The safety and reliability of Automated Driving Systems (ADSs) must be validated prior to large-scale deployment. Among existing validation approaches, scenario-based testing has been regarded as a promising method to improve testing efficiency and reduce associated costs. Recently, the emergence of Large Language Models (LLMs) has introduced new opportunities to reinforce this approach. While an increasing number of studies have explored the use of LLMs in the field of automated driving, a dedicated review focusing on their application within scenario-based testing remains absent. This survey addresses this gap by systematically categorizing the roles played by LLMs across various phased of scenario-based testing, drawing from both academic research and industrial practice. In addition, key characteristics of LLMs and corresponding usage strategies are comprehensively summarized. The paper concludes by outlining five open challenges and potential research directions. To support ongoing research efforts, a continuously updated repository of recent advancements and relevant open-source tools is made available at: https://github.com/ftgTUGraz/LLM4ADSTest.

翻译：自动驾驶系统（ADS）在大规模部署前必须经过安全性与可靠性验证。在现有验证方法中，场景化测试被视为提升测试效率、降低相关成本的有效途径。近年来，大语言模型（LLMs）的出现为强化该方法带来了新的机遇。尽管已有越来越多的研究探索LLMs在自动驾驶领域的应用，但聚焦于其在场景化测试中应用的专题综述仍属空白。本综述通过系统梳理学术研究与工程实践，对LLMs在场景化测试各阶段所承担的角色进行了分类归纳。此外，本文全面总结了LLMs的关键特性及相应的使用策略，并最终提出五个开放挑战与潜在研究方向。为支持持续的研究工作，相关最新进展与开源工具的持续更新资源库发布于：https://github.com/ftgTUGraz/LLM4ADSTest。