Exploring Artificial Intelligence in the Automotive Industry: A Bibliometric Analysis, Systematic Review, and Future Research Directions

Darvish, Majid; Khodadad Hosseini, Seyed Hamid; Mansourimoayyed, Fereshteh; Goudarzi, Gholam Reza

doi:10.22034/jnamm.2026.579268.1270

Exploring Artificial Intelligence in the Automotive Industry: A Bibliometric Analysis, Systematic Review, and Future Research Directions

Document Type : Review Article

Authors

Majid Darvish ¹

Seyed Hamid Khodadad Hosseini ²

Fereshteh Mansourimoayyed ¹

Gholam Reza Goudarzi ³

¹ Department of Business Management, Tarbiat Modares University, Tehran, Iran.

² Department of Business Management, Tarbiat Modares University, Tehran, Iran

³ Department Management, Imam Sadiq University, Tehran, Iran.

https://doi.org/10.22034/jnamm.2026.579268.1270

Abstract

Abstract
The aim of this study is to explore artificial intelligence in the automotive industry through a bibliometric analysis, systematic review, and identification of future research horizons. In this research, 179 international articles indexed in the Web of Science and Scopus databases were analyzed. The methodology consisted of two stages: first, a bibliometric analysis was conducted by VOSviewer software to identify thematic clusters in the literature related to artificial intelligence and the automotive industry. Second, a qualitative systematic review was carried out to provide deeper insights into these clusters. The findings revealed a significant focus on autonomous vehicles, deep learning, and machine learning as key artificial intelligence technologies. Four thematic clusters were identified: AI-based automotive ecosystems, core AI technologies and security, connectivity and resource management, and advanced vehicle technologies. In particular; safety, resource optimization, and legal frameworks emerged as key areas within these clusters. This study identified four emerging research areas that will play important roles in shaping the future of artificial intelligence in the automotive industry and provide transformative opportunities to address existing knowledge gaps.
Introduction
The automotive industry, which stands at the forefront of technological revolution, is undergoing a profound transformation through the integration of artificial intelligence (AI) (Gao & Bian, 2021; Nascimento et al., 2020). The role of AI as a key driver of innovation, efficiency, and safety in this industry has become increasingly evident, as vehicles are no longer merely means of transportation but are evolving into intelligent entities capable of perception, decision‑making, and communication (Li et al., 2019). This transformation heralds a new era in the transportation sector in which AI‑enabled vehicles provide enhanced safety, improved efficiency, and superior user experiences (Demlehner et al., 2021; Naz et al., 2022).
The convergence of the automotive industry and artificial intelligence has led to the rapid expansion of AI‑based solutions and applications, including autonomous driving, predictive maintenance, and advanced driver‑assistance systems (ADAS) (Li et al., 2023). These innovations have significantly improved road safety, optimized energy consumption, and redefined transportation solutions (Banerjee et al., 2023; Mehta et al., 2024). The successful implementation of AI in this industry promises technological advancement, economic growth, environmental sustainability, and improved social welfare (Dumitrascu et al., 2023; Shahedi et al., 2023). From advanced driver‑assistance systems to in‑vehicle voice recognition technologies, AI‑driven technologies have enhanced vehicle performance, safety, and comfort (Mehta et al., 2024). Moreover, consumer expectations for AI‑enabled features in vehicles have increased, prompting automotive companies to invest more heavily in research and development (Demlehner et al., 2021). The market for artificial intelligence in the automotive industry continues to expand, reflecting the growing recognition of its transformative potential (Jain & P. Kulkarni, 2022).
Recent review studies have examined the use of artificial intelligence techniques, such as machine learning and deep learning, to address various challenges in the automotive industry. For instance, Damaj et al. (2021) focused on the application of AI in vehicle maintenance and diagnostics, proposing AI‑based models for fault detection, prediction of remaining useful life, and maintenance of automotive components.
Bibliometric analysis quantitatively maps publication trends and identifies key patterns and influential research areas, while systematic review provides a deeper examination of thematic clusters and conceptual developments (Van Eck & Waltman, 2017). This combined approach offers a more comprehensive understanding of the field and helps researchers and practitioners navigate the evolving landscape of artificial intelligence in the automotive industry more effectively. Therefore, the main research question of this study is: How can artificial intelligence in the automotive industry be explored through bibliometric analysis, systematic review, and the identification of future research horizons?
Theoretical Framework
Artificial Intelligence
Artificial intelligence enables the transformation of data into information, information into knowledge, and knowledge into intelligent action, thereby facilitating the development of decision‑support systems in conditions characterized by uncertainty and intense competition. From a theoretical perspective, artificial intelligence can be considered a strategic resource within the frameworks of the Resource‑Based View (RBV) and dynamic capabilities theory, through which organizations enhance their capacity for analysis, innovation, and responsiveness to environmental changes (Mahmood, 2023).
Hayatmehr et al. (2026) examined the impact of the application of artificial intelligence and intelligent learning on the strategic thinking skills and academic performance of management students, considering the moderating role of individual ethics. The results indicated that the use of artificial intelligence tools has a positive effect on strategic thinking (including systems thinking, creative thinking, future‑oriented thinking, and critical thinking), intelligent learning, and academic performance. The mediating role of strategic thinking—particularly critical thinking and future‑oriented thinking—was confirmed in the relationship between the use of artificial intelligence and academic performance, as well as between intelligent learning and academic performance. Furthermore, individual ethics not only positively influences intelligent learning but also plays a moderating role in the relationship between intelligent learning and strategic thinking (systems thinking, critical thinking, and future‑oriented thinking). This study provides novel insights into the application of artificial intelligence tools in developing strategic thinking skills and performance, offering valuable implications for researchers, managers, students, and organizations.
Zolghadr et al. (2025) investigated the development of a model for the application of artificial intelligence in the export of electronic industry products. The findings showed that all composite reliability indices were above 0.7 and the convergent validity values for most constructs exceeded 0.5. The results of the hypothesis testing also indicated that all relationships among the model’s constructs were fully supported at a significance level of p < 0.001. Moreover, to evaluate the overall model fit and measure the structural model, the Goodness‑of‑Fit (GoF) index was applied. The GoF value was reported as 0.815, indicating a strong model fit.
Research Methodology
In this study, 179 international articles indexed in the Web of Science and Scopus databases were analyzed. The research methodology consisted of two stages. First, a bibliometric analysis was conducted by VOS viewer software in order to identify thematic clusters within the literature related to artificial intelligence and the automotive industry. Subsequently, a qualitative systematic review was carried out to provide deeper insights into these identified clusters and to further interpret the main research trends and developments in this field.
Research Findings
To analyze the findings, VOS viewer software was employed to identify thematic clusters within the literature related to artificial intelligence and the automotive industry. The results revealed a significant concentration of research on autonomous vehicles, deep learning, and machine learning as core artificial intelligence technologies in the automotive domain.
Four major thematic clusters were identified:

AI‑based automotive ecosystems
Core AI technologies and security
Connectivity and resource management
Advanced vehicle technologies

Within these clusters, safety, resource optimization, and regulatory frameworks emerged as particularly prominent and influential areas. The findings indicate that research in this field is increasingly moving toward integrated, intelligent, and sustainable mobility systems.
Furthermore, the study identified four emerging research domains that are expected to play a critical role in shaping the future trajectory of artificial intelligence in the automotive industry. These areas provide transformative opportunities to address existing knowledge gaps and to advance both theoretical development and practical implementation in AI‑driven automotive systems.
Conclusion
The present study was conducted with the aim of exploring artificial intelligence in the automotive industry through a bibliometric analysis, systematic review, and identification of future research horizons. The findings are consistent with prior studies (Hayatmehr et al., 2026; Zolghadr et al., 2025; Ahmadi Alinoudehi et al., 2025; Heidariyan et al., 2025; Haghighi, 2024; Rahimi Klor et al., 2024; Hasan & Ojala, 2024; Akbari Emami et al., 2023; Etemadi et al., 2023; Neethirajan, 2023), confirming the expanding strategic role of artificial intelligence across industries.
In particular, Hasan and Ojala (2024) demonstrated that AI management contributes to improved resource reconfiguration, reduced transaction costs, and the advancement of global sustainable development. These findings reinforce the view that artificial intelligence is not merely a technological tool but a transformative strategic capability within the automotive ecosystem.
Moreover, legal concerns surrounding the development of AI‑driven vehicles require further scholarly attention. For instance, in the event that an autonomous vehicle causes an accident, critical questions arise regarding liability—whether it lies with the manufacturer, the software developer, or the vehicle owner. Existing legal frameworks may struggle to keep pace with rapid technological advancements. Given the diversity of regulatory systems across countries, this issue poses additional challenges for automotive companies operating in multiple jurisdictions. Therefore, aligning legal regulations with technological progress in the automotive industry represents a significant and promising avenue for future research.

Keywords

Artificial Intelligence

Automotive Industry

Resource Management

Machine Learning

Core Technologies

Resource Optimization

Bibliometric Review

Subjects

Other topics related to business management, entrepreneurship, and marketing

Aadil, F., & Song, O., & Mushtaq, M., & Maqsood, M., & Ejaz Sheikh, S., & Baber, J. (2023). An efficient cluster optimization framework for internet of things (IoT) based Wireless Body Area Networks. Journal of Enterprise Information Management, 36(3), 839–860. https://doi.org/10.1108/JEIM-02-2020-0075

Ahmadi Alinoudehi, K., & Ashouri, H., & Hakibaei, Z. S. (2026). Analysis of the Dimensions and Components of AI‑Based Digital Transformation Management. (e242146). Journal of New Approaches in Management and Marketing, 4(4), e242146 doi: 10.22034/jnamm.2026.550414.1173.. (In Persian)

Agoston, D. V. (2024). Of artificial intelligence, machine learning, and the human brain: Celebrating Miklos Palkovits’ 90th birthday. Frontiers in Neuroanatomy, 18, Article 1374864. https://doi.org/10.3389/fnana.2024.1374864

Ahmedov, H., & Yi, D., & Sui, J. (2021). Application of a brain-inspired deep imitation learning algorithm in autonomous driving. Software Impacts, 10. https://doi.org/10.1016/j.simpa.2021.100165

Akbari emami, S., & Jamipour, M., & Fathi, S. (2023). Designing a framework for using artificial intelligence in human resource management: An exploratory approach. Journal of Sustainable Human Resource Management, 5(9), 284-263. doi: 10.22080/shrm.2023.4416 (In Persian)

Aizat, M., & Qistina, N., & Rahiman, W. (2024). A Comprehensive Review of Recent Advances in Automated Guided Vehicle Technologies: Dynamic Obstacle Avoidance in Complex Environment Toward Autonomous Capability. IEEE Transactions on Instrumentation and Measurement, 73. https://doi.org/10.1109/TIM.2023.3338722

Aldhyani, T., & Alkahtani, H. (2022). Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity. SENSORS, 22(1). https://doi.org/10.3390/s22010360

Alsanwy, S., & Chalak Qazani, M. R., & Al-Ashwal, W., & Shajari, A., & Nahvandi, S., & Asadi, H. (2024). Vehicle Trajectory Prediction Using Deep Learning for Advanced Driver Assistance Systems and Autonomous Vehicles. 2024 IEEE International Systems Conference (SysCon), 1–8. https://doi.org/10.1109/SysCon61195.2024.10553601

An, B., & Kim, Y. (2021). Real-time Camera and Video Streaming Through Optimized Settings of Ethernet AVB in Vehicle Network System. KSII Transactions on Internet And Information Systems, 15(8), 3025–3047. https://doi.org/10.3837/tiis.2021.08.018

An, B., & Kim, Y. (2022). Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence. JOURNAL OF WEB ENGINEERING, 21(2), 459–496. https://doi.org/10.13052/jwe1540-9589.21215

Arena, F., & Collotta, M., & Luca, L., & Ruggieri, M., & Termine, F. (2022). Predictive Maintenance in the Automotive Sector: A Literature Review. Mathematical AND Computational APPLICATIONS, 27(1). https://doi.org/10.3390/mca27010002

Arshathkhan A & Priya R. (2024). Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions. International Journal of Advanced Research in Science, Communication and Technology, 234–240. https://doi.org/10.48175/IJETIR-1243

Azam, S., & Munir, F., & Sheri, A., & Kim, J., & Jeon, M. (2020). System, Design and Experimental Validation of Autonomous Vehicle in an Unconstrained Environment. SENSORS, 20(21). https://doi.org/10.3390/s20215999

Bagherian M., & Saadat P., & Besamili M. (2023). The role of artificial intelligence and its importance in accounting systems. Scientific Journal of Modern Research Approaches to Management and Accounting, 7(24), 713-727. Retrieved from https://www.majournal.ir/index.php/ma/article/view/1900. (In Persian)

Bai, J., & Zhang, Z., & Shen, B. (2022). Internet of vehicles security situation awareness based on intrusion detection protection systems. Journal of Computational Methods in Sciences And Engineering, 22(1), 189–195. https://doi.org/10.3233/JCM-215889

Banerjee, S., & Das, D., & Chatterjee, P., & Blakely, B., & Ghosh, U. (2023). A Blockchain-Enabled Sustainable Safety Management Framework for Connected Vehicles. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. https://doi.org/10.1109/TITS.2023.3330159

Barbado, A., & Corcho, Ó. (2021). Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2107.06031

Bathla, G., & Bhadane, K., & Singh, R., & Kumar, R., & Aluvalu, R., & Krishnamurthi, R., & Kumar, A., & Thakur, R., & Basheer, S. (2022). Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/7632892

Biswas, A., & Wang, H. (2023). Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain. SENSORS, 23(4). https://doi.org/10.3390/s23041963

Campero-Jurado, I., & Quintanar-Gómez, J., & Vargas-Buitrón, O., & Trejo-Macotela, F., & Robles-Camarillo, D., & Simancas-Acevedo, E. (2019). Embedded system based on IoT and V2X for Smart Cities. International Journal of Combinatorial Optimization Problems and Informatics, 10(3), 50–58.

Chen, S., & Wen, H., & Wu, J. (2022). Artificial Intelligence Based Traffic Control for Edge Computing Assisted Vehicle Networks. Journal of Internet Technology, 23(5), 989–996. https://doi.org/10.53106/160792642022092305007

Damaj, I., & Serhal, D., & Hamandi, L., & Zantout, R., & Mouftah, H. (2021). Connected and Autonomous Electric Vehicles: Quality of Experience survey and taxonomy. Vehicular Communications, 28. https://doi.org/10.1016/j.vehcom.2020.100312

Demlehner, Q., & Schoemer, D., & Laumer, S. (2021). How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases. International Journal of Information Management, 58, 102317. https://doi.org/10.1016/j.ijinfomgt.2021.102317

Dini, P., & Saponara, S. (2023). Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity. SENSORS, 23(22). https://doi.org/10.3390/s23229231

Dumitrascu, O., & Dumitrascu, M., & Dobrota, D. (2020). Performance Evaluation for a Sustainable Supply Chain Management System in the Automotive Industry Using Artificial Intelligence. In PROCESSES (Vol. 8, Issue 11). MDPI. https://doi.org/10.3390/pr8111384

Ersöz, O., & Inal, A., & Aktepe, A., & Türker, A., & Ersöz, S. (2022). A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect. SUSTAINABILITY, 14(21). https://doi.org/10.3390/su142114536

Etemadi, M., & Chitsaz, E., & Koushki, S., & Jafari, S. M. (2024). Artificial Intelligence (AI) vs. Human-Led Approaches in Human Resource Recruitment Assessment: A Meta-Synthesis of Advantages and Disadvantages. Journal of Sustainable Human Resource Management, 6(11), 214-191. doi: 10.22080/shrm.2024.5100 (In Persian)

Farivar, F., & Haghighi, M., & Jolfaei, A., & Wen, S. (2021). Covert Attacks Through Adversarial Learning: Study of Lane Keeping Attacks on the Safety of Autonomous Vehicles. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 26(3), 1350–1357. https://doi.org/10.1109/TMECH.2021.3064816

Gao, X., & Bian, X. (2021). Autonomous driving of vehicles based on artificial intelligence. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 41(4), 4955–4964. https://doi.org/10.3233/JIFS-189982

Haenlein, M., & Kaplan &, A., & Chee, W. T., & Pengzhu, Zh. (2019). Artificial Intelligence (AI) and Management Analytics. Journal of Management Analytics. 6(4). 341-343. DOI: 10.1080/23270012.2019.1699876.

Haghighi, M. (2024). The necessity of using artificial intelligence in the development and progress of the country's exports and imports, 10th International Conference on Interdisciplinary Studies in Management and Engineering, Tehran, https://civilica.com/doc/2119142 (in Persian)

Hasan, R., & Ojala, A. (2024). Managing artificial intelligence in international business: Toward a research agenda on sustainable production and consumption. Thunderbird International Business Review, 66(2), 151-170.‏ DOI:10.1002/tie.22369

Hayatmehr, Z., & Bairamzadeh, S., & jalalzadeh, S. R. (2026). The impact of artificial intelligence and smart learning on strategic thinking and performance with the moderating role of personal morality (Case study: Management students). Management and Educational Perspective, 8(1), 112-134. doi: 10.22034/jmep.2025.499739.1467. (in Persian)

Heidarian, A., & Shafizadeh, H., & Shariatmadari, N. (2025). Designing a model for using artificial intelligence in learning for elementary school students. Management and Educational Perspective, 7(2), 443-467. doi: 10.22034/jmep.2025.547563.1574. (in Persian)

Hu, W., & Wu, H., & Cho, H., & Tseng, F. (2020). Optimal Route Planning System for Logistics Vehicles Based on Artificial Intelligence. Journal of Internet Technology, 21(3), 757–764. https://doi.org/10.3966/160792642020052103013

Hussain, D., & Khan, M., & Abbas, S., & Naqvi, R., & Mushtaq, M., & Rehman, A., & Nadeem, A. (2021). Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine. CMC-Computers Materials & Continua, 66(1), 141–156. https://doi.org/10.32604/cmc.2020.013458

Jain, P., Aggarwal, K. (2020). Transforming marketing with artificial intelligence. Int. Res. J. Eng. Technol. 7(7), 3964–3976. DOI:10.13140/RG.2.2.25848.67844

Joshi, A., & Capezza, S., & Alhaji, A., & Chow, M.-Y. (2023). Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems. IEEE/CAA Journal of Automatica Sinica, 10(7), 1513–1529. https://doi.org/10.1109/JAS.2023.123657

Khan, M. (2021). Intelligent Environment Enabling Autonomous Driving. IEEE ACCESS, 9, 32997–33017. https://doi.org/10.1109/ACCESS.2021.3059652

Kim, W., & Yang, H., & Kim, J. (2023). Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles. APPLIED SCIENCES-BASEL, 13(10). https://doi.org/10.3390/app13106147

Kiss, G. (2019). The danger of using artificial intelligence in the development of autonomous vehicles. Interdisciplinary description of complex systems, 17(4), 716–722. https://doi.org/10.7906/indecs.17.4.3

Kolekar, S., & Gite, S., & Pradhan, B., & Alamri, A. (2022). Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization. SENSORS, 22(24). https://doi.org/10.3390/s22249677

Kumar, V., & Zhu, D., & Dadam, S. R. (2023). Connected Vehicle Data – Prognostics and Monetization Opportunity. 2023-01–1685. https://doi.org/10.4271/2023-01-1685

Li, W.,& Li, G., & Kamarthi, S. (2023). The Study of Trends in AI Applications for Vehicle Maintenance Through Keyword Co-occurrence Network Analysis. International Journal of Prognostics and Health Management, 14(2), 3583. https://doi.org/10.36001/IJPHM.2023.v14i2.3583

Li, Z., & Zhang, K., & Chen, B., & Dong, Y., & Zhang, L. (2019). Driver identification in intelligent vehicle systems using machine learning algorithms. In IET IN℡LIGENT TRANSPORT SYSTEMS (Vol. 13, Issues 1, SI, pp. 40–47). WILEY. https://doi.org/10.1049/iet-its.2017.0254

Liu, J., & Cao, X. (2023). Empowering autonomous systems with AI-enabled V2X communication based signal analysis using sliding window integrated ensemble machine learning model. COMPUTERS & ELECTRICAL ENGINEERING, 111. https://doi.org/10.1016/j.compeleceng.2023.108936

Luetge, C., & Poszler, F., & Acosta, A. J., & Danks, D., & Gottehrer, G., & Mihet-Popa, L., & Naseer, A. (2021). AI4People: Ethical Guidelines for the Automotive Sector—Fundamental Requirements and Practical Recommendations. INTERNATIONAL JOURNAL OF TECHNOETHICS, 12(1), 101–125. https://doi.org/10.4018/IJT.20210101.oa2

M. Jain & P. Kulkarni. (2022). Application of AI, IOT and ML for Business Transformation of The Automotive Sector. 2022 International Conference on Decision Aid Sciences and Applications (DASA), 1270–1275. https://doi.org/10.1109/DASA54658.2022.9765294

Ma, Y., & Wang, Z., & Yang, H., & Yang, L. (2020). Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE-CAA Journal of Automatica Sinica, 7(2), 315–329. https://doi.org/10.1109/JAS.2020.1003021

Mahmood, S. (2023). Artificial intelligence-driven innovation and export performance: Evidence from high-tech industries. Technological Forecasting and Social Change, 199, 122700. https://doi.org/10.1016/j.techfore.2023.122700

Medrano-Berumen, C., & Akbas, M. (2021). Validation of decision-making in artificial intelligence-based autonomous vehicles. Journal of Information and Telecommunication, 5(1), 83–103. https://doi.org/10.1080/24751839.2020.1824154

Mehta, A. A., & Padaria, A. A., & Bavisi, D. J., & Ukani, V., & Thakkar, P., & Geddam, R., & Kotecha, K., & Abraham, A. (2024). Securing the Future: A Comprehensive Review of Security Challenges and Solutions in Advanced Driver Assistance Systems. IEEE ACCESS, 12, 643–678. https://doi.org/10.1109/ACCESS.2023.3347200

Menon, V., & Jacob, S., & Joseph, S., & Sehdev, P., & Khosravi, M., & Al-Turjman, F. (2022). An IoT-enabled intelligent automobile system for smart cities. INTERNET OF THINGS, 18. https://doi.org/10.1016/j.iot.2020.100213

Ming, Y., & Li, Y., & Zhang, Z., & Yan, W. (2021). A Survey of Path Planning Algorithms for Autonomous Vehicles. Sae International Journal of Commercial Vehicles, 14(1), 97–109. https://doi.org/10.4271/02-14-01-0007

Murtaza, M., & Cheng, C., & Fard, M., & Zeleznikow, J. (2023). The importance of transparency in naming conventions, designs, and operations of safety features: From modern ADAS to fully autonomous driving functions. AI & SOCIETY, 38(2), 983–993. https://doi.org/10.1007/s00146-022-01442-x

Muzahid, A., & Kamarulzaman, S., & Rahman, M., & Murad, S., & Kamal, M., & Alenezi, A. (2023). Multiple vehicle cooperation and collision avoidance in automated vehicles: Survey and an AI-enabled conceptual framework. SCIENTIFIC REPORTS, 13(1). https://doi.org/10.1038/s41598-022-27026-9

Nascimento, A. M., & Vismari, L. F., & Molina, C. B. S. T., & Cugnasca, P. S., & Camargo, J. B., & Jr., de Almeida, J. R., & Jr., Inam, R., & Fersman, E., & Marquezini, M. V., & Hata, A. Y. (2020). A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety. In IEEE TRANSACTIONS ON IN℡LIGENT TRANSPORTATION SYSTEMS (Vol. 21, Issue 12, pp. 4928–4946). IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. https://doi.org/10.1109/TITS.2019.2949915

Naz, N., & Ehsan, M. K., & Amirzada, M. R., & Ali, M. Y., & Qureshi, M. A. (2022). Intelligence of Autonomous Vehicles: A Concise Revisit. JOURNAL OF SENSORS, 2022, 2690164. https://doi.org/10.1155/2022/2690164

Neethirajan, S. (2023). Artificial intelligence and sensor technologies in dairy livestock export: charting a digital transformation. Sensors, 23(16), 7045. doi.org/10.3390/s23167045

Ning, Z., & Zhang, K., & Wang, X., & Guo, L., & Hu, X., & Huang, J., & Hu, B., & Kwok, R. (2021). Intelligent Edge Computing in Internet of Vehicles: A Joint Computation Offloading and Caching Solution. IEEE Transactions on Intelligent Transportation Systems, 22(4), 2212–2225. https://doi.org/10.1109/TITS.2020.2997832

Pan, C., & Gao, Y., & Gu, A. (2023). Enhancing reliability analysis of AI systems using STPA: A case study on the DroneResponse UAV system. In S. Saxena & C. Zhao (Eds.), International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023) (p. 43). SPIE. https://doi.org/10.1117/12.3011516

Park, J., & Quach, N., & Kim, Y., & Cheng, R., & Jenco, M., & Yin, C., & Lee, A., & Won, Y. (2023). Machine Learning Analysis of Autonomous Vehicle Sensors Under Extreme Conditions in Alaska. JOURNAL OF ELECTRONIC PACKAGING, 145(4). https://doi.org/10.1115/1.4063486

Rahimi Klor, H., & Mohammadkhani, R. (2024). Designing a model of the consequences of the use of artificial intelligence and machine learning in advertising and sales. Intelligent Business Management Studies, 12(48), 223-270. doi: 10.22054/ims.2024.76711.2403. (in Persian)

Rao, Y., & Yang, F. (2020). Research on Path Tracking Algorithm of Autopilot Vehicle Based on Image Processing. International Journal of Pattern Recognition and Artificial Intelligence, 34(5). https://doi.org/10.1142/S0218001420540130

Reveles-Gómez, L., & Luna-García, H., & Celaya-Padilla, J., & Barría-Huidobro, C., & Gamboa-Rosales, H., & Solís-Robles, R., & Arceo-Olague, J., & Galván-Tejada, J., & Galván-Tejada, C., & Rondon, D., & Villalba-Condori, K. (2023). Detection of Pedestrians in Reverse Camera Using Multimodal Convolutional Neural Networks. SENSORS, 23(17). https://doi.org/10.3390/s23177559

Sadaf, M., & Iqbal, Z., & Javed, A., & Saba, I., & Krichen, M., & Majeed, S., & Raza, A. (2023). Connected and Automated Vehicles: Infrastructure, Applications, Security, Critical Challenges, and Future Aspects. TECHNOLOGIES, 11(5). https://doi.org/10.3390/technologies11050117

Shahedi, A., & Dadashpour, I., & Rezaei, M. (2023). Barriers to the sustainable adoption of autonomous vehicles in developing countries: A multi-criteria decision-making approach. HELIYON, 9(5). https://doi.org/10.1016/j.heliyon.2023.e15975

Shahzadi, S., & Chaudhry, N., & Iqbal, M. (2023). A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks. SENSORS, 23(17). https://doi.org/10.3390/s23177366

Singh, P. K., & Singh, R., & Nandi, S. (2020). V-CARE: A Blockchain Based Framework for Secure Vehicle Health Record System (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2007.13647

Tang, F., & Kawamot, Y., & Kato, N., & Liu, J. (2020). Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches. PROCEEDINGS OF THE IEEE, 108(2), 292–307. https://doi.org/10.1109/JPROC.2019.2954595

Todorovic, M., & Aldakkhelallah, A., & Simic, M. (2023). Managing Transitions to Autonomous and Electric Vehicles: Scientometric and Bibliometric Review. WORLD ELECTRIC VEHICLE JOURNAL, 14(11). https://doi.org/10.3390/wevj14110314

Tran, D. V., & Le, C. T. Q. (2022). Developing a Regulatory Framework for Autonomous Vehicles: A Proximal Analysis of European Approach and Its Application to ASEAN Countries. TALTECH JOURNAL OF EUROPEAN STUDIES, 12(2), 165–188. https://doi.org/10.2478/bjes-2022-0016

Triki, N., & Karray, M., & Ksantini, M. (2023). A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles. APPLIED SCIENCES-BASEL, 13(8), 4793. https://doi.org/10.3390/app13084793

Urbieta, I., & Nieto, M., & García, M., & Otaegui, O. (2021). Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). APPLIED SCIENCES-BASEL, 11(17). https://doi.org/10.3390/app11177782

Vagale, A., & Oucheikh, R., & Bye, R., & Osen, O., & Fossen, T. (2021). Path planning and collision avoidance for autonomous surface vehicles I: a review. JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 26(4), 1292–1306. https://doi.org/10.1007/s00773-020-00787-6

Vakaruk, S., & Sierra-García, J., & Mozo, A., & Pastor, A. (2021). Forecasting Automated Guided Vehicle Malfunctioning with Deep Learning in a 5G-Based Industry 4.0 Scenario. IEEE COMMUNICATIONS MAGAZINE, 59(11), 102–108. https://doi.org/10.1109/MCOM.221.2001079

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

Van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7

Vishal Goyal, Kamal Sharma, A. J. (2024). Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights. Journal of Electrical Systems, 20(4s), 508–523. https://doi.org/10.52783/jes.2061

Yan, M., & Xiong, R., & Wang, Y., & Li, C. (2024). Edge Computing Task Offloading Optimization for a UAV-Assisted Internet of Vehicles via Deep Reinforcement Learning. IEEE Transactions on Vehicular Technology, 73(4), 5647–5658. https://doi.org/10.1109/TVT.2023.3331363

Zhang, Q., & Tian, S., & Lin, X. (2023). Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China. ELECTRONICS, 12(2). https://doi.org/10.3390/electronics12020445

Zhang, T. (2020). Toward Automated Vehicle Teleoperation: Vision, Opportunities, and Challenges. IEEE INTERNET OF THINGS JOURNAL, 7(12), 11347–11354. https://doi.org/10.1109/JIOT.2020.3028766

Zhang, W., & Huang, C. (2024). Timeliness Study of Home Energy Management System Based on Dynamic Allocation of Bandwidth. IEEE Access, 12, 46329–46346. https://doi.org/10.1109/ACCESS.2024.3381624

Zolghadr, A., & Sarmad saeidi, S., & Ghasemi, B. (2025). Presenting a model for applying artificial intelligence in exporting electronics products. Journal of New Approaches in Management and Marketing, 4(3), 209-228. doi: 10.22034/jnamm.2026.510320.1078. (in Persian)