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    <responseDate>2026-10-14T17:15:47Z</responseDate>
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    <identifier>10.57760/sciencedb.02891</identifier>
    <datestamp>2022-11-17T09:26:16Z</datestamp>
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  <dc:date>2022-11-17</dc:date>
  <dc:title>Detections for tracking and lane-level map generation with roadside LiDAR</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.02891</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The dataset includes the detection reulsts for tracking with roadside LiDAR. You can run &amp;quot;python ShowResults.py&amp;quot; to show the demo.The results currently include 4 locations:angangxilu (intersection), xincun (intersection), yananstreet(road segment), and yatai (road segment). We will continue to supplement more data in future. In addition, &amp;quot;FilteredPointCloudsForVeloviewFormat&amp;quot; folder contains filtered point clouds containing only vehicle and roaduser points. You can cluster them for yourself approches.Furthermore, &amp;quot;maps&amp;quot; directory includes the lane marking information of each site, &amp;quot;CreateMapImage.py&amp;quot; is the demo to show them.&amp;quot;DuplicateObjectIndex&amp;quot; directory includes information about  duplicate objets of detection. You can fusion duplicate objects to solve the error that the detection algorithm detects an object into multiple objects because of occlusion.</dc:description>
  <dc:subject>roadside LiDAR; lane-level map; low-channel; multi-object tracking</dc:subject>
  <dc:creator>Hui Liu</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1109/jas.2023.123183</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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