Global compliance-driven test case generation for automotive communication protocols via dynamic agentic retrieval-augmented generation

As global Intelligent Connected Vehicles (ICVs) deployment accelerates, onboard communication systems face critical challenges from multi-regional standards and dynamic regulations. Conventional manual or scripted test case generation exhibits insufficient efficiency and coverage, while static large language models struggle with knowledge obsolescence and cross-domain reasoning. This paper proposes an Agentic RAG (Retrieval-Augmented Generation with intelligent agents) framework to automate automotive communication protocol testing. By constructing a heterogeneous knowledge base spanning global standards and localized scenarios, and implementing multi-agent collaboration for dynamic retrieval and adaptive case generation, the framework achieves autonomous parsing of complex test requirements. Evaluations on authentic multi-regional datasets covering China, the European Union, the USA, Japan, and Southeast Asia demonstrate superior coverage and generation efficiency, while achieving compliance accuracy comparable to manual engineering compared to conventional methods and baseline RAG models. This work provides an AI-driven solution for accelerating regional market access and reducing compliance risks in global ICV deployment.