September 14, 2021 Journal article Open Access

Piperigkos, Nikos; Lalos S., Aris; Berberidis, Kostas

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  <identifier identifierType="DOI">10.5281/zenodo.6381511</identifier>
  <creators>
    <creator>
      <creatorName>Piperigkos, Nikos</creatorName>
      <givenName>Nikos</givenName>
      <familyName>Piperigkos</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0262-7619</nameIdentifier>
      <affiliation>University of Patras, Athena Research Center, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Lalos S., Aris</creatorName>
      <givenName>Aris</givenName>
      <familyName>Lalos S.</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0511-9302</nameIdentifier>
      <affiliation>Athena Research Center, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Berberidis, Kostas</creatorName>
      <givenName>Kostas</givenName>
      <familyName>Berberidis</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2175-9043</nameIdentifier>
      <affiliation>University of Patras, Athena Research Center, Greece</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Graph Laplacian Diffusion Localization of Connected and Automated Vehicles</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-09-14</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6381511</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6381510</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies coupled with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-toVehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The proposed distributed and diffusion localization schemes significantly reduced the GPS error and do not only converged to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various methods, which in turn outperform other state of the art approaches. The impact of the network connections and the network latency are also investigated.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/871738/">871738</awardNumber>
      <awardTitle>Cross-layer cognitive optimization tools &amp; methods for the lifecycle support of dependable CPSoS</awardTitle>
    </fundingReference>
  </fundingReferences>
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