Akıllı Ulaşım Sistemleri ve Otonom Sürüş Protokollerinde Güvenilir Bilgi Erişimi: RAG Mimarisinin Mevzuat Analizi Üzerine Deneysel Bir Değerlendirme

Year 2026, Volume: 9 Issue: 1, 22 - 41, 25.03.2026

Ahmet Akkaya

https://doi.org/10.51513/jitsa.1848120

https://izlik.org/JA53MZ36YG

Abstract

Akıllı Ulaşım Sistemleri (AUS) ve otonom sürüş teknolojilerindeki hızlı gelişim, karmaşık teknik protokollerin ve hukuki mevzuatların doğru analiz edilmesini zorunlu kılmaktadır. Büyük Dil Modelleri (LLM), bu metinlerin işlenmesinde büyük potansiyel sunsa da "halüsinasyon" riski otonom araçlar gibi güvenlik-kritik alanlarda ciddi bir engel teşkil etmektedir. Bu çalışmanın amacı, Geri Getirme Destekli Nesil (RAG) mimarisinin; Karayolları Trafik Kanunu, Euro NCAP protokolleri ve Ulusal AUS Strateji belgeleri üzerindeki performansını deneysel olarak değerlendirmektir. Çalışma kapsamında, 5 farklı doküman tipinden türetilen 44 özgün senaryo üzerinden standart LLM ve önerilen RAG sistemi karşılaştırılmıştır. Elde edilen bulgular, RAG mimarisinin anlamsal benzerlik skorlarında standart modellere oranla %16,65 düzeyinde bir iyileşme sağladığını ortaya koymuştur. Yapılan istatistiksel analizler, bu performans artışının p = 0,0072 değeri ile yüksek düzeyde anlamlı olduğunu ve Cohen’s d = 0,30 etki büyüklüğüne sahip olduğunu kanıtlamıştır. Sonuçlar, RAG sistemlerinin bilgi doğruluğunu artırarak otonom sürüş ekosisteminde mevzuat uyumu ve karar destek mekanizmaları için güvenilir bir çözüm sunduğunu göstermektedir. Bu çalışma, yerel mevzuat odaklı bir benchmark sunarak literatürdeki önemli bir boşluğu doldurmaktadır.

Keywords

Geri Getirme Destekli Nesil (RAG) , Akıllı Ulaşım Sistemleri , Otonom Sürüş Mevzuatı , Halüsinasyon Denetimi , Semantik Doğruluk , Euro NCAP

Ethical Statement

Çalışma kapsamında herhangi bir kurum veya kişi ile çıkar çatışması bulunmamaktadır.

Supporting Institution

Çalışma herhangi bir destek almamıştır.

Thanks

Teşekkür edilecek bir kurum veya kişi bulunmamaktadır.

Reliable Information Access in Intelligent Transportation Systems and Autonomous Driving Protocols: An Experimental Evaluation of the RAG Architecture's Regulatory Analysis

https://izlik.org/JA53MZ36YG

Abstract

The rapid advancement in Intelligent Transportation Systems (ITS) and autonomous driving technologies necessitates the accurate analysis of complex technical protocols and legal regulations. While Large Language Models (LLMs) offer significant potential for processing these texts, the risk of "hallucinations" remains a critical barrier in safety-critical domains such as autonomous vehicles. This study aims to experimentally evaluate the performance of Retrieval-Augmented Generation (RAG) architectures on key ITS documents, including the Turkish Highway Traffic Law, Euro NCAP protocols, and National ITS Strategy papers. Within the scope of the research, standard LLMs and the proposed RAG system were compared across 44 unique scenarios derived from five distinct document types. The findings reveal that the RAG architecture provided a 16.65% improvement in semantic similarity scores compared to standard models. Statistical analyses confirmed that this performance increase is highly significant (p = 0.0072) with an effect size of Cohen’s d = 0.30. The results demonstrate that RAG systems substantially increase information integrity, offering a reliable solution for regulatory compliance and decision-support mechanisms in the autonomous driving ecosystem. By providing a localised legislation-oriented benchmark, this study fills a significant gap in the literature regarding safety-critical information retrieval.

Keywords

Retrieval-Augmented Generation (RAG) , Intelligent Transportation Systems , Autonomous Driving Legislation , Hallucination Mitigation , Semantic Similarity , Euro NCAP

Ethical Statement

This study does not involve any conflict of interest with any institution or individual.

Supporting Institution

The study received no funding.

Thanks

There is no institution or person to thank.

References

• Akkaya, A., & Közkurt, C. (2025). An effective approach for adaptive operator selection and comparison for PSO algorithm. Cluster Computing, 28(6), 368.
• Alshemali, B., & Kalita, J. (2020). Improving the Reliability of Deep Neural Networks in NLP: A Review. Knowledge-Based Systems, 191, 105210. https://doi.org/10.1016/j.knosys.2019.105210
• Andriopoulos, K., & Pouwelse, J. (2023). Augmenting LLMs with Knowledge: A survey on hallucination prevention (No. arXiv:2309.16459). arXiv. https://doi.org/10.48550/arXiv.2309.16459
• Awadid, A., Becquart, M., Gagnant, M., & Meyer-Vitali, A. (2025). A Retrieval-Augmented Generation (RAG) System for Supporting Architectural Design of Intelligent Transportation Systems. In 2025 25th International Conference on Software Quality, Reliability, and Security Companion (QRS-C) (pp. 1-10). IEEE.
• Backgård, W. (2025). The Calculated Law Exploring the limits of transformer models in legal reasoning. https://gupea.ub.gu.se/handle/2077/87586
• Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles: Challenges, opportunities, and future implications for transportation policies. Journal of Modern Transportation, 24(4), 284–303. https://doi.org/10.1007/s40534-016-0117-3
• Balu, B. V., Geissler, F., Carella, F., Zacchi, J. V., Jiru, J., Mata, N., & Stolle, R. (2025, May). Towards automated safety requirements derivation using agent-based rag. In Proceedings of the AAAI Symposium Series (Vol. 5, No. 1, pp. 299-307).
• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
• Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
• Cui, J., Ning, M., Li, Z., Chen, B., Yan, Y., Li, H., Ling, B., Tian, Y., & Yuan, L. (2023). ChatLaw: Open-source legal large language model with integrated external knowledge bases. arXiv. https://doi.org/10.48550/arXiv.2306.16092
• Hong, G., Ouyang, T., Zhao, K., Zhou, Z., & Chen, X. (2025). CoEdge-RAG: Optimizing Hierarchical Scheduling for Retrieval-Augmented LLMs in Collaborative Edge Computing. In 2025 IEEE Real-Time Systems Symposium (RTSS) (pp. 162-174). IEEE.
• Dilek, E., Talih, Ö., & Ceylan, H. (2023). Ulusal Akıllı Ulaşım Sistemleri Mimarisinin Yaygınlaştırılması: Türkiye Önerisi. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 6(2), 353–392. https://doi.org/10.51513/jitsa.1309583
• Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., Metropolitansky, D., Ness, R. O., & Larson, J. (2025). From Local to Global: A Graph RAG Approach to Query-Focused Summarization (No. arXiv:2404.16130). arXiv. https://doi.org/10.48550/arXiv.2404.16130
• Filipovska, E., Mladenovska, A., Dobreva, J., Kitanovski, D., Mitrov, G., Lameski, P., & Zdravevski, E. (2025). Evaluation of Vector Databases and LLMs in RAG-Based Multi-document Question Answering. In B. Risteska Stojkoska & S. Janeska Sarkanjac (Eds), ICT Innovations 2024. TechConvergence: AI, Business, and Startup Synergy (pp. 3–18). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-86162-8_1
• Finardi, P., Avila, L., Castaldoni, R., Gengo, P., Larcher, C., Piau, M., Costa, P., & Caridá, V. (2024). The Chronicles of RAG: The Retriever, the Chunk and the Generator (No. arXiv:2401.07883). arXiv. https://doi.org/10.48550/arXiv.2401.07883
• Fraade-Blanar, L., Blumenthal, M. S., Anderson, J. M., & Kalra, N. (2018). Measuring Automated Vehicle Safety: Forging a Framework. https://www.rand.org/pubs/research_reports/RR2662.html
• Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2024). Retrieval-Augmented Generation for Large Language Models: A Survey (No. arXiv:2312.10997). arXiv. https://doi.org/10.48550/arXiv.2312.10997
• Guha, N., Nyarko, J., Ho, D., Ré, C., Chilton, A., K, A., Chohlas-Wood, A., Peters, A., Waldon, B., Rockmore, D., Zambrano, D., Talisman, D., Hoque, E., Surani, F., Fagan, F., Sarfaty, G., Dickinson, G., Porat, H., Hegland, J., … Li, Z. (2023). LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 36, 44123–44279.
• Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Trans. Inf. Syst., 43(2), 42:1-42:55. https://doi.org/10.1145/3703155
• Huly, O., Pogrebinsky, I., Carmel, D., Kurland, O., & Maarek, Y. (2024). Old IR Methods Meet RAG. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2559–2563. https://doi.org/10.1145/3626772.3657935
• Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of Hallucination in Natural Language Generation. ACM Comput. Surv., 55(12), 248:1-248:38. https://doi.org/10.1145/3571730
• Jiang, Z., Ma, X., & Chen, W. (2024). LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs (No. arXiv:2406.15319). arXiv. https://doi.org/10.48550/arXiv.2406.15319
• Jin, B., Yoon, J., Han, J., & Arik, S. O. (2024). Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG (No. arXiv:2410.05983). arXiv. https://doi.org/10.48550/arXiv.2410.05983
• Juvekar, K., & Purwar, A. (2024). COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval (No. arXiv:2406.00638). arXiv. https://doi.org/10.48550/arXiv.2406.00638
• Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182–193. https://doi.org/10.1016/j.tra.2016.09.010
• Kang, L. (2024). Exploring a data-driven framework for safety performance management: A theoretical investigation at the enterprise level. Journal of Loss Prevention in the Process Industries, 91, 105384. https://doi.org/10.1016/j.jlp.2024.105384
• Karacan, H. ve Akçay, M. (2019). Mevzuat metinlerinin anlamsal aranması için ontoloji tabanlı bir yaklaşım. Bilişim Teknolojileri Dergisi, 12(4), 325-334. https://doi.org/10.17671/gazibtd.553258
• Katz, D. M., Bommarito, M. J., Gao, S., & Arredondo, P. (2024). GPT-4 passes the bar exam. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 382(2270), 20230254. https://doi.org/10.1098/rsta.2023.0254
• Koopman, P., & Wagner, M. (2016). Challenges in Autonomous Vehicle Testing and Validation. SAE International Journal of Transportation Safety, 4(1), 15–24.
• Korkusuz, M. E., & Karamete, A. (2017). MMORPG Türünde Geliştirilen Bir Eğitsel Oyunun Basit Elektrik Devreleri Ünitesine Uygulanması ve Çeşitli Değişkenler Bakımından İncelenmesi. Eskişehir Osmangazi Üniversitesi Türk Dünyası Uygulama ve Araştırma Merkezi Eğitim Dergisi, 2(1), 78–96.
• Lee, J., Chen, A., Dai, Z., Dua, D., Sachan, D. S., Boratko, M., Luan, Y., Arnold, S. M. R., Perot, V., Dalmia, S., Hu, H., Lin, X., Pasupat, P., Amini, A., Cole, J. R., Riedel, S., Naim, I., Chang, M.-W., & Guu, K. (2024). Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? (No. arXiv:2406.13121). arXiv. https://doi.org/10.48550/arXiv.2406.13121
• Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
• Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics, 12, 157–173. https://doi.org/10.1162/tacl_a_00638
• Malik, S., Khan, M. A., El-Sayed, H., Khan, J., & Ullah, O. (2023). How Do Autonomous Vehicles Decide? Sensors, 23(1), 317. https://doi.org/10.3390/s23010317
• Mallen, A., Asai, A., Zhong, V., Das, R., Khashabi, D., & Hajishirzi, H. (2023). When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 9802–9822). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.546
• Mao, Y., He, P., Liu, X., Shen, Y., Gao, J., Han, J., & Chen, W. (2021). Generation-augmented retrieval for open-domain question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 4089-4100).
• Marín, J. (2025). Semantic Grounding Index: Geometric Bounds on Context Engagement in RAG Systems (No. arXiv:2512.13771). arXiv. https://doi.org/10.48550/arXiv.2512.13771
• Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2025). Large Language Models: A Survey (No. arXiv:2402.06196). arXiv. https://doi.org/10.48550/arXiv.2402.06196
• Mozaffari, S., Al-Jarrah, O. Y., Dianati, M., Jennings, P., & Mouzakitis, A. (2022). Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review. IEEE Transactions on Intelligent Transportation Systems, 23(1), 33–47. https://doi.org/10.1109/TITS.2020.3012034
• Nicastro, R., Sardu, A., Panchaud, N., & De Virgilio, C. (2017). The Architecture of the Rag GTPase Signaling Network. Biomolecules, 7(3), 48. https://doi.org/10.3390/biom7030048
• Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., & Shoham, Y. (2023). In-Context Retrieval-Augmented Language Models. Transactions of the Association for Computational Linguistics, 11, 1316–1331. https://doi.org/10.1162/tacl_a_00605
• Rau, D., Wang, S., Déjean, H., & Clinchant, S. (2024). Context Embeddings for Efficient Answer Generation in RAG (No. arXiv:2407.09252). arXiv. https://doi.org/10.48550/arXiv.2407.09252 Rawte, V., Sheth, A., & Das, A. (2023). A Survey of Hallucination in Large Foundation Models (No. arXiv:2309.05922). arXiv. https://doi.org/10.48550/arXiv.2309.05922
• Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 3982-3992).
• Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W. (2023). REPLUG: Retrieval-Augmented Black-Box Language Models (No. arXiv:2301.12652). arXiv. https://doi.org/10.48550/arXiv.2301.12652
• Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Retrieval Augmentation Reduces Hallucination in Conversation (No. arXiv:2104.07567). arXiv. https://doi.org/10.48550/arXiv.2104.07567
• Singh, J. (2023). The Ethical Implications of AI and RAG Models in Content Generation: Bias, Misinformation, and Privacy Concerns. Journal of Science & Technology, 4(1), 156–170.
• van Ratingen, M. R. (2017). The Euro NCAP Safety Rating. In A. Piskun (Ed.), Karosseriebautage Hamburg 2017 (pp. 11–20). Springer Fachmedien. https://doi.org/10.1007/978-3-658-18107-9_2
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. ukasz, & Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
• Wu, D., Nie, L., Mumtaz, R. A., & Agarwal, K. (2025). A LLM-Based Hybrid-Transformer Diagnosis System in Healthcare. IEEE Journal of Biomedical and Health Informatics, 29(9), 6428–6439. https://doi.org/10.1109/JBHI.2024.3481412
• Yan, S.-Q., Gu, J.-C., Zhu, Y., & Ling, Z.-H. (2024). Corrective Retrieval Augmented Generation (No. arXiv:2401.15884). arXiv. https://doi.org/10.48550/arXiv.2401.15884
• Zakka, C., Shad, R., Chaurasia, A., Dalal, A. R., Kim, J. L., Moor, M., Fong, R., Phillips, C., Alexander, K., Ashley, E., Boyd, J., Boyd, K., Hirsch, K., Langlotz, C., Lee, R., Melia, J., Nelson, J., Sallam, K., Tullis, S., … Hiesinger, W. (2024). Almanac—Retrieval-Augmented Language Models for Clinical Medicine. NEJM AI, 1(2), AIoa2300068. https://doi.org/10.1056/AIoa2300068
• Zhao, B., Jin, W., Ser, J. D., & Yang, G. (2023). ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification (No. arXiv:2305.15024). arXiv. https://doi.org/10.48550/arXiv.2305.15024
• Zhong, Z., Liu, H., Cui, X., Zhang, X., & Qin, Z. (2025). Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation. In O. Rambow, L. Wanner, M. Apidianaki, H. Al-Khalifa, B. D. Eugenio, & S. Schockaert (Eds), Proceedings of the 31st International Conference on Computational Linguistics (pp. 5756–5774). Association for Computational Linguistics. https://aclanthology.org/2025.coling-main.384/