Working With Autonomous Trucks To Improve Routine Maintenance Operations [Research Brief]

Details

• Creators:
  Chin, Kristie ; Gold, Andrea ; McAuley, Anna ; Werner, Mark ; Zhang, Zhanmin
• Corporate Creators:
  University of Texas at Austin. Center for Transportation Research
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Autonomous vehicles
  • Maintenance
  • Data collection
  • Artificial intelligence
  • Strategic planning
  • Trucking
  • Automation
• Publication/ Report Number:
  0-7129
• Resource Type:
  Brief
• Geographical Coverage:
  Texas ; United States
• Edition:
  Research Brief
• Corporate Publisher:
  University of Texas at Austin. Center for Transportation Research
• Abstract:
  The aim of Project 0-7129 was to demonstrate that AV trucking data can be effectively ingested and reported to TxDOT maintenance personnel, improving operational efficiency. Texas leads the nation in AV testing and deployment, with significant stakeholder support for the IRMF’s potential to reduce reporting latency and improve geographical and severity insights. Numerous ongoing initiatives in Texas, including the Nighttime Inspection Application and the CAV Task Force, present opportunities for the IRMF to successfully scale alongside these projects, leveraging the foundational work already completed.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/77787/dot_77787_DS1.pdf
• File Type:
  [PDF - 270.18 KB]
• Collection(s):
  Autonomous Vehicles ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:e1c4d4ab3ccca8c57a4076790e209cc227fe0288e22ebe5f8efc06dd878d1aef16a74d5eb04ba097791f0347577edbc19a0820e4027d5c9e30d03e157ccdfbd9