Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems

TL;DR

This paper introduces a framework that leverages Large Language Models to automate and verify safety-critical automotive software development, ensuring compliance with safety standards through integrated testing and verification.

Contribution

It presents a novel integration of GenAI and LLMs into the automotive SDLC for automated, safety-compliant code generation and verification.

Findings

Automated code generation for safety-critical systems achieved.

Framework ensures compliance with automotive safety standards.

Benchmarking identifies optimal LLMs for accuracy and reliability.

Abstract

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.