掌控全局：关于自动驾驶系统测试现状的多公司研究
In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing
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中文总结 AI 辅助
针对自动驾驶系统测试复杂且缺乏标准的问题，通过对九家公司专家访谈，经主题分析总结行业测试情况、挑战与趋势，提出以证据为中心的闭环测试框架，为ADS测试提供指导并指明未来方向。
中文摘要 AI 辅助
自动驾驶系统（ADS）正在迅速发展并越来越多地应用于实际场景中，这对有效测试以确保系统功能和安全性提出了更高要求。然而，ADS测试仍然复杂，在场景选择、性能评估和验收标准方面缺乏完善的标准。为更好地了解当前ADS测试实践与挑战，我们对来自六个不同国家的九家公司中从事ADS开发和测试的专家进行了访谈研究。通过主题分析，我们总结了行业测试实践、挑战、潜在解决方案、未来趋势，并提出了一个以证据为中心的ADS测试闭环框架。研究结果表明，当前实践主要集中在基于场景和X-in-the-loop测试方法上，有各种工具、指标、基准和测试策略支持。参与者强调了与场景真实性、场景覆盖、模拟逼真度和验收标准相关的主要挑战，同时也讨论了潜在解决方案，如使用人工智能、世界模型和端到端方法。此外，参与者设想未来的ADS测试将在整个行业中变得更加自动化、数据驱动和透明。总体而言，本研究提供了一个全面的基于行业的ADS测试概述，提出了一个以证据为中心的闭环测试框架，为ADS测试提供可操作的指导，并概述了未来研究和实践的重要方向。
英文摘要
Autonomous driving systems (ADS) are rapidly advancing and increasingly deployed in real-world applications. This creates growing demands for effective testing to ensure system functionality and safety. However, ADS testing remains complex and lacks well-established standards for scenario selection, performance evaluation, and acceptance criteria. To better understand current ADS testing practices and challenges, we conducted an interview study with experts working on ADS development and testing in nine companies from six different countries. Through thematic analysis, we synthesized industrial testing practices, challenges, potential solutions, future trends, and proposed an evidence-centered closed-loop testing framework for ADS testing. Our findings show that current practices primarily focus on scenario-based and X-in-the-loop testing approaches, supported by diverse tools, metrics, benchmarks, and testing strategies. The participants highlighted major challenges related to scenario realism, scenario coverage, simulation fidelity, and acceptance criteria, while also discussing potential solutions such as the use of AI, world models, and end-to-end approaches. Furthermore, participants envisioned future ADS testing to become more automated, data-driven, and transparent across the industry. Overall, this study provides a comprehensive industry-grounded overview of ADS testing, proposes an evidence-centered closed-loop testing framework to provide actionable guidance for ADS testing, and outlines important directions for future research and practice.
