As the automotive industry shifts its focus toward software-defined vehicles, the need for faster and reliable software development continues to grow. However, traditional methods show their limitations. The rise of Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), introduces new opportunities to automate automotive software development tasks such as requirement analysis and code generation. However, due to the complexity of automotive systems, where software components must interact with each other seamlessly, challenges remain in software integration and system-level validation. In this paper, we propose to combine GenAI with model-driven engineering to automate automotive software development. Our approach uses LLMs to convert free-text requirements into event chain descriptions and to generate platform-independent software components that realize the required functionality. At the same time, formal models are created based on event chain descriptions to support system validation and the generation of integration code for integrating generated software components in the whole vehicle system through middleware. This approach increases development automation while enabling formal analysis to improve system reliability. As a proof of concept, we used GPT-4o to implement our method and tested it in the CARLA simulation environment with ROS2 middleware. We evaluated the system in a simple Autonomous Emergency Braking scenario.

翻译：随着汽车行业将重心转向软件定义汽车，对更快速、可靠软件开发的需求持续增长。然而，传统方法已显现其局限性。生成式人工智能（GenAI）的兴起，特别是大语言模型（LLMs），为自动化汽车软件开发任务（如需求分析和代码生成）带来了新机遇。但由于汽车系统的复杂性——软件组件必须无缝交互，软件集成和系统级验证仍面临挑战。本文提出将GenAI与模型驱动工程相结合，以实现汽车软件开发的自动化。我们的方法利用LLMs将自由文本需求转换为事件链描述，并生成实现所需功能的平台无关软件组件。同时，基于事件链描述创建形式化模型，以支持系统验证，并通过中间件生成集成代码，将生成的软件组件整合至整车系统中。该方法提升了开发自动化水平，同时支持形式化分析以提高系统可靠性。作为概念验证，我们使用GPT-4o实现了该方法，并在CARLA仿真环境中结合ROS2中间件进行了测试。我们在一个简单的自动紧急制动场景中对系统进行了评估。