用大模型自动把汽车需求转为可运行的ADAS代码和测试场景。
GenAI for Automotive Software Development: From Requirements to Wheels
- 用大模型从需求生成ADAS代码、测试场景与验证约束。
- 结合法规文档增强测试场景生成，提升合规性。
- 适合汽车软件开发团队缩短研发周期。
本文提出一种基于生成式AI的汽车软件自动化开发方法，聚焦自动驾驶与高级驾驶辅助系统（ADAS）功能。输入为系统需求，输出包括用于仿真环境的测试场景代码，以及面向车辆硬件平台的ADAS实现代码。引入基于模型驱动工程（MDE）的需求一致性检查机制，利用大语言模型（LLMs）完成需求的模型化摘要（Ecore元模型、XMI模型实例及OCL约束生成）、测试场景生成、仿真代码（Python）和目标平台代码（C++）生成。同时采用检索增强生成（RAG）技术，从自动驾驶法规文档中提取信息以增强测试场景生成。该流程旨在缩短合规与重构周期，显著减少ADAS相关功能的开发与测试时间。
原文摘要 · Abstract (English)
This paper introduces a GenAI-empowered approach to automated development of automotive software, with emphasis on autonomous and Advanced Driver Assistance Systems (ADAS) capabilities. The process starts with requirements as input, while the main generated outputs are test scenario code for simulation environment, together with implementation of desired ADAS capabilities targeting hardware platform of the vehicle connected to testbench. Moreover, we introduce additional steps for requirements consistency checking leveraging Model-Driven Engineering (MDE). In the proposed workflow, Large Language Models (LLMs) are used for model-based summarization of requirements (Ecore metamodel, XMI model instance and OCL constraint creation), test scenario generation, simulation code (Python) and target platform code generation (C++). Additionally, Retrieval Augmented Generation (RAG) is adopted to enhance test scenario generation from autonomous driving regulations-related documents. Our approach aims shorter compliance and re-engineering cycles, as well as reduced development and testing time when it comes to ADAS-related capabilities.
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