A RAG-based Large Language Model Framework for Tracing Requirements to Design Information of Automated Driving Systems

A RAG-based Large Language Model Framework for Tracing Requirements to Design Information of Automated Driving Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A RAG-based Large Language Model Framework for Tracing Requirements to Design Information of Automated Driving Systems Peng Su, Rui Xu, Jiacai Huang, Dejiu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8541175/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract The development of Automated Driving Systems (ADS) entails managing hierarchical architectural views in which driving features are systematically specified, implemented, verified and validated with heterogeneous design artifacts. Due to the safety-critical nature of such systems, one of the most important development tasks is to systematically manage the system requirements across multiple abstraction levels, ranging from high-level system features to detailed technical specifications and corresponding verification and validation test cases. Recent advances in Large Language Models (LLM) show limited applicability in such system development context, as models trained on large-scale open-domain corpora often lack the precious, domain-specific reasoning required for requirement analysis beyond general linguistic competence. To alleviate these limitations in requirement analysis of ADS, this paper presents a functional framework that integrates a toolchain combining system models and LLM to trace requirements to design information through Retrieval-Augmented Generation (RAG)–based solutions. Specifically, we propose a knowledge base by modeling the design information of ADS. The knowledge base is constructed by graph-based models, supporting an interpretable process of multi-depth retrieval and reasoning. As a result, the LLM makes precious responses by integrating the input requirements and the retrieved domain knowledge. To evaluate the proposed framework, we create a dataset by synthesizing design information from public resources. In addition, we use a more extensive public dataset to assess the performance of the framework in terms of robustness and adaptiveness. The results indicate the proposed framework shows significant improvement over the baseline methods. Requirement Analysis Graph-based Models Large Language Models Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Reviewers agreed at journal 16 Jan, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8541175","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575653031,"identity":"9187001d-d270-4616-b3c6-afe845a0b697","order_by":0,"name":"Peng Su","email":"","orcid":"","institution":"Nanjing Institute of Technology,","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Su","suffix":""},{"id":575653033,"identity":"359e89e0-fcb7-450b-8fd6-c3f260a3ebce","order_by":1,"name":"Rui Xu","email":"","orcid":"","institution":"KTH Royal Institute of 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Due to the safety-critical nature of such systems, one of the most important development tasks is to systematically manage the system requirements across multiple abstraction levels, ranging from high-level system features to detailed technical specifications and corresponding verification and validation test cases. Recent advances in Large Language Models (LLM) show limited applicability in such system development context, as models trained on large-scale open-domain corpora often lack the precious, domain-specific reasoning required for requirement analysis beyond general linguistic competence. To alleviate these limitations in requirement analysis of ADS, this paper presents a functional framework that integrates a toolchain combining system models and LLM to trace requirements to design information through Retrieval-Augmented Generation (RAG)\u0026ndash;based solutions. Specifically, we propose a knowledge base by modeling the design information of ADS. 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