[2207.09259] Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

<!DOCTYPE html> <html lang="en"> <head> <title>[2207.09259] Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios</title> <meta name="viewport" content="width=device-width, initial-scale=1"> <link rel="apple-touch-icon" sizes="180x180" href="/static/browse/0.3.4/images/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="/static/browse/0.3.4/images/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="/static/browse/0.3.4/images/icons/favicon-16x16.png"> <link rel="manifest" href="/static/browse/0.3.4/images/icons/site.webmanifest"> <link rel="mask-icon" href="/static/browse/0.3.4/images/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="theme-color" content="#ffffff"> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/arXiv.css?v=20241206" /> <link rel="stylesheet" type="text/css" media="print" href="/static/browse/0.3.4/css/arXiv-print.css?v=20200611" /> <link rel="stylesheet" type="text/css" media="screen" href="/static/browse/0.3.4/css/browse_search.css" /> <script language="javascript" src="/static/browse/0.3.4/js/accordion.js" /></script> <link rel="canonical" href="https://arxiv.org/abs/2207.09259"/> <meta name="description" content="Abstract page for arXiv paper 2207.09259: Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios"><meta property="og:type" content="website" /> <meta property="og:site_name" content="arXiv.org" /> <meta property="og:title" content="Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios" /> <meta property="og:url" content="https://arxiv.org/abs/2207.09259v1" /> <meta property="og:image" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:secure_url" content="/static/browse/0.3.4/images/arxiv-logo-fb.png" /> <meta property="og:image:width" content="1200" /> <meta property="og:image:height" content="700" /> <meta property="og:image:alt" content="arXiv logo"/> <meta property="og:description" content="Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Adaptive Testing for Connected and Automated Vehicles with Sparse..."/> <meta name="twitter:description" content="Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios" /><meta name="citation_author" content="Yang, Jingxuan" /><meta name="citation_author" content="He, Honglin" /><meta name="citation_author" content="Zhang, Yi" /><meta name="citation_author" content="Feng, Shuo" /><meta name="citation_author" content="Liu, Henry X." /><meta name="citation_date" content="2022/07/19" /><meta name="citation_online_date" content="2022/07/19" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2207.09259" /><meta name="citation_arxiv_id" content="2207.09259" /><meta name="citation_abstract" content="Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a 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class="descriptor">Authors:</span><a href="https://arxiv.org/search/eess?searchtype=author&query=Yang,+J" rel="nofollow">Jingxuan Yang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=He,+H" rel="nofollow">Honglin He</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Zhang,+Y" rel="nofollow">Yi Zhang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Feng,+S" rel="nofollow">Shuo Feng</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Liu,+H+X" rel="nofollow">Henry X. Liu</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios, by Jingxuan Yang and 4 other authors</div> <a class="mobile-submission-download" href="/pdf/2207.09259">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times. </blockquote>  <div class="metatable"> <table summary="Additional metadata"><tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Systems and Control (eess.SY)</span></td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2207.09259">arXiv:2207.09259</a> [eess.SY]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2207.09259v1">arXiv:2207.09259v1</a> [eess.SY]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2207.09259" 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From: Jingxuan Yang [<a href="/show-email/ce298b30/2207.09259" rel="nofollow">view email</a>] <br/> <strong>[v1]</strong> Tue, 19 Jul 2022 13:17:09 UTC (194 KB)<br/> </div> </div>  <div class="extra-services"> <div class="full-text"> <a name="other"></a> <span class="descriptor">Full-text links:</span> <h2>Access Paper:</h2> <ul> <div id="download-button-info" hidden> View a PDF of the paper titled Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios, by Jingxuan Yang and 4 other authors</div><li><a href="/pdf/2207.09259" aria-describedby="download-button-info" accesskey="f" class="abs-button download-pdf">View PDF</a></li><li><a href="/src/2207.09259" class="abs-button download-eprint">TeX Source</a></li><li><a href="/format/2207.09259" class="abs-button download-format">Other Formats</a></li></ul> <div class="abs-license"><a href="http://creativecommons.org/licenses/by/4.0/" title="Rights to this article" class="has_license"> <img 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[2207.09259] Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios