用大模型自动生成符合安全标准的汽车代码，提升开发效率与合规性。
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
- 用大模型生成C++代码，结合静态验证与测试驱动开发。
- 在自适应巡航系统上验证，生成代码满足安全标准要求。
- 适合汽车软件开发者与AI工程化研究者参考。
由于系统复杂度提升和严格监管要求，开发安全关键型汽车软件面临巨大挑战。本文提出一种将生成式人工智能（GenAI）融入软件开发生命周期（SDLC）的新框架。该框架利用大语言模型（LLMs）自动编写C++代码，融合静态验证、测试驱动开发及迭代优化等安全实践。通过反馈驱动的流水线，集成测试、仿真与验证，确保符合安全标准。框架在自适应巡航控制（ACC）系统开发中进行验证，并通过对比基准测试筛选出最优的LLM以保障准确性和可靠性。结果表明，该框架可实现代码自动生成，同时确保满足安全关键需求，系统性地将GenAI引入汽车软件工程。本工作推动了AI在安全关键领域的应用，弥合了先进生成模型与真实安全要求之间的差距。
原文摘要 · Abstract (English)
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.
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