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    <identifier>10.57760/sciencedb.29388</identifier>
    <datestamp>2025-10-10T17:25:27Z</datestamp>
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  <dc:date>2025-10-10</dc:date>
  <dc:title>Quantum-Topological Meta-Learning for Tire-Road Contact Stability and Multi-Modal Road Prediction in Autonomous Driving</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.29388</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset is derived from the project of &amp;quot; Research on Quantum Topological Element Learning in Tire-Road Contact Stability and Multimodal Road Surface Prediction for L4 Autonomous Driving. &amp;quot; It is collected on three platforms : hardware-in-the-loop bench, real vehicle road test and extreme working condition environment cabin. The data cover 10 typical pavements such as dry asphalt, ice surface, snow, steel plate and mud. The temperature range is &amp;minus; 40 &amp;deg; C ~ 85 &amp;deg; C, the vehicle speed is 0 ~ 120 km / h, the vertical load is 29.6 ~ 43.7 kN, and the cumulative 30,000 km enhanced durability and 500 h hygrothermal aging test.</dc:description>
  <dc:subject>Quantum topological field theory; Tire contact stability zone; Multi modal pavement prediction; Meta learning adapter; Hardware in the loop verification</dc:subject>
  <dc:creator>Na Wang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1371/journal.pone.0335922</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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