1. Home
  2. TRID
  3. View Record
https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

Examining Senior Drivers Adaptation to Mixed-Level Automated Vehicles –Phase II Analysis of Naturalistic Driving Data (04-103) [supporting dataset]

Advances in the development of automated vehicle technologies (AVT) have the potential to benefit senior drivers by reducing exposure to hazards and compensating for diminished cognitive abilities sometimes seen in this population. However, the degree to which such benefits can be realized in this vulnerable population depends largely on the facility with which these drivers adapt to these features and the extent to which use of these features enhances mobility and driver performance. Phase I of this study investigated the adaptation of eighteen senior drivers to vehicles equipped with Advanced Vehicle Technologies (AVT). Specifically, this research investigated the following questions: How do senior drivers’ acceptance of AVT change after extended use? How do senior drivers’ attitudes change over a period of exposure to a vehicle equipped with AVT? How do senior drivers perceive the safety benefits of AVT? How do senior drivers adapt to and learn to use AVT? How do senior drivers’ trust and satisfaction change with accumulated usage? What do senior drivers like most and like least about AVT? Researchers measured visual processing speed and cognitive status at intake and assessed participant attitudes and the effect of exposure on driver confidence in and satisfaction with AVT via surveys administered before and after the driving experience, brief weekly check-in surveys during the driving experience, and focus group sessions conducted after the participant returned the study vehicle. These data were analyzed during the first year of this research and can be found here: https://dataverse.vtti.vt.edu/dataset.xhtml?persistentId=doi:10.15787/VTT1/VUXYYM Phase II of this effort supports analysis of video and sensor data collected during Phase I of the project (SMX), which enrolled 18 participants aged 70-79 to drive an instrumented fleet vehicle equipped with advanced driver assistance systems for a six-week period to assess the impact of AVTs on senior mobility and driver performance. Analysis of data collected as part of this effort is augmented by comparison with data collected from older SHRP 2 participants and from participants in the VCC 50 project. Analyses included a comparison of mobility and kinematic data with similar data collected as part of the VCC50 Study and the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), with a focus on SHRP 2 data collected from older drivers at the research site located in State College, PA (SHRP2-PENN). The research team developed and employed an object detection algorithm to identify trips where adaptive cruise control (ACC) was activated by the participant. Kinematic data from these trips were compared with data from SHRP 2 trips and with data from Phase I trips where ACC was not activated.

  • Dataset URL:
  • Record URL:
  • Supplemental Notes:
    • The dataset supports report: Examining Seniors’ Adaptation to Mixed Function Automated Vehicles: Analysis of Naturalistic Driving Data, 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

    ,    

    Virginia Tech Transportation Institute

    ,    

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
  • Publication Date: 2020-6-18

Language

  • English

Media Info

  • Media Type: Dataset
  • Dataset: Version: 1.0 Integrity Hash:
  • Dataset publisher:

    Dataverse

    ,    

Subject/Index Terms

Filing Info

  • Accession Number: 01780585
  • Record Type: Publication
  • Contract Numbers: 69A3551747115
  • Files: UTC, TRIS, ATRI, USDOT
  • Created Date: Aug 27 2021 2:58PM