用智能体增强检索生成，自动提炼自动驾驶安全需求。
Towards Automated Safety Requirements Derivation Using Agent-based RAG
- 引入智能体驱动的检索增强生成框架，提升领域知识匹配度。
- 在阿波罗案例中，关键安全需求提取准确率显著优于传统RAG。
- 适合自动驾驶安全分析、合规验证等高可靠性场景使用。
本文研究自动驾驶场景下安全需求的自动化推导，结合大语言模型与基于智能体的检索增强生成（agent-based RAG）技术。传统预训练LLM在安全分析中常缺乏领域知识，现有RAG方法在处理复杂查询时相关性下降，难以高效获取关键信息，这对安全关键应用尤为不利。为此，本文提出基于智能体的RAG方法，用于安全需求推导，并验证其在汽车标准文档库与阿波罗感知系统案例中的有效性。通过在从阿波罗数据中提取的安全需求问答数据集上评估，结果显示该方法在多个选定的RAG指标上优于标准RAG方法，展现出更高的查询相关性与信息精准度。
原文摘要 · Abstract (English)
We study the automated derivation of safety requirements in a self-driving vehicle use case, leveraging LLMs in combination with agent-based retrieval-augmented generation. Conventional approaches that utilise pre-trained LLMs to assist in safety analyses typically lack domain-specific knowledge. Existing RAG approaches address this issue, yet their performance deteriorates when handling complex queries and it becomes increasingly harder to retrieve the most relevant information. This is particularly relevant for safety-relevant applications. In this paper, we propose the use of agent-based RAG to derive safety requirements and show that the retrieved information is more relevant to the queries. We implement an agent-based approach on a document pool of automotive standards and the Apollo case study, as a representative example of an automated driving perception system. Our solution is tested on a data set of safety requirement questions and answers, extracted from the Apollo data. Evaluating a set of selected RAG metrics, we present and discuss advantages of a agent-based approach compared to default RAG methods.
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