Araştırma Makalesi
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Otonom Araçlarda Yapay Zekâ Araştırmalarının Evrimi (2015–2025): Bibliyometrik ve Tematik Bir İnceleme

Yıl 2025, Cilt: 8 Sayı: 2, 1 - 22, 25.10.2025
https://doi.org/10.51513/jitsa.1741108

Öz

Bu çalışma, 2015–2025 döneminde otonom araçlar (OA) alanındaki yapay zekâ (YZ) araştırmalarını kapsamlı bir bibliyometrik ve tematik incelemeyle ele almaktadır. Web of Science (WoS) veri tabanında dizinlenen 856 akademik yayına dayanan analizde, yayın eğilimleri, atıf dinamikleri, iş birliği ağları, anahtar kelime eş-oluşumları ve bibliyografik eşleşmeler VOSviewer ve Excel programları kullanılarak haritalanmıştır. Ayrıca, en çok atıf alan 25 makale üzerinden yapılan içerik analizi, baskın araştırma temaları ve metodolojik yaklaşımları ortaya koymaktadır. Bulgular, 2019 sonrası yayın hacmi ve akademik etki açısından dikkate değer bir artış olduğunu ve derin öğrenme, planlama algoritmaları, Endüstri 4.0 entegrasyonu ve akıllı ulaşım sistemlerine yoğunlaşan bir ilgi bulunduğunu göstermektedir. Amerika Birleşik Devletleri, Çin ve Birleşik Krallık, araştırma çıktıları ve küresel iş birliklerinde başı çekmektedir. Ancak, etik, düzenleyici ve toplumsal boyutların belirgin biçimde yeterince temsil edilmediği disipliner bir dengesizlik tespit edilmiştir. Nicel bibliyometrik yöntemleri nitel tematik analizle birleştiren bu inceleme, YZ destekli otonom mobilitenin evrimine bütüncül bir bakış sunmakta ve gelecekteki araştırmalar için kavramsal ve metodolojik öneriler getirmektedir. Çalışma, hızla dönüşen otonom araç teknolojileri alanında yön bulmak isteyen akademisyenler, politika yapıcılar ve sektör temsilcileri için değerli bir rehber niteliği taşımaktadır.

Kaynakça

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  • Aradi, S. (2022). Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(2), 740-759.
  • Aydın, Ö. (2021). Authentication and billing scheme for the electric vehicles: EVABS. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 6(1), 29-42.
  • Babaei, P., Riahinia, N., E., E., & Azimi, A. (2025). Towards a Data‐Driven Digital Twin AI‐Based Architecture for Self‐Driving Vehicles. IET Intelligent Transport Systems. https://doi.org/10.1049/itr2.70017.
  • Bartneck, C., Lütge, C., Wagner, A., & Welsh, S. (2020). Autonomous Vehicles. An Introduction to Ethics in Robotics and AI. https://doi.org/10.5772/intechopen.73376.
  • Batdı, V., & Talan, T. (2019). Augmented reality applications: A Meta-analysis and thematic analysis. Turkish Journal of Education, 8(4), 276-297.
  • 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. https://doi.org/10.1155/2022/7632892.
  • Beck, R., Dibbern, J., & Wiener, M. (2022). A multi-perspective framework for research on (sustainable) autonomous systems. Business & information systems engineering, 64(3), 265-273.
  • Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022). Autonomous Vehicles on the Edge: A Survey on AVRacing. IEEE Open Journal of Intelligent Transportation Systems, 3, 458-488. https://doi.org/10.1109/ojits.2022.3181510.
  • Bonnefon, J., Shariff, A., & Rahwan, I. (2015). The social dilemma of autonomous vehicles. Science, 352, 1573 - 1576. https://doi.org/10.1126/science.aaf2654.
  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
  • Çetaş.(2024). Otonom Araçlarda Yapay Zeka. Zeka. Retrieved July 9, 2025 from https://www.cetas.com.tr/blog/otonom-araclarda-yapay-zeka
  • Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880.
  • Chou, Y. L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022). Counterfactuals and causability in explainable AI: Theory, algorithms, and applications. Information Fusion, 81, 59-83.
  • Di, X., & Shi, R. (2021). A survey on AVcontrol in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning. Transportation Research Part C: Emerging Technologies, 125, 103008.
  • Dreossi, T., Fremont, D. J., Ghosh, S., Kim, E., Ravanbakhsh, H., Vazquez-Chanlatte, M., & Seshia, S. A. (2019). VerifAI: A toolkit for the formal design and analysis of AI-based systems. In Computer Aided Verification (pp. 432-442). Springer.
  • Duan, J., Li, S. E., Guan, Y., Sun, Q., & Cheng, B. (2020). Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data. IET Intelligent Transport Systems, 14(5), 297-305.
  • Ergunşah, Ş., & Koşunalp, S. (2022). İnsansız hava araçları tabanlı çevresel uygulamalara genel bir bakış. International Journal of Management Information Systems and Computer Science, 6(1), 43-53.
  • Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: A survey study from consumers' perspectives. BMC Medical Informatics and Decision Making, 20(1), 170.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge C., Madelin R., Pagallo U., Rossi F., Schafer B., Valcke P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707.
  • Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2020). A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(6), 5876-5888.
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  • Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research synthesis methods, 11(2), 181-217.
  • Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied AI and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
  • Hopkins, J. L. (2021). An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia. Computers in Industry, 125, 103323.
  • Huang, L., Ladikas, M., Schippl, J., He, G., & Hahn, J. (2023). Knowledge mapping of an AI application scenario: A bibliometrics analysis of the basic research of data-driven autonomous vehicles. Technology in Society. https://doi.org/10.1016/j.techsoc.2023.102360.
  • Huang, L., Ladikas, M., Schippl, J., He, G., Lu, Y., & Hahn, J. (2022). Basic Research Performance of Data-Driven AVin a Global Context: A Bibliometrics Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4291172.
  • Hussain, R., & Zeadally, S. (2019). Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys & Tutorials, 21, 1275-1313. https://doi.org/10.1109/COMST.2018.2869360.
  • IEEE (2023). P7000: Model Process for Addressing Ethical Concerns in Autonomous Systems. IEEE Standards.
  • Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0.
  • Khan, M. J., Khan, M. A., Beg, A., Malik, S., & El-Sayed, H. (2022). An overview of the 3GPP identified Use Cases for V2X Services. Procedia Computer Science, 198, 750-756.
  • Khan, M., Sayed, H., Malik, S., Zia, T., Khan, J., Alkaabi, N., & Ignatious, H. (2022). Level-5 Autonomous Driving—Are We There Yet? A Review of Research Literature. ACM Computing Surveys (CSUR), 55, 1 - 38. https://doi.org/10.1145/3485767.
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The Evolution of Artificial Intelligence Research in Autonomous Vehicles (2015–2025): A Bibliometric and Thematic Review

Yıl 2025, Cilt: 8 Sayı: 2, 1 - 22, 25.10.2025
https://doi.org/10.51513/jitsa.1741108

Öz

This study presents a comprehensive bibliometric and thematic review of artificial intelligence (AI) research in the domain of autonomous vehicles (AV) over the period 2015–2025. Based on 856 academic publications indexed in the Web of Science (WoS) database, the analysis utilizes VOSviewer and Excel to map publication trends, citation dynamics, collaborative networks, keyword co-occurrences, and bibliographic coupling. Furthermore, content analysis of the top 25 most-cited articles reveals dominant research themes and methodological approaches. The findings show a substantial surge in publication volume and scholarly impact after 2019, with core attention given to deep learning, planning algorithms, Industry 4.0 integration, and intelligent transportation systems. The United States, China, and the United Kingdom lead in research output and global collaborations. However, the study identifies a disciplinary imbalance, with ethical, regulatory, and societal dimensions significantly underrepresented. By combining quantitative bibliometric methods with qualitative thematic insights, this review provides a holistic overview of the evolution of AI-powered autonomous mobility and proposes conceptual and methodological directions for future research. The study offers valuable guidance for academics, policymakers, and industry stakeholders seeking to navigate the rapidly transforming landscape of autonomous vehicle technologies.

Kaynakça

  • Abduljabbar, R., Dia, H., Liyanage, S., and Bagloee, S. A. (2019). Applications of AI in transport: An overview. Sustainability, 11(1), 189.
  • Alam, F., Mehmood, R., Katib, I., Albogami, N. N., & Albeshri, A. (2017). Data fusion and IoT for smart ubiquitous environments: A survey. IEEE Access, 5, 9533-9554.
  • Aradi, S. (2022). Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(2), 740-759.
  • Aydın, Ö. (2021). Authentication and billing scheme for the electric vehicles: EVABS. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 6(1), 29-42.
  • Babaei, P., Riahinia, N., E., E., & Azimi, A. (2025). Towards a Data‐Driven Digital Twin AI‐Based Architecture for Self‐Driving Vehicles. IET Intelligent Transport Systems. https://doi.org/10.1049/itr2.70017.
  • Bartneck, C., Lütge, C., Wagner, A., & Welsh, S. (2020). Autonomous Vehicles. An Introduction to Ethics in Robotics and AI. https://doi.org/10.5772/intechopen.73376.
  • Batdı, V., & Talan, T. (2019). Augmented reality applications: A Meta-analysis and thematic analysis. Turkish Journal of Education, 8(4), 276-297.
  • 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. https://doi.org/10.1155/2022/7632892.
  • Beck, R., Dibbern, J., & Wiener, M. (2022). A multi-perspective framework for research on (sustainable) autonomous systems. Business & information systems engineering, 64(3), 265-273.
  • Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022). Autonomous Vehicles on the Edge: A Survey on AVRacing. IEEE Open Journal of Intelligent Transportation Systems, 3, 458-488. https://doi.org/10.1109/ojits.2022.3181510.
  • Bonnefon, J., Shariff, A., & Rahwan, I. (2015). The social dilemma of autonomous vehicles. Science, 352, 1573 - 1576. https://doi.org/10.1126/science.aaf2654.
  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
  • Çetaş.(2024). Otonom Araçlarda Yapay Zeka. Zeka. Retrieved July 9, 2025 from https://www.cetas.com.tr/blog/otonom-araclarda-yapay-zeka
  • Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880.
  • Chou, Y. L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022). Counterfactuals and causability in explainable AI: Theory, algorithms, and applications. Information Fusion, 81, 59-83.
  • Di, X., & Shi, R. (2021). A survey on AVcontrol in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning. Transportation Research Part C: Emerging Technologies, 125, 103008.
  • Dreossi, T., Fremont, D. J., Ghosh, S., Kim, E., Ravanbakhsh, H., Vazquez-Chanlatte, M., & Seshia, S. A. (2019). VerifAI: A toolkit for the formal design and analysis of AI-based systems. In Computer Aided Verification (pp. 432-442). Springer.
  • Duan, J., Li, S. E., Guan, Y., Sun, Q., & Cheng, B. (2020). Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data. IET Intelligent Transport Systems, 14(5), 297-305.
  • Ergunşah, Ş., & Koşunalp, S. (2022). İnsansız hava araçları tabanlı çevresel uygulamalara genel bir bakış. International Journal of Management Information Systems and Computer Science, 6(1), 43-53.
  • Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: A survey study from consumers' perspectives. BMC Medical Informatics and Decision Making, 20(1), 170.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge C., Madelin R., Pagallo U., Rossi F., Schafer B., Valcke P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707.
  • Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2020). A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(6), 5876-5888.
  • Garikapati, D., & Shetiya, S. (2024). Autonomous Vehicles: Evolution of AI and the Current Industry Landscape. Big Data Cogn. Comput., 8, 42. https://doi.org/10.3390/bdcc8040042.
  • Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research synthesis methods, 11(2), 181-217.
  • Hengstler, M., Enkel, E., & Duelli, S. (2016). Applied AI and trust—The case of autonomous vehicles and medical assistance devices. Technological Forecasting and Social Change, 105, 105-120.
  • Hopkins, J. L. (2021). An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia. Computers in Industry, 125, 103323.
  • Huang, L., Ladikas, M., Schippl, J., He, G., & Hahn, J. (2023). Knowledge mapping of an AI application scenario: A bibliometrics analysis of the basic research of data-driven autonomous vehicles. Technology in Society. https://doi.org/10.1016/j.techsoc.2023.102360.
  • Huang, L., Ladikas, M., Schippl, J., He, G., Lu, Y., & Hahn, J. (2022). Basic Research Performance of Data-Driven AVin a Global Context: A Bibliometrics Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4291172.
  • Hussain, R., & Zeadally, S. (2019). Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys & Tutorials, 21, 1275-1313. https://doi.org/10.1109/COMST.2018.2869360.
  • IEEE (2023). P7000: Model Process for Addressing Ethical Concerns in Autonomous Systems. IEEE Standards.
  • Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0.
  • Khan, M. J., Khan, M. A., Beg, A., Malik, S., & El-Sayed, H. (2022). An overview of the 3GPP identified Use Cases for V2X Services. Procedia Computer Science, 198, 750-756.
  • Khan, M., Sayed, H., Malik, S., Zia, T., Khan, J., Alkaabi, N., & Ignatious, H. (2022). Level-5 Autonomous Driving—Are We There Yet? A Review of Research Literature. ACM Computing Surveys (CSUR), 55, 1 - 38. https://doi.org/10.1145/3485767.
  • Kim, D. H., Kim, T. J., Wang, X., Kim, M., Quan, Y. J., Oh, J. W., ... & Ahn, S. H. (2018). Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 555-568.
  • Kim, H., & Duffy, V. (2021). Bibliometric Analysis on the Safety of Autonomous Vehicles with AI. 278-289. https://doi.org/10.1007/978-3-030-90966-6_20.
  • Korkmaz, A. (2023). Predictive Modeling of Urban Traffic Accident Severity in Türkiye's Centennial: Machine Learning Approaches for Sustainable Cities. Kent Akademisi, 16(Türkiye Cumhuriyeti’nin 100. Yılı Özel Sayısı| Special Issue for the 100th Anniversary of the Republic of Türkiye), 395-406.
  • Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications. https://doi.org/10.4135/9781412961288.n73.
  • Li, R., Han, Y., & Zhou, H. (2025). What changes the AVacceptance after COVID-19? Evidence from China. Research in Transportation Economics, 109, 101498.
  • Liu, L., Lu, S., Zhong, R., Wu, B., Yao, Y., Zhang, Q., & Shi, W. (2020). Computing Systems for Autonomous Driving: State of the Art and Challenges. IEEE Internet of Things Journal, 8, 6469-6486. https://doi.org/10.1109/JIOT.2020.3043716.
  • Liu, Q., Tian, D., Li, Y., Kim, H., & Serikawa, S. (2019). The Cognitive Internet of Vehicles for Autonomous Driving. IEEE Network, 33, 65-73. https://doi.org/10.1109/MNET.2019.1800339.
  • Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020). AI applications in the development of autonomous vehicles: A survey. IEEE-CAA Journal of Automatica Sinica, 7(2), 315-329.
  • Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations. Scientometrics, 126(1), 871-906.
  • Mongeon, P., Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106, 213–228. https://doi.org/10.1007/s11192-015-1765-5
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  • Y. Ma, Y., Wang, Z., Yang, H., & Yang, L. (2020). AI applications in the development of autonomous vehicles: a survey. IEEE/CAA Journal of Automatica Sinica, 7, 315-329. https://doi.org/10.1109/JAS.2020.1003021.
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka (Diğer)
Bölüm Makaleler
Yazarlar

Selma Bulut 0000-0002-6559-7704

Erken Görünüm Tarihi 22 Ekim 2025
Yayımlanma Tarihi 25 Ekim 2025
Gönderilme Tarihi 14 Temmuz 2025
Kabul Tarihi 7 Ekim 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 2

Kaynak Göster

APA Bulut, S. (2025). Otonom Araçlarda Yapay Zekâ Araştırmalarının Evrimi (2015–2025): Bibliyometrik ve Tematik Bir İnceleme. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 8(2), 1-22. https://doi.org/10.51513/jitsa.1741108