掌舵之位：多公司自动驾驶系统测试现实研究

摘要

自动驾驶系统（ADS）正迅速发展，并在现实应用中日益普及。这对开展有效测试以保障系统功能与安全性提出了日益增长的需求。然而，ADS测试仍然复杂，且在场景选择、性能评估和验收标准方面缺乏完善的标准。为更深入地了解当前ADS测试的实践与挑战，我们对来自六个国家九家公司的ADS开发与测试领域专家进行了访谈研究。通过主题分析，我们综合归纳了工业界的测试实践、挑战、潜在解决方案及未来趋势，并提出了一种以证据为中心的ADS闭环测试框架。研究结果表明，当前实践主要聚焦于基于场景的测试和X在环测试方法，并辅以多种工具、指标、基准和测试策略。参与者强调了与场景真实性、场景覆盖率、仿真保真度及验收标准相关的重大挑战，同时探讨了如利用人工智能、世界模型和端到端方法等潜在解决方案。此外，参与者展望未来ADS测试将在整个行业范围内变得更加自动化、数据驱动和透明。总体而言，本研究提供了基于工业实践的ADS测试全面概述，提出了一种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，并指出了未来研究与实践的重要方向。

English

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.