Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
This work addresses the problem of increasing complexity and regulatory demands in automotive software development for engineers and safety-critical systems, representing an incremental advancement by applying existing LLMs to a new domain with specific safety integrations.
This paper tackles the challenge of developing safety-critical automotive software by proposing a framework that integrates Generative AI and Large Language Models to automate code generation, validated through an Adaptive Cruise Control system to ensure compliance with safety standards.
Developing safety-critical automotive software presents significant challenges due to increasing system complexity and strict regulatory demands. This paper proposes a novel framework integrating Generative Artificial Intelligence (GenAI) into the Software Development Lifecycle (SDLC). The framework uses Large Language Models (LLMs) to automate code generation in languages such as C++, incorporating safety-focused practices such as static verification, test-driven development and iterative refinement. A feedback-driven pipeline ensures the integration of test, simulation and verification for compliance with safety standards. The framework is validated through the development of an Adaptive Cruise Control (ACC) system. Comparative benchmarking of LLMs ensures optimal model selection for accuracy and reliability. Results demonstrate that the framework enables automatic code generation while ensuring compliance with safety-critical requirements, systematically integrating GenAI into automotive software engineering. This work advances the use of AI in safety-critical domains, bridging the gap between state-of-the-art generative models and real-world safety requirements.