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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
        <publisher-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2020.13.ERVR-380</article-id>
      <article-id pub-id-type="sici">2470-1173(20200126)2020:13L.3801;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2020n13_input/s11.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2020/00002020/00000013/art00010</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Development and evaluation of immersive educational system to improve driver’s risk prediction ability in traffic accident situation</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Suto</surname>
            <given-names>Hiroto</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Zhang</surname>
            <given-names>Xingguo</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Shen</surname>
            <given-names>Xun</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Raksincharoensak</surname>
            <given-names>Pongsathorn</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Tsumura</surname>
            <given-names>Norimichi</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>26</day>
        <month>01</month>
        <year>2020</year>
      </pub-date>
      <volume>2020</volume>
      <issue>13</issue>
      <fpage>380-1</fpage>
      <lpage>380-6</lpage>
      <permissions>
        <copyright-year>2020</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>Improving drivers’ risk prediction ability can reduce the accident risk significantly. The existing accident awareness training systems show poor performance due to the lack of immersive sense. In this research, an immersive educational system is proposed for risk prediction
 training based on VR technology. The system provides a highly realistic driving experience to driver through 360 degrees video using VR goggle. In the nearly actual driving scene, users are expected to point out every potential dangerous scenario in different cases. Afterwards, the system
 evaluates users’ performances and gives the corresponding explanations to help users improve safety awareness. The results show that the system is more effective than previous systems on improving drivers’ risk prediction capability.</italic>
        </p>
      </abstract>
      <kwd-group>
        <kwd>Virtual Reality</kwd>
        <kwd>Interaction</kwd>
        <kwd>Traffic Accident</kwd>
        <kwd>Risk Prediction</kwd>
        <kwd>360 degrees video</kwd>
        <kwd>VR goggle</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
