Digitizing Traffic Control Infrastructure for Autonomous Vehicles (AV) [Summary]

Details

• Creators:
  Steele, Joanne ; Wu, Jason ; Duong, Ash ; Wyatt, Bethany ; Tapp, Charles ; Tambunga, Jacob ; Kuhr, James ; Ma, Jianming ; McCarty, Matthew
• Corporate Creators:
  Texas A&M Transportation Institute ; TTI
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Alternatives analysis
  • Autonomous vehicles
  • Blockchains
  • Connected vehicles
  • Data collection
  • Digitization
  • Highway traffic control systems
  • Signalized intersections
  • Traffic signs
• Resource Type:
  Brief
• Geographical Coverage:
  Texas ; United States
• Edition:
  Project Summary
• Contracting Officer:
  Steele, Joanne
• Corporate Publisher:
  Texas Department of Transportation. Research and Technology Implementation Office
• Abstract:
  High precision road maps are a crucial component to facilitating autonomous driving techniques. Autonomous vehicles (AVs) are experiencing exponential growth. According to the latest forecast from IHS Markit, over 33 million AVs will be on the road globally by 2040, posing a higher requirement to ensure AVs’ driving safety. Although current AVs rely on vehicular sensing techniques (e.g., camera, LiDAR, radar), studies have suggested that creating high-quality road maps with precisely digitized traffic control infrastructures (TCIs) (e.g., traffic signs, signals, intersections) is necessary to enhance the safe driving operations of AVs.
• Format:
  PDF
• Funding:
  0-7128
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/72738/dot_72738_DS1.pdf
• File Type:
  [PDF - 490.95 KB]
• Collection(s):
  Autonomous Vehicles ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:deacc8139ca5a7d1c242aae37f1e75f5b673c3d90a67a11e6857790aef01c88109d1c1760de8ab072f07058aba02ca2313e8c943933daa0c51f2813e4da64284