Testing autonomous driving systems for safety and reliability is extremely complex. A primary challenge is identifying the relevant test scenarios, especially the critical ones that may expose hazards or risks of harm to autonomous vehicles and other road users. There are several proposed methods and tools for critical scenario identification, while the industry practices, such as the selection, implementation, and limitations of the approaches, are not well understood. In this study, we conducted 10 interviews with 13 interviewees from 7 companies in autonomous driving in Sweden. We used thematic modeling to analyse and synthesize the interview data. We found there are little joint efforts in the industry to explore different approaches and tools, and every approach has its own limitations and weaknesses. To that end, we recommend combining different approaches available, collaborating among different stakeholders, and continuously learning the field of critical scenario identification and testing. The contributions of our study are the exploration and synthesis of the industry practices and related challenges for critical scenario identification and testing, and the potential increase of the industry relevance for future studies in related topics.

翻译：测试自动驾驶系统的安全性与可靠性极为复杂。首要挑战在于识别相关测试场景，尤其是可能暴露自动驾驶车辆及其他道路使用者危害或风险的关键场景。目前已有多种关键场景识别方法与工具，但工业实践中的方法选择、实施及局限性等方面尚不明确。本研究对瑞典7家自动驾驶企业的13名受访者进行了10次访谈，采用主题建模分析并综合访谈数据。研究发现，行业内缺乏联合探索不同方法与工具的协作，且每种方法均有其局限与不足。为此，我们建议综合运用现有不同方法，加强利益相关方合作，并持续跟进关键场景识别与测试领域的发展。本研究的贡献在于探索并综合了关键场景识别与测试的工业实践及相关挑战，同时有望提升相关课题未来研究的工业相关性。