Optimizing Test Case Sampling for Safety Validation of Automated Driving System with Naturalistic Driving Study

Optimizing Test Case Sampling for Safety Validation of Automated Driving System with Naturalistic Driving Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimizing Test Case Sampling for Safety Validation of Automated Driving System with Naturalistic Driving Study Feng Guo, Qian Chen, Jingbin Xu, Xin Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6130565/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Validating the safety of automated driving systems (ADS) demands a thoughtful strategy for constructing appropriate test cases that enable direct and fair comparisons with human drivers. The complexity of driving and the rarity of safety-critical situations pose challenges in creating a reliable and efficient validation framework. This paper addresses these issues by selecting appropriate test cases from the largest-scale naturalistic driving study (NDS). We introduce a novel Kernel Test Case Sampling (KTCS) method, which selects cases satisfying two key criteria: representativeness, ensuring alignment with real-world scenarios, and coverage, capturing high-risk corner cases. By selecting 118 cases, our method effectively captures long-tailed scenarios while approximating the NDS distribution. Additionally, we provide a reliable approach for calculating accident rates, enabling fair comparisons with human drivers. Our method supports standardized and scalable ADS safety validation, facilitating accelerated development and deployment while building public trust and regulatory confidence. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Statistics Full Text Additional Declarations There is NO Competing Interest. Suggested reviewers: Dr. Henry Liu, ( [email protected] ) Supplementary Files SupplementaryMaterial.pdf Supplementary materials for detailed proof of theorems and simulation study setups Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6130565","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424134815,"identity":"2cc4bdae-bac7-47c8-9e22-b1f16936137b","order_by":0,"name":"Feng Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBAC9gYogx9MshGhhecAlCHZwEyqFoMDRGth7z38guHXYTnjG/kHGD6UHSZCC8+5NAvGvsPGZjeSGRhnnCNCi71EjpkBY8/hxG1nDjMw87YRY4v8G4iWzT1ALX+J0iLBY/yA4cfhxA3szQzMjERp4ckxY0hsSDeWON5scLDnXDoRWtjPGH/48Mdajr+Z8eGDH2XWhLUAAZtEYhuEdYAo9UDA/IHhD7FqR8EoGAWjYEQCAOUqORUuwcsWAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2572-481X","institution":"Virginia Polytechnic Institute and State University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Guo","suffix":""},{"id":424134816,"identity":"aefed383-7a04-4abb-bade-c04b0e853a10","order_by":1,"name":"Qian Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Chen","suffix":""},{"id":424134817,"identity":"3a852f8d-f2d5-4849-b638-7843d7239413","order_by":2,"name":"Jingbin Xu","email":"","orcid":"https://orcid.org/0000-0003-3392-2514","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Jingbin","middleName":"","lastName":"Xu","suffix":""},{"id":424134818,"identity":"076b8e30-93ed-47eb-9a79-eb79b7b3e704","order_by":3,"name":"Xin Xing","email":"","orcid":"https://orcid.org/0000-0001-9121-0086","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2025-02-28 17:25:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6130565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6130565/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-69675-8","type":"published","date":"2026-02-24T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105883113,"identity":"9b6504d0-2c54-4263-80e5-7716b30721cc","added_by":"auto","created_at":"2026-04-01 07:06:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2152735,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Naturecommunicationsamplingproject.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6130565/v1_covered_6a22b422-ce79-496c-a05a-e93e21a8c695.pdf"},{"id":77841894,"identity":"7cb67f05-d8a9-44c4-93b0-2eee1287218d","added_by":"auto","created_at":"2025-03-06 05:06:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1995479,"visible":true,"origin":"","legend":"Supplementary materials for detailed proof of theorems and simulation study setups","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6130565/v1/a0df01ae3d1b69b3043519d6.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.\nSuggested reviewers: Dr. Henry Liu, ([email protected])","formattedTitle":"Optimizing Test Case Sampling for Safety Validation of Automated Driving System with Naturalistic Driving Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6130565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6130565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Validating the safety of automated driving systems (ADS) demands a thoughtful strategy for constructing appropriate test cases that enable direct and fair comparisons with human drivers. 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