Automated Vehicle Crash Rate Comparison Using Naturalistic Data

The fundamental objectives of the research described in this report are to improve the quality of the available data involving self-driving cars and to analyze existing data to better understand the relative crash rate of self-driving cars. Five research questions guided this analysis: (1) How many crashes go unreported to police or insurance? (2) Do unreported crash rates vary by location? (3) How is the comparison between crash rates for the Self-Driving Car and national crash rates affected by the percentage of unreported crashes and severity level? (4) How do crash rates vary based on street type and speed limit? (5) What are the factors contributing to unreported crashes?

• Record URL:
  http://www.vtti.vt.edu/PDFs/Automated%20Vehicle%20Crash%20Rate%20Comparison%20Using%20Naturalistic%20Data_Final%20Report_20160107.pdf
• 
• Corporate Authors:

  Virginia Tech Transportation Institute

  Blacksburg, VA  United States 

  Google Incorporated

  1600 Amphitheatre Parkway
  Mountain View, CA  United States  94043
• Authors:
  • Blanco, Myra
  • Atwood, Jon
  • Russell, Sheldon
  • Trimble, Tammy
  • McClafferty, Julie
  • Perez, Miguel
• Publication Date: 2016-1

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Appendices; Figures; References; Tables;
• Pagination: 88p

Subject/Index Terms

• TRT Terms: Alternatives analysis; Automatic data collection systems; Crash rates; Crash reports; Crash severity; Data quality; Highway safety; Intelligent vehicles; Speed limits
• Geographic Terms: United States
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01593724
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 16 2016 9:35AM