Ensuring the safety and reliability of Automated Driving Systems (ADS) remains a critical challenge, as traditional verification methods such as large-scale on-road testing are prohibitively costly and time-consuming.To address this,scenario-based testing has emerged as a scalable and efficient alternative,yet existing surveys provide only partial coverage of recent methodological and technological advances.This review systematically analyzes 31 primary studies,and 10 surveys identified through a comprehensive search spanning 2015~2025;however,the in-depth methodological synthesis and comparative evaluation focus primarily on recent frameworks(2023~2025),reflecting the surge of Artificial Intelligent(AI)-assisted and multimodal approaches in this period.Traditional approaches rely on expert knowledge,ontologies,and naturalistic driving or accident data,while recent developments leverage generative models,including large language models,generative adversarial networks,diffusion models,and reinforcement learning frameworks,to synthesize diverse and safety-critical scenarios.Our synthesis identifies three persistent gaps:the absence of standardized evaluation metrics,limited integration of ethical and human factors,and insufficient coverage of multimodal and Operational Design Domain (ODD)-specific scenarios.To address these challenges,this review contributes a refined taxonomy that incorporates multimodal extensions,an ethical and safety checklist for responsible scenario design,and an ODD coverage map with a scenario-difficulty schema to enable transparent benchmarking.Collectively,these contributions provide methodological clarity for researchers and practical guidance for industry,supporting reproducible evaluation and accelerating the safe deployment of higher-level ADS.

翻译：确保自动驾驶系统的安全性和可靠性仍是一项关键挑战，因为传统的验证方法（如大规模道路测试）成本高昂且耗时。为应对这一问题，基于场景的测试已成为一种可扩展且高效的替代方案，然而现有综述仅部分覆盖了近期方法论和技术进展。本文系统性地分析了31项主要研究，以及通过2015~2025年全面检索识别的10篇综述；但深度方法论综合与比较评估主要聚焦于近期框架（2023~2025年），反映了这一时期人工智能辅助与多模态方法的激增。传统方法依赖专家知识、本体论以及自然驾驶或事故数据，而近期发展则利用生成模型（包括大型语言模型、生成对抗网络、扩散模型和强化学习框架）合成多样化的安全关键场景。我们的综合分析识别出三个持续存在的空白：缺乏标准化评估指标、伦理与人因因素整合有限、以及多模态和运行设计域特定场景覆盖不足。为应对这些挑战，本综述贡献了一个细化的分类体系：包含多模态扩展、用于负责任场景设计的伦理与安全检查清单，以及结合场景难度模式的ODD覆盖图谱，以支持透明化基准测试。总体而言，这些贡献为研究人员提供了方法论清晰性，并为行业提供了实践指导，支持可复现评估并加速高级别自动驾驶系统的安全部署。