Y.A. Hermawan, “Influence of Artificial Intelligence in Automotive Industry,” Journal of Physics: Conference Series, vol. 2019, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Valentyna Cherviakova, and Tetiana Cherviakova, “Value Opportunities for Automotive Manufacturers in Conditions of Digital Transformation of the Automotive Industry,” Journal of Applied Economic Sciences, no. 62, pp. 2351-2362, 2018.
[Google Scholar] [Publisher Link]
[3] Paola Tubaro, and Antonio A. Casilli, “Micro-Work, Artificial Intelligence and the Automotive Industry,” Journal of Industrial and Business Economics, vol. 46, pp. 333-345, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jilei Tian, Alvin Chin, and Halim Yanikomeroglu, “Connected and Autonomous Driving,” IT Professional, vol. 20, no. 6, pp. 31-34, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Nikolai Mayer, S. Jimmy Gandhi, and Dirk Hecht, “AN Understanding of Artificial Intelligence Applications in the Automotive Industry Value Chain,” International Annual Conference Proceedings of the American Society for Engineering Management and 40th Meeting Celebration: A Systems Approach to Engineering Management Solutions, 2019.
[Google Scholar]
[6] Micheal Omotayo Alabi, Ken Nixon, and Ionel Botef, “A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry,” American Journal of Engineering and Applied Sciences, vol. 11, no. 3, pp. 1114-1124, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ana L. Ballinas-Hern´andez, Ivan Olmos-Pineda, and J. Arturo Olvera-L´opez, “Speed Bump Detection on Roads using Artificial Vision,” Research in Computing Science, vol. 148, no. 9, pp. 71-82, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Franja Z. Benčić et al., “Trends in AI based Automotive Industry using Patent Analysis,” 26th Telecommunications forum TELFOR, pp. 1-4, 2018.
[Google Scholar]
[9] Terry Hayhoe et al., “Sustainable Manufacturing in Industry 4.0: Cross-Sector Networks of Multiple Supply Chains, Cyber-Physical Production Systems, and AI-Driven Decision-Making,” Journal of Self-Governance and Management Economics, vol. 7, no. 2, pp. 31 36, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Liviu A. Marina, Bogdan Trasnea, and Sorin M. Grigorescu, “A Multi-Platform Framework for Artificial Intelligence Engines in Automotive Systems,” 22nd International Conference on System Theory, Control and Computing, Sinaia, Romania, pp. 559-564, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Dominique Knittel, Hamid Makich, and Mohammed Nouari, “Milling Diagnosis Using Artificial Intelligence Approaches,” Mechanics & Industry, pp. 1-9, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Qing Rao, and Jelena Frtunikj, “Deep Learning for Self-Driving Cars,” Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, pp. 35-38, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Richard Jones et al., “Heterogeneously Integrated InP/Silicon Photonics: Fabricating Fully Functional Transceivers,” IEEE Nanotechnology Magazine, vol. 13, no. 2, pp. 17-26, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Qing Rao, and Jelena Frtunikj, “Deep Learning for Self-Driving Cars: Chances and Challenges,” Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, pp. 35-38, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Simon Hagemann, Atakan Sünnetcioglu, and Rainer Stark, “Hybrid Artificial Intelligence System for the Design of Highly-Automated Production Systems,” Procedia Manufacturing, vol. 28, pp. 160-166, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Markus Edwin Schatz, “Enabling Composite Optimization through Soft Computing of Manufacturing Restrictions and Costs Via a Narrow Artificial Intelligence,” Journal of Composites Science, vol. 2, no. 4, pp. 1-16, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] E.N. Smirnov, and S.A. Lukyanov, “Development of the Global Market of Artificial Intelligence Systems,” Economy of Regions, no. 1, pp. 57-69, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Fábio Eid Morooka et al., “Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review,” Machine Learning and Knowledge Extraction, vol. 5, no. 3, pp. 763-781, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Norbert Markó et al., “Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis,” Machines, vol. 11, no. 12, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xu Wang et al., “An Autonomous Vehicle Behavior Decision Method Based on Deep Reinforcement Learning with Hybrid State Space and Driving Risk,” Sensors, vol. 25, no. 3, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Fabio Antonialli, Andrea Martinesco, and Sylvie Mira-Bonnardel, “Artificial Intelligence as a Determinant for Reshaping the Automotive Industry and Urban Mobility Services,” International Journal of Automotive Technology and Management, vol. 22, no. 3, pp. 324-351, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Tai Yoon Chai, and Ismail Nizam, “Impact of Artificial Intelligence in Automotive Industries Transformation,” International Journal of Information System and Engineering, vol. 9, no. 1, pp. 1-35, 2021.
[Google Scholar]
[23] Shuchita Gupta et al., “The Evolution of Artificial Intelligence in the Automotive Industry,” Proceedings - Annual Reliability and Maintainability Symposium, Orlando, FL, USA, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Sayed Suhaib Kamran et al., “Artificial Intelligence and Advanced Materials in Automotive Industry: Potential Applications and Perspectives,” Materials Today: Proceedings, vol. 62, no. 6, pp. 4207-4214, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Sajjad Ahmad Khan, Hyun Jun Lee, and Huhnkuk Lim, “Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach,” Electronics, vol. 12, no. 13, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]