A Critical Review on the Use of Artificial Intelligence in the Automotive Industry
A Critical Review on the Use of Artificial Intelligence in the Automotive Industry |
||
![]() |
![]() |
|
© 2025 by IJETT Journal | ||
Volume-73 Issue-6 |
||
Year of Publication : 2025 | ||
Author : Mbatha Abednigo Jabu, AA Alugongo, and NZ Nkomo | ||
DOI : 10.14445/22315381/IJETT-V73I6P136 |
How to Cite?
Mbatha Abednigo Jabu, AA Alugongo, and NZ Nkomo, "A Critical Review on the Use of Artificial Intelligence in the Automotive Industry," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.450-456, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P136
Abstract
Artificial Intelligence has been used as an effective approach to processing data because of the growing volume of data and calls to speed up its processing. Artificial Intelligence has the potential to transform the automotive industry like no other technology, and it is gaining an increasingly important role in the current automotive industry. Due to this, automobiles have an increasing number of sensors installed in them, turning them into machines that can gather, process, and display data in real-time. The convenience that automobiles provide for individuals on their trips, the designs of the vehicles themselves, and, most importantly these days, the technology they offer have made the automotive business one of the most well-liked sectors in our society. Additionally, as technology advances, technical malfunctions become more common. As a result, testing has become a crucial step in the production of any vehicle. As a result, businesses have begun investing in automated testing to cut down on both labor costs and long-term expenses. This paper reviews the use of artificial intelligence in the automotive industry and its application, limitations, and potential use. Furthermore, this paper discusses machine learning and deep learning technology used. There is a need to create efficient ways to process data. Further research is still needed to make artificial intelligence as advanced as the human mind and solve complex problems.
Keywords
Artificial Intelligence, Automotive industry, Deep Learning, Machine learning, Industry 4.0.
References
[1] Muhamed Ćosić, Vehbi Ramaj, and Rudolf Petrušić, “Application of Artificial Intelligence in the Automotive Industry,” 2023.
[Google Scholar]
[2] Bernard Marr, Understanding the 4 Types of Artificial Intelligence (AI), LinkedIn, 2021. [Online]. Available: https://www.linkedin.com/pulse/understanding-4-types-artificial-intelligence-ai-bernard-marr
[3] Ali Tarab Rizvi et al., “Artificial Intelligence (AI) and its Applications in Indian Manufacturing: A Review,” Current Advances in Mechanical Engineering, Singapore, pp. 825-835, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Bruce Weindelt, “Digital Transformation of Industries: Automotive Industry,” World Economic Forum in Collaboration with Accenture, vol. 4, 2016.
[Google Scholar]
[5] E.S. Soegoto, R.D. Utami, and Y.A. Hermawan, “Influence of Artificial Intelligence in Automotive Industry,” Journal of Physics: Conference Series, vol. 1402, no. 6, pp. 1-5, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Martin Hofmann, Florian Neukart, and Thomas Bäck, “Artificial Intelligence and Data Science in the Automotive Industry,” arXiv Preprint, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] S. Meenakshi Ammal, M. Kathiresh, and R. Neelaveni, Artificial Intelligence and Sensor Technology in the Automotive Industry: An Overview, Automotive Embedded Systems: Key Technologies, Innovations, and Applications, Springer, pp. 145-164, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Nino Adamashvili, and Mariantonietta Fiore, “Investigating The Role of Business Marketing Techniques in Sales Process,” European Journal of Management Issues, vol. 25, no. 3-4, pp. 135-143, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Stuart J. Russell, and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 2016.
[Google Scholar] [Publisher Link]
[10] Brianna Richardson, and Juan E. Gilbert, “A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions,” arXiv Preprint, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Eric Forcael et al., “Construction 4.0: A Literature Review,” Sustainability, vol. 12, no. 22, pp. 1-28, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Vishnuprasad V. Prabhakar, C.S. Belarmin Xavier, and K.M. Abubeker, “A Review on Challenges and Solutions in the Implementation of AI, IoT and Blockchain in Construction Industry,” Materials Today: Proceedings, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Andre Luckow et al., “Deep Learning in the Automotive Industry: Applications and Tools,” IEEE International Conference on Big Data (Big Data), Washington, DC, USA, pp. 3759-3768, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] François Chollet and J.J. Allaire, Deep Learning with R. Greenwich, CT, Manning Publications Co, USA, 2018.
[Google Scholar] [Publisher Link]
[15] Jason P.C. Chiu, and Eric Nichols, “Named Entity Recognition with Bidirectional LSTM-CNNs,” Transactions of the Association for Computational Linguistics, vol. 4, pp. 357-370, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[16] James Hammerton, “Named Entity Recognition with Long Short-Term Memory,” Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, Edmonton Canada , vol. 4, pp. 172-175, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Sepp Hochreiter, and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Pierre Thodoroff, Joelle Pineau, and Andrew Lim, “Learning Robust Features using Deep Learning for Automatic Seizure Detection,” Machine Learning for Healthcare Conference, vol. 56, pp. 178-190, 2016.
[Google Scholar] [Publisher Link]
[19] Trevor Hastie, Jerome Friedman, and Robert Tibshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, New York, vol. 2, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Aditya Vishwakarma, A Review: Machine Learning Algorithms, Data Science: Practical Approach with Python & R, IIP Series, vol. 3, pp. 162-175, 2024.
[CrossRef] [Publisher Link]
[21] Issam El Naqa, and Martin J. Murphy, What is Machine Learning?, Springer, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Thorsten Wuest et al., “Machine Learning in Manufacturing: Advantages, Challenges, and Applications,” Production Manufacturing Research, vol. 4, no. 1, pp. 23-45, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Gülser Köksal, Inci Batmaz, and Murat Caner Testik, “A Review of Data Mining Applications for Quality Improvement in Manufacturing Industry,” Expert Systems with Applications, vol. 38, no. 10, pp. 13448-13467, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Doh-Soon Kwak, and Kwang-Jae Kim, “A Data Mining Approach Considering Missing Values for the Optimization of Semiconductor-Manufacturing Processes,” Expert Systems with Applications, vol. 39, no. 3, pp. 2590-2596, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Dongil Kim et al., “Machine Learning-Based Novelty Detection for Faulty Wafer Detection in Semiconductor Manufacturing,” Expert Systems with Applications, vol. 39, no. 4, pp. 4075-4083, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Gian Antonio Susto et al., “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach,” IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 812-820, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[27] T. Mitchell et al., “Machine Learning,” Annual Review of Computer Science, vol. 4, no. 1, pp. 417-433, 1990.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Rashad A.R. Bantan et al., “Discrimination of Sunflower Seeds using Multispectral and Texture Dataset in Combination with Region Selection and Supervised Classification Methods,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 11, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Raúl Rojas, Unsupervised Learning and Clustering Algorithms, Neural Networks: A Systematic Introduction, pp. 99-121, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Samreen Naeem et al., “An Unsupervised Machine Learning Algorithms: Comprehensive Review,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 911-921, 2023. [CrossRef] [Google Scholar] [Publisher Link] [31] “Semi-Supervised Learning,” Debategraph, vol. 5, no. 2, 2006.
[Google Scholar] [Publisher Link]
[32] Damien Ernst, and Arthur Louette, “Introduction to Reinforcement Learning,” University of Liège, 2024.
[Google Scholar] [Publisher Link]
[33] Rich Caruana, “Multitask Learning,” Machine Learning, vol. 28, no. 1, pp. 41-75, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Zhi-Hua Zhou, Ensemble Methods: Foundations and Algorithms, 1st ed., CRC press, 2012.
[Google Scholar] [Publisher Link]
[35] Cleotilde Gonzalez, Javier F. Lerch, and Christian Lebiere, “Instance‐Based Learning in Dynamic Decision Making,” Cognitive Science, vol. 27, no. 4, pp. 591-635, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Cleotilde Gonzalez, Javier F. Lerch, and Christian Lebiere, “Instance-Based Decision Making Model of Repeated Binary Choice,” Proceedings of the 8th international Conference on Cognitive Modeling, Ann Arbor, Michigan, pp. 67-72, 2007.
[Google Scholar] [Publisher Link]
[37] Cleotilde Gonzalez, and Christian Lebiere, “Instance-Based Cognitive Models of Decision-Making,” 2005.
[Google Scholar]
[38] Michael K. Martin, Cleotilde Gonzalez, and Christian Lebiere, Learning to Make Decisions in Dynamic Environments: ACT-R Plays the Beer Game, 1st ed., Sixth International Conference on Cognitive Modeling, Psychology Press, pp. 178-183, 2004.
[Google Scholar] [Publisher Link]
[39] Jeffrey De Fauw et al., “Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease,” Nature Medicine, vol. 24, no. 9, pp. 1342-1350, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Huimin Lu et al., “Brain Intelligence : Go Beyond Artificial Intelligence,” Mobile Networks and Application, vol. 23, no. 2, pp. 368-375, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Manu Sharma et al., “Implementing Challenges of Artificial Intelligence: Evidence from Public Manufacturing Sector of an Emerging Economy,” Government Information Quarterly, vol. 39, no. 4, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Keliang Zhou, Taigang Liu, and Lifeng Zhou, “Industry 4.0: Towards Future Industrial Opportunities and Challenges,” 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, pp. 2147-2152, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Wesam Salah Alaloul et al., “Industrial Revolution 4.0 in the Construction Industry: Challenges and Opportunities for Stakeholders,” Ain Shams Engineering Journal, vol. 11, no. 1, pp. 225-230, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Thuy Duong Oesterreich, and Frank Teuteberg, “Understanding the Implications of Digitisation and Automation in the Context of Industry 4.0: A triangulation Approach and Elements of a Research Agenda for the Construction Industry,” Computers in Industry, vol. 83, pp. 121-139, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[45] R.A. Mashelkar, “Exponential Technology, Industry 4.0 and Future of Jobs in India,” Review of Market Integration, vol. 10, no. 2, pp. 138-157, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[46] Arka Ghosh, David John Edwards, and M. Reza Hosseini, “Patterns and Trends in Internet of Things (IoT) Research: Future Applications in the Construction Industry,” Engineering, Construction and Architectural Management, vol. 28, no. 2, pp. 457-481, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Nathan Melenbrink, Justin Werfel, and Achim Menges, “On-Site Autonomous Construction Robots: Towards Unsupervised Building,” Automation in Construction, vol. 119, 2020.
[CrossRef] [Google Scholar] [Publisher Link]