執掌方向盤：一項跨公司研究揭示自動駕駛系統測試的真實面貌

摘要

自動駕駛系統（ADS）正快速發展，並日益廣泛部署於實際應用中。這對於有效的測試需求不斷增長，以確保系統功能與安全性。然而，自動駕駛系統測試仍相當複雜，且在場景選擇、效能評估與驗收標準方面缺乏完善成熟的規範。為更深入理解當前自動駕駛系統測試的實務作法與挑戰，我們對來自六個國家、九家企業中從事自動駕駛系統開發與測試的專家進行了訪談研究。透過主題分析法，我們綜整了產業界的測試實務、挑戰、潛在解決方案與未來趨勢，並提出一套以證據為中心的閉環式自動駕駛系統測試框架。研究結果顯示，當前的實務作法主要聚焦於基於場景的測試與X-in-the-loop（X在環）測試方法，並輔以多樣化的工具、指標、基準測試與測試策略。受訪專家強調了與場景真實性、場景覆蓋率、模擬保真度及驗收標準相關的重大挑戰，同時也探討了運用人工智慧、世界模型與端到端方法等潛在解決方案。此外，受訪專家預期未來的自動駕駛系統測試將在整個產業中變得更加自動化、資料驅動且具備更高的透明度。整體而言，本研究提供了以產業實務為基礎的自動駕駛系統測試全面綜述，提出了一套以證據為中心的閉環測試框架，以提供可行的自動駕駛系統測試指導方針，並勾勒出未來研究與實務的重要方向。

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.