Connected Autonomous Vehicles: Safety During Merging and Lane Change and Impact on Traffic Flow [Research Brief]

Details

• Creators:
  Iannou, Petros ; Monteiro, Fernando V
• Corporate Creators:
  METRANS Transportation Center (Calif.) ; Pacific Southwest Region 9 UTC, University of Southern California
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous vehicles
  • Connected vehicles
  • Lane changing
  • Merging control
  • Risk assessment
  • Safety analysis
  • Simulation
  • Traffic flow
  • Traffic safety
  • Vehicle to vehicle communications
• Resource Type:
  Brief
• Geographical Coverage:
  United States
• Edition:
  Research Brief
• Corporate Publisher:
  METRANS Transportation Center (Calif.)
• Abstract:
  The development of autonomous vehicles that behave as independent robots relying solely on their on-board sensors to operate in highly uncertain environments which are to be learned using massive data and machine learning and artificial intelligence techniques is an approach that dominated the industry of autonomous vehicles. Current autonomous vehicles will not be able to navigate in dense traffic unless they violate safety rules like human drivers do, by cutting in front of other vehicles putting themselves in dangerous for collision spots for short period of time something no autonomous vehicle will be allowed to do by design.
• Format:
  PDF
• Funding:
  69A3551747109 Caltrans Grant 65A0674,
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/67955/dot_67955_DS1.pdf
• File Type:
  [PDF - 177.83 KB]
• Collection(s):
  Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:af172bea093d330231ec2fb468ab5c640824d9b44d9d5990749ca9201efe4b62da13efb0bfe0daca864c86c552ef597a33cde00dfce0204ee6b95762b055e6b7