Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?

<!DOCTYPE html>     <html class="no-js" lang="en">  <head> <title>Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios? - ADS</title>  <link rel="apple-touch-icon" sizes="180x180" href="//styles/favicon/apple-touch-icon.png" /> <link rel="icon" type="image/png" sizes="32x32" href="//styles/favicon/favicon-32x32.png" /> <link rel="icon" type="image/png" sizes="16x16" href="//styles/favicon/favicon-16x16.png" /> <link rel="manifest" href="//styles/favicon/site.webmanifest" /> <link rel="mask-icon" href="//styles/favicon/safari-pinned-tab.svg" color="#5bbad5" /> <meta name="apple-mobile-web-app-title" content="ADS" /> <meta name="application-name" content="ADS" /> <meta name="msapplication-TileColor" content="#ffc40d" /> <meta name="theme-color" content="#ffffff" />   <script>(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-NT2453N');</script>  <link rel="stylesheet" href="/styles/css/styles.css"> <meta name="robots" content="noarchive"> <link rel="canonical" href="http://ui.adsabs.harvard.edu/abs/2021arXiv210703078G/abstract"/> <meta name="description" content="Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication.">  <meta property="og:type" content="eprint"> <meta property="og:title" content="Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?"> <meta property="og:site_name" content="ADS"> <meta property="og:description" content="Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication."> <meta property="og:url" content="https://ui.adsabs.harvard.edu/abs/2021arXiv210703078G/abstract"> <meta property="og:image" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"> <meta property="article:published_time" content="07/2021"> <meta property="article:author" content="Garg, Mohit"> <meta property="article:author" content="Johnston, Cian"> <meta property="article:author" content="Bouroche, M茅lanie">  <meta name="citation_journal_title" content="arXiv e-prints"> <meta name="citation_authors" content="Garg, Mohit;Johnston, Cian;Bouroche, M茅lanie"> <meta name="citation_title" content="Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?"> <meta name="citation_date" content="07/2021"> <meta name="citation_firstpage" content="arXiv:2107.03078"> <meta name="citation_doi" content="10.48550/arXiv.2107.03078"> <meta name="citation_language" content="en"> <meta name="citation_keywords" content="Computer Science - Distributed"> <meta name="citation_keywords" content="Parallel"> <meta name="citation_keywords" content="and Cluster Computing"> <meta name="citation_keywords" content="Electrical Engineering and Systems Science - Signal Processing"> <meta name="citation_keywords" content="Electrical Engineering and Systems Science - Systems and Control"> <meta name="citation_abstract_html_url" content="https://ui.adsabs.harvard.edu/abs/2021arXiv210703078G/abstract"> <meta name="citation_publication_date" content="07/2021"> <meta name="citation_arxiv_id" content="arXiv:2107.03078" /> <link title="schema(PRISM)" rel="schema.prism" href="http://prismstandard.org/namespaces/1.2/basic/" /> <meta name="prism.publicationDate" content="07/2021" /> <meta name="prism.publicationName" content="arXiv" /> <meta name="prism.startingPage" content="arXiv:2107.03078" /> <link title="schema(DC)" rel="schema.dc" href="http://purl.org/dc/elements/1.1/" /> <meta name="dc.identifier" content="doi:10.48550/arXiv.2107.03078" /> <meta name="dc.date" content="07/2021" /> <meta name="dc.source" content="arXiv" /> <meta name="dc.title" content="Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?" /> <meta name="dc.creator" content="Garg, Mohit"> <meta name="dc.creator" content="Johnston, Cian"> <meta name="dc.creator" content="Bouroche, M茅lanie">  <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:description" content="Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication."/> <meta name="twitter:title" content="Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?"/> <meta name="twitter:site" content="@adsabs"/> <meta name="twitter:domain" content="ADS"/> <meta name="twitter:image:src" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"/> <meta name="twitter:creator" content="@adsabs"/> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <base href="/"> <style> .btn-full-ads { color: #fff !important; background-color: #1a1a1a !important; border-color: #1a1a1a !important; margin-top: 9px !important; padding-bottom: 10px !important; padding-top: 10px !important; } .btn-full-ads:hover, .btn-full-ads:focus, .btn-full-ads:active, .btn-full-ads.active, .open>.dropdown-toggle.btn-full-ads { color: #000 !important; 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<li class="author"><a href="/search/?q=author%3A%22Johnston%2C+Cian%22">Johnston, Cian</a> </li>; <li class="author"><a href="/search/?q=author%3A%22Bouroche%2C+M%C3%A9lanie%22">Bouroche, M茅lanie</a> </li> </ul> </div> <div class="s-abstract-text"> <h4 class="sr-only">Abstract</h4> <p> Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication. </p> </div> <br> <dl class="s-abstract-dl-horizontal"> <dt>Publication:</dt> <dd> <div id="article-publication">arXiv e-prints</div> </dd> <dt>Pub Date:</dt> <dd>July 2021</dd> <dt>DOI:</dt> <dd> <p class="doi-p"> <a href="/link_gateway/2021arXiv210703078G/doi:10.48550/arXiv.2107.03078" target="_blank" rel="noreferrer noopener">10.48550/arXiv.2107.03078</a> <i class="fa fa-external-link"></i> </p> </dd> <dt>arXiv:</dt> <dd> <span> <a href="/link_gateway/2021arXiv210703078G/arXiv:2107.03078" target="_blank" rel="noreferrer noopener">arXiv:2107.03078</a> <i class="fa fa-external-link"></i> </span> </dd> <dt>Bibcode:</dt> <dd> <a href="/abs/2021arXiv210703078G/abstract"> 2021arXiv210703078G </a> <i class="icon-help" title="The bibcode is assigned by the ADS as a unique identifier for the paper."></i> </dd> <dt>Keywords:</dt> <dd> <ul class="list-inline"> <li>Computer Science - Distributed;</li> <li>Parallel;</li> <li>and Cluster Computing;</li> <li>Electrical Engineering and Systems Science - Signal Processing;</li> <li>Electrical Engineering and Systems Science - Systems and Control</li> </ul> </dd> </dl> </article> </div> <div data-widget="ShowCitations"></div> <div data-widget="ShowReferences"></div> <div data-widget="ShowCoreads"></div> <div data-widget="ShowSimilar"></div> <div data-widget="ShowTableofcontents"></div> <div data-widget="ShowGraphics"></div> <div data-widget="ShowExportcitation" data-origin="abstract"></div> <div data-widget="ShowMetrics" data-allow-redirect="false"></div> <div data-widget="MetaTagsWidget"></div> </div> </div> </div> <div class="s-right-col-container col-xs-12 col-sm-12 col-md-3 col-lg-2 s-right-column" id="right-col-container" > <div data-widget="ShowResources"> <div data-reactroot="" class="s-right-col-widget-container" style="padding: 10px" > <div> <div class="resources__container"> <div class="resources__full__list"> <div class="resources__header__row"> <i class="fa fa-file-text-o" aria-hidden="true"> </i> <div class="resources__header__title">full text sources</div> </div> <div class="resources__content"> <div class="resources__content__title">Preprint</div> <div class="resources__content__links"> <span> <a href="/link_gateway/2021arXiv210703078G/EPRINT_PDF" rel="noopener" class="resources__content__link unlock" > <i class="fa fa-file-pdf-o" aria-hidden="true"> </i> </a> <div class="resources__content__link__separator">|</div> </span> <span> <a href="/link_gateway/2021arXiv210703078G/EPRINT_HTML" rel="noopener" class="resources__content__link unlock" > <i class="fa fa-file-text" aria-hidden="true"> </i> </a> </span> </div> </div> </div> </div> <div data-widget="ShowAssociated"> </div> </div> </div> </div> <div data-widget="ShowGraphicsSidebar"> </div> </div> </div> </div> </div> </div> </div> <div id="footer-container"> <div data-widget="FooterWidget"> <div class="footer s-footer"> <footer> <div class="__footer_wrapper"> <div class="__footer_brand"> 漏 The SAO Astrophysics Data System <div class="__footer_brand_extra"> <p> <i class="fa fa-envelope"></i> adshelp[at]cfa.harvard.edu </p> <p> The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement <em>80NSSC21M0056</em> </p> </div> <div class="__footer_brand_logos"> <div class="logo1"> <a href="http://www.si.edu" target="_blank" rel="noreferrer noopener"> <img id="smithsonian-logo" src="/styles/img/smithsonian-logo.svg" alt="Smithsonian logo" /> </a> </div> <div class="logo2"> <a href="https://www.cfa.harvard.edu/" target="_blank" rel="noreferrer noopener"> <img src="/styles/img/cfa.png" alt="Harvard Center for Astrophysics logo" id="cfa-logo" /> </a> </div> <div class="logo3"> <a href="http://www.nasa.gov" target="_blank" rel="noreferrer noopener"> <img src="/styles/img/nasa-partner.svg" alt="NASA logo" id="nasa-logo" /> </a> </div> </div> <div class="__footer_brand_disclaimer"> *The material contained in this document is based upon work supported by a National Aeronautics and Space Administration (NASA) grant or cooperative agreement. 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// Add class "autocomplete-active": x[currentFocus].classList.add("autocomplete-active"); } function removeActive(x) { // Remove the "active" class from all autocomplete items: for (var i = 0; i < x.length; i++) { x[i].classList.remove("autocomplete-active"); } } function closeAllLists(elmnt) { // Close all autocomplete lists in the document, except the one passed as an argument: var x = document.getElementsByClassName("autocomplete-items"); for (var i = 0; i < x.length; i++) { if (elmnt != x[i] && elmnt != searchBox) { x[i].parentNode.removeChild(x[i]); } } } // Any other clicks in the document: document.addEventListener("click", function (e) { closeAllLists(e.target); }); } var autoList = [ { value: 'author:""', label: 'Author', match: 'author:"' }, { value: 'author:"^"', label: 'First Author', match: 'first author' }, { value: 'author:"^"', label: 'First Author', match: 'author:"^' }, { value: 'bibcode:""', label: 'Bibcode', desc: 'e.g. bibcode:1989ApJ...342L..71R', match: 'bibcode:"' }, { value: 'bibstem:""', label: 'Publication', desc: 'e.g. bibstem:ApJ', match: 'bibstem:"' }, { value: 'bibstem:""', label: 'Publication', desc: 'e.g. bibstem:ApJ', match: 'publication (bibstem)' }, { value: 'arXiv:', label: 'arXiv ID', match: 'arxiv:' }, { value: 'doi:', label: 'DOI', match: 'doi:' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'full:' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'fulltext' }, { value: 'full:""', label: 'Full text search', desc: 'title, abstract, and body', match: 'text' }, { value: 'year:', label: 'Year', match: 'year' }, { value: 'year:1999-2005', label: 'Year Range', desc: 'e.g. 1999-2005', match: 'year range' }, { value: 'aff:""', label: 'Affiliation', match: 'aff:' }, { value: 'abs:""', label: 'Search abstract + title + keywords', match: 'abs:' }, { value: 'database:astronomy', label: 'Limit to papers in the astronomy database', match: 'database:astronomy' }, { value: 'database:physics', label: 'Limit to papers in the physics database', match: 'database:physics' }, { value: 'title:""', label: 'Title', match: 'title:"' }, { value: 'orcid:', label: 'ORCiD identifier', match: 'orcid:' }, { value: 'object:', label: 'SIMBAD object (e.g. object:LMC)', match: 'object:' }, { value: 'property:refereed', label: 'Limit to refereed', desc: '(property:refereed)', match: 'refereed' }, { value: 'property:refereed', label: 'Limit to refereed', desc: '(property:refereed)', match: 'property:refereed' }, { value: 'property:notrefereed', label: 'Limit to non-refereed', desc: '(property:notrefereed)', match: 'property:notrefereed' }, { value: 'property:notrefereed', label: 'Limit to non-refereed', desc: '(property:notrefereed)', match: 'notrefereed' }, { value: 'property:eprint', label: 'Limit to eprints', desc: '(property:eprint)', match: 'eprint' }, { value: 'property:eprint', label: 'Limit to eprints', desc: '(property:eprint)', match: 'property:eprint' }, { value: 'property:openaccess', label: 'Limit to open access', desc: '(property:openaccess)', match: 'property:openaccess' }, { value: 'property:openaccess', label: 'Limit to open access', desc: '(property:openaccess)', match: 'openaccess' }, { value: 'doctype:software', label: 'Limit to software', desc: '(doctype:software)', match: 'software' }, { value: 'doctype:software', label: 'Limit to software', desc: '(doctype:software)', match: 'doctype:software' }, { value: 'property:inproceedings', label: 'Limit to papers in conference proceedings', desc: '(property:inproceedings)', match: 'proceedings' }, { value: 'property:inproceedings', label: 'Limit to papers in conference proceedings', desc: '(property:inproceedings)', match: 'property:inproceedings' }, { value: 'citations()', label: 'Citations', desc: 'Get papers citing your search result set', match: 'citations(' }, { value: 'references()', label: 'References', desc: 'Get papers referenced by your search result set', match: 'references(' }, { value: 'trending()', label: 'Trending', desc: 'Get papers most read by users who recently read your search result set', match: 'trending(' }, { value: 'reviews()', label: 'Review Articles', desc: 'Get most relevant papers that cite your search result set', match: 'reviews(' }, { value: 'useful()', label: 'Useful', desc: 'Get papers most frequently cited by your search result set', match: 'useful(' }, { value: 'similar()', label: 'Similar', desc: 'Get papers that have similar full text to your search result set', match: 'similar(' }, ]; // initiate the autocomplete function on the "q" element, and pass along the operators array as possible autocomplete values: inputBox = document.getElementById("q") if (inputBox) { inputBox.focus() // autofucs inputBox.setSelectionRange(inputBox.value.length, inputBox.value.length); // bring cursor to the end autocomplete(inputBox, autoList); } </script> <script> (function() { // turn off no-js if we have javascript document.documentElement.className = document.documentElement.className.replace("no-js", "js"); function getCookie(cname) { var name = cname + "="; var decodedCookie = decodeURIComponent(document.cookie); var ca = decodedCookie.split(';'); for (var i = 0; i < ca.length; i++) { var c = ca[i]; while (c.charAt(0) == ' ') { c = c.substring(1); } if (c.indexOf(name) == 0) { return c.substring(name.length, c.length); } } return ""; } (function() { // looks for the cookie, and sets true if its 'always' const coreCookie = getCookie('core') === 'always'; // only load bumblebee if we detect the core cookie and we are on abstract page if (coreCookie || (!(/^\/abs\//.test(document.location.pathname)) && !coreCookie)) { return; 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