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Traffic3D: A Rich 3D Traffic Environment to Train Intelligent Agents

Garg, Deepeka; Bugajski, Callum; Mount, Sarah; Vogiatzis, George; Chli, Maria


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  <identifier identifierType="DOI">10.5281/zenodo.3968432</identifier>
  <creators>
    <creator>
      <creatorName>Garg, Deepeka</creatorName>
      <givenName>Deepeka</givenName>
      <familyName>Garg</familyName>
      <affiliation>Aston University</affiliation>
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    <creator>
      <creatorName>Bugajski, Callum</creatorName>
      <givenName>Callum</givenName>
      <familyName>Bugajski</familyName>
      <affiliation>Beautiful Canoe</affiliation>
    </creator>
    <creator>
      <creatorName>Mount, Sarah</creatorName>
      <givenName>Sarah</givenName>
      <familyName>Mount</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7575-8420</nameIdentifier>
      <affiliation>Beautiful Canoe</affiliation>
    </creator>
    <creator>
      <creatorName>Vogiatzis, George</creatorName>
      <givenName>George</givenName>
      <familyName>Vogiatzis</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3226-0603</nameIdentifier>
      <affiliation>Aston University</affiliation>
    </creator>
    <creator>
      <creatorName>Chli, Maria</creatorName>
      <givenName>Maria</givenName>
      <familyName>Chli</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2840-4475</nameIdentifier>
      <affiliation>Aston University</affiliation>
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  <titles>
    <title>Traffic3D: A Rich 3D Traffic Environment to Train Intelligent Agents</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>traffic</subject>
    <subject>simulator</subject>
    <subject>unity3d</subject>
    <subject>intelligent agent</subject>
    <subject>ai</subject>
    <subject>reinforcement learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-09-25</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3968432</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3460496</relatedIdentifier>
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  <version>0.1.0</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/MPL-2.0">Mozilla Public License 2.0</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Traffic3D is a new traffic simulation paradigm, built to push forward research in human-like learning (for example, based on photo-realistic visual input). It provides a fast, cheap and scalable proxy for real-world traffic environments. This implies effective simulation of diverse and dynamic 3D-road traffic scenarios, closely mimicking real-world traffic characteristics such as faithful simulation of individual vehicle behaviour, their precise physics of movement and photo-realism. Traffic3D can facilitate research across multiple domains, including reinforcement learning, object detection and segmentation, unsupervised representation learning and visual question answering.&lt;/p&gt;

&lt;p&gt;Traffic3D is based on the&amp;nbsp;&lt;a href="https://unity3d.com/unity"&gt;Unity 3d games engine&lt;/a&gt;. The AI is written in&amp;nbsp;&lt;a href="https://www.python.org/"&gt;Python3&lt;/a&gt;&amp;nbsp;with&amp;nbsp;&lt;a href="https://pytorch.org/"&gt;PyTorch&lt;/a&gt;. It is available for Windows, Linux and OSX.&lt;/p&gt;</description>
  </descriptions>
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