Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.

翻译：背景：驾驶自动化系统（DAS），包括自动驾驶和高级驾驶辅助，是一个重要的安全关键领域。DAS通常集成使用机器学习（ML）分析车辆环境的感知系统。目标：我们探讨从业者在该领域遇到的新颖或不同的需求工程（RE）主题与挑战。方法：我们开展了一项涉及五家公司19位参与者的访谈研究，并进行了主题分析。结果：从业者难以预先明确需求，往往依赖场景和运行设计域（ODD）作为RE产物。挑战涉及ODD检测与ODD退出检测、真实场景、边缘情况规约、需求分解、可追溯性、数据与标注规约创建，以及量化质量需求。结论：我们的研究结果有助于理解RE在DAS感知系统中的实践方式，所收集的挑战可推动DAS及其他基于ML的系统的未来研究。