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.
References
Al-Gerafi, M. A., Goswami, S. S., Khan, M. A., Naveed, Q. N., Lasisi, A., AlMohimeed, A., & Elaraby, A. (2024). Designing of an effective e-learning website using inter-valued fuzzy hybrid MCDM concept: A pedagogical approach. Alexandria Engineering Journal, 97, 61-87. https://doi.org/10.1016/j.aej.2024.04.012
Ali, E. S., Hasan, M. K., Hassan, R., Saeed, R. A., Hassan, M. B., Islam, S., Nafi, N. S., & Bevinakoppa, S. (2021). Machine learning technologies for secure vehicular communication in internet of vehicles: recent advances and applications. Security and Communication Networks, 1-23. https://doi.org/10.1155/2021/8868355
Bachute, M. R., & Subhedar, J. M. (2021). Autonomous driving architectures: insights of machine learning and deep learning algorithms. Machine Learning with Applications, 6, 100164. https://doi.org/10.1016/j.mlwa.2021.100164
Borg, M., Henriksson, J., Socha, K., Lennartsson, O., Sonnsjö Lönegren, E., Bui, T., Tomaszewski, P., Sathyamoorthy, S. R., Brink, S., & Moghadam, M. H. (2023). Ergo, SMIRK is safe: a safety case for a machine learning component in a pedestrian automatic emergency brake system. Software Quality Journal, 31(2), 335-403. https://doi.org/10.1007/s11219-022-09613-1
Gao, D., Yao, B., Chang, G., & Li, Q. (2022). Multi-Objective Optimization Design of Vehicle Side Crashworthiness Based on Machine Learning Point-Adding Method. Applied Sciences, 12(20), 10320. https://doi.org/10.3390/app122010320
Ionașcu, A. E., Goswami, S. S., Dănilă, A., Horga, M. G., Barbu, C., & Adrian, Ş. C. (2024). Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach. Mathematics, 12(8), 1157. https://doi.org/10.3390/math12081157
Koopman, P., & Wagner, M. (2017). Autonomous vehicle safety: An interdisciplinary challenge. IEEE Intelligent Transportation Systems Magazine, 9(1), 90-96. https://doi.org/10.1109/MITS.2016.2583491
Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2020). A survey of deep learning applications to autonomous vehicle control. IEEE Transactions on Intelligent Transportation Systems, 22(2), 712-733. https://doi.org/10.1109/TITS.2019.2962338
Malik, M., Nandal, R., Maan, U., & Prabhu, L. (2022). Enhancement in identification of unsafe driving behaviour by blending machine learning and sensors. International Journal of System Assurance Engineering and Management, 1-10. https://doi.org/10.1007/s13198-022-01710-5
Mittal, U. (2023). Detecting Hate Speech Utilizing Deep Convolutional Network and Transformer Models. International Conference on Electrical, Electronics, Communication and Computers, IEEE, 1-4. https://doi.org/10.1109/ELEXCOM58812.2023.10370502
Mittal, U., & Panchal, D. (2023). AI-based evaluation system for supply chain vulnerabilities and resilience amidst external shocks: An empirical approach. Reports in Mechanical Engineering, 4(1), 276-289. https://doi.org/10.31181/rme040122112023m
Mittal, U., Yang, H., Bukkapatnam, S. T., & Barajas, L. G. (2008). Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations. International Conference on Automation Science and Engineering, IEEE, 498-503. https://doi.org/10.1109/COASE.2008.4626530
Naresh, V. S., Ratnakara Rao, G. V., & Prabhakar, D. V. N. (2024). Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1539. https://doi.org/10.1002/widm.1539
Norouzi, A., Heidarifar, H., Borhan, H., Shahbakhti, M., & Koch, C. R. (2023). Integrating machine learning and model predictive control for automotive applications: A review and future directions. Engineering Applications of Artificial Intelligence, 120, 105878. https://doi.org/10.1016/j.engappai.2023.105878
Osman, O. A., Hajij, M., Bakhit, P. R., & Ishak, S. (2019). Prediction of near-crashes from observed vehicle kinematics using machine learning. Transportation Research Record, 2673(12), 463-473. https://doi.org/10.1177/0361198119862629
Pandharipande, A., Cheng, C. H., Dauwels, J., Gurbuz, S. Z., Ibanez-Guzman, J., Li, G., Piazzoni, A., Wang, P., & Santra, A. (2023). Sensing and machine learning for automotive perception: A review. IEEE Sensors Journal, 23(11), 11097-11115. https://doi.org/10.1109/JSEN.2023.3262134
Rana, K., & Khatri, N. (2024). Automotive intelligence: Unleashing the potential of AI beyond advance driver assisting system, a comprehensive review. Computers and Electrical Engineering, 117, 109237. https://doi.org/10.1016/j.compeleceng.2024.109237
Sahoo, S. K., Das, A. K., Samanta, S., & Goswami, S. S. (2023a). Assessing the role of sustainable development in mitigating the issue of global warming. Journal of process management and new technologies, 11(1-2), 1-21. https://doi.org/10.5937/jpmnt11-44122
Sahoo, S. K., & Goswami, S. S. (2024). Green Supplier Selection using MCDM: A Comprehensive Review of Recent Studies. Spectrum of Engineering and Management Sciences, 2(1), 1-16. https://doi.org/10.31181/sems1120241a
Sahoo, S. K., Goswami, S. S., & Halder, R. (2024). Supplier Selection in the Age of Industry 4.0: A Review on MCDM Applications and Trends. Decision Making Advances, 2(1), 32-47. https://doi.org/10.31181/dma21202420
Sahoo, S. K., Goswami, S. S., Sarkar, S., & Mitra, S. (2023b). A review of digital transformation and industry 4.0 in supply chain management for small and medium-sized enterprises. Spectrum of Engineering and Management Sciences, 1(1), 58-72. https://doi.org/10.31181/sems1120237j
Shahriar, M. S., Kale, A. K., & Chang, K. (2023). Enhancing Intersection Traffic Safety Utilizing V2I Communications: Design and Evaluation of Machine Learning Based Framework. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3319382
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability engineering & system safety, 215, 107864. https://doi.org/10.1016/j.ress.2021.107864
Wang, K., Ying, Z., Goswami, S. S., Yin, Y., & Zhao, Y. (2023). Investigating the role of artificial intelligence technologies in the construction industry using a Delphi-ANP-TOPSIS hybrid MCDM concept under a fuzzy environment. Sustainability, 15(15), 11848. https://doi.org/10.3390/su151511848
Yang, D., Zhu, L., Liu, Y., Wu, D., & Ran, B. (2018). A novel car-following control model combining machine learning and kinematics models for automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 20(6), 1991-2000. https://doi.org/10.1109/TITS.2018.2854827