Workflow-Level Design Principles for Trustworthy GenAI in Automotive System Engineering

2026-02-23

AI Summary

This work addresses the limited applicability of large language models in safety-critical automotive systems engineering due to concerns regarding trustworthiness, traceability, and compatibility with established verification workflows. The authors propose workflow-level design principles for trustworthy generative AI and implement them within an end-to-end automotive engineering pipeline encompassing requirement change identification, SysML v2 architecture updates, and regression testing. To enhance completeness in change detection, they employ segmented prompt decomposition, diversity sampling, and lightweight NLP-based validation. Traceable test generation is achieved through explicit variable-to-port mappings. Experimental results demonstrate that the approach significantly improves the detection rate of critical changes in large-scale specifications, ensures correctness of architectural updates, and enables automated, traceable regression testing—providing a practical foundation for deploying generative AI in safety-critical contexts.

Technology Category

• Natural Language Processing: Generation
• Planning, Routing, and Scheduling: Planning with Language Models
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The adoption of large language models in safety-critical system engineering is constrained by trustworthiness, traceability, and alignment with established verification practices. We propose workflow-level design principles for trustworthy GenAI integration and demonstrate them in an end-to-end automotive pipeline, from requirement delta identification to SysML v2 architecture update and re-testing. First, we show that monolithic ("big-bang") prompting misses critical changes in large specifications, while section-wise decomposition with diversity sampling and lightweight NLP sanity checks improves completeness and correctness. Then, we propagate requirement deltas into SysML v2 models and validate updates via compilation and static analysis. Additionally, we ensure traceable regression testing by generating test cases through explicit mappings from specification variables to architectural ports and states, providing practical safeguards for GenAI used in safety-critical automotive engineering.

Problem

• trustworthiness
• traceability
• safety-critical
• verification
• GenAI

Innovation

• workflow-level design
• requirement delta identification
• SysML v2 integration
• traceable regression testing
• trustworthy GenAI