The Virginia Connected Corridor 50 Elite Vehicle Naturalistic Driving Study (VCC50 Elite NDS) (00-029) [supporting dataset]

Project Description: Automated driving systems (ADS) have the potential to fundamentally transform transportation by reducing crashes, congestion, and cost while improving traffic efficiency and access to mobility for the transportation-challenged population. However, people may not use ADS as intended due to their misunderstanding of such systems’ capabilities and limitations. Recent news articles, reporting Tesla drivers napping behind the wheel, suggest the need for a better understanding of how people are using ADS as well as what benefits and consequence that such systems have on transportation safety. Therefore, this work aims to investigate the (1) real-world use of automated longitudinal and lateral control features (e.g., adaptive cruise control and lane keeping assistance), (2) capabilities and limitations of such systems found in real-world operation, as well as (3) safety benefits and unintended consequences of these technologies. For this purpose, this study will leverage data collected from 50 participants who drove personally owned vehicles equipped with ADS for 12 months. The work is expected to contribute to a greater understanding of the prevalence and safety consequences of ADS use on public roadways, as well as drivers’ perception of the early production ADS. The findings from this project may further inform the development of human-machine interfaces, training programs, and owners’ manuals to reduce unintended use of ADS and negative consequences. The identified characteristics of the situations when the driving automation requested human drivers’ intervention or failed without alerts will further inform the development of testing scenarios to ensure ADS safety. Data Scope: To evaluate the real-world driver interaction with commercially available longitudinal and lateral control features, the research team investigated an existing naturalistic driving database collected from the Virginia Connected Corridor 50 Elite Vehicle Naturalistic Driving Study (VCC50 Elite NDS). The VCC50 Elite dataset contains data from 50 drivers of vehicles with longitudinal control systems (ACC) at a minimum, although most also had lateral control systems. The participants were adult drivers aged 24 to 76 who were recruited and primarily commuted in the Washington, DC metro area which includes northern Virginia and sections of Maryland for 12 months (estimated 684,931 miles driven across all participants). The personal vehicles of the 50 participants were instrumented with Virginia Tech Transportation Institute (VTTI)-designed data acquisition systems. The available NDS data support the simultaneous investigation of the driver, vehicle, and environmental factors pertaining to driving automation system operation across a wide range of drivers in various situations. This dataset captured 223 safety critical events (SCEs including crashes or near-crashes) which have been manually coded by the VTTI’s data reduction team.

• Dataset URL:
  http://doi.org/10.15787/VTT1/98NBN7
• Record URL:
  https://safed.vtti.vt.edu/wp-content/uploads/2021/01/Safe-D-Final-Research-Report_VTTI-00-029_Final-ready-for-Site.pdf
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/58336
• Dataset URL:
  https://rosap.ntl.bts.gov/view/dot/58883
• Supplemental Notes:
  • The dataset supports report: Real-World Use of Automated Driving Systems and their Safety Consequences: A Naturalistic Driving Data Analysis, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Safety through Disruption University Transportation Center (Safe-D)

  Virginia Tech Transportation Institute
  Blacksburg, VA  United States  24060

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Kim, Hyungil
  • 0000-0001-9014-7484
  • Song, Miao
  • 0000-0002-3323-9822
  • Greatbatch, Rick
  • Novotny, Adam
  • Doerzaph, Zac
  • 0000-0002-3897-1430
• Publication Date: 2020-12-8

Language

• English

Media Info

• Media Type: Dataset
• Dataset: Version: 2.0 Integrity Hash:
• Dataset publisher:

  Virginia Tech Transportation Institute Dataverse

  ,    

Subject/Index Terms

• TRT Terms: Automatic data collection systems; Autonomous intelligent cruise control; Commuters; Data; Driver support systems; Drivers; Highway safety
• Geographic Terms: Washington (District of Columbia)
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01775943
• Record Type: Publication
• Contract Numbers: 69A3551747115/VTTI-00-029
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Jul 6 2021 12:26PM