This study provides a comprehensive industry-grounded overview of ADS testing, proposes<br>an evidence-centered closed-loop testing framework to provide actionable guidance for ADS testing, and<br>outlines important directions for future research and practice.</p>\n","updatedAt":"2026-08-03T09:25:42.040Z","author":{"_id":"672288dd7055eec76d377268","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/672288dd7055eec76d377268/bBQ51VwKZJIkHkNffuEuJ.jpeg","fullname":"Yuan Gao","name":"Yuan-avs","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8474350571632385},"editors":["Yuan-avs"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/672288dd7055eec76d377268/bBQ51VwKZJIkHkNffuEuJ.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2607.15820","authors":[{"_id":"6a705e4802c90f968f48a1d3","name":"Qunying Song","hidden":false},{"_id":"6a705e4802c90f968f48a1d4","name":"Yuan Gao","hidden":false},{"_id":"6a705e4802c90f968f48a1d5","name":"Johannes Betz","hidden":false},{"_id":"6a705e4802c90f968f48a1d6","name":"Dietmar Pfahl","hidden":false},{"_id":"6a705e4802c90f968f48a1d7","name":"Mohammad Reza Mousavi","hidden":false},{"_id":"6a705e4802c90f968f48a1d8","name":"Federica Sarro","hidden":false}],"publishedAt":"2026-07-17T00:00:00.000Z","submittedOnDailyAt":"2026-08-03T00:00:00.000Z","title":"In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing","submittedOnDailyBy":{"_id":"672288dd7055eec76d377268","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/672288dd7055eec76d377268/bBQ51VwKZJIkHkNffuEuJ.jpeg","isPro":true,"fullname":"Yuan Gao","user":"Yuan-avs","type":"user","name":"Yuan-avs"},"summary":"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.","upvotes":1,"discussionId":"6a705e4902c90f968f48a1d9","organization":{"_id":"6a4f83663e43ae629a202b05","name":"TUM-AVS","fullname":"TUM - Professorship of Autonomous Vehicle Systems","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/6723459e7ddd97700c1c1c6a/AJSXS1Qq8ywx4puQ91Fhm.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"65c4eb7cd1dcbd30d86febec","avatarUrl":"/avatars/001c8f02e8ce794b2c21883628b2da72.svg","isPro":false,"fullname":"free-bit","user":"free-bit","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"6a4f83663e43ae629a202b05","name":"TUM-AVS","fullname":"TUM - Professorship of Autonomous Vehicle Systems","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/6723459e7ddd97700c1c1c6a/AJSXS1Qq8ywx4puQ91Fhm.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2607/2607.15820.md","query":{}}">
In the Driver's Seat: A Multi-Company Study on the Reality of Autonomous Driving System Testing
Abstract
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.
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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.
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Cite arxiv.org/abs/2607.15820 in a model README.md to link it from this page.
Cite arxiv.org/abs/2607.15820 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2607.15820 in a Space README.md to link it from this page.
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