Accelerating Safety-Critical Automotive Software Development Through Reproducible AI-Assisted Requirements Engineering

Abstract

Automotive software developed under ISO 26262 and ISO/SAE 21434 depends on requirements that are traceable, reviewable, and grounded in safety and cybersecurity evidence. The practical bottleneck is often not requirement writing alone, but turning fragmented project artifacts into auditable specifications at engineering speed. This paper presents CADRE, an AI-assisted requirements-engineering framework for safety- and cybersecurity-critical automotive systems. CADRE combines deterministic parsers for structured artifacts with retrieval-augmented synthesis for semi-structured and unstructured sources. It constrains this workflow through schema validation, source-grounded traceability, expert review gates, fixed decoding controls, and cryptographic provenance tracking. The evaluation covers four industrial automotive modules from ASIL-B to ASIL-D and CAL-2 to CAL-4. CADRE produced 1,022 synthesized requirements, achieved 98.6% traceability coverage, kept the fabrication rate at 0.2%, and produced byte-identical outputs across independent runs. The results indicate that language-model assistance can add value in regulated automotive requirements workflows when it is embedded in deterministic, provenance-rich, and expert-governed processes.