Characterizing Level 2 Automation in a Naturalistic Driving Fleet

In the ongoing NOVA fleet data collection effort, vehicles with Society of Automotive Engineers (SAE) Level 2 (L2) features will be instrumented with sophisticated data acquisition systems that are able to collect multiple video views, sensor data, and vehicle network data. These data acquisition systems are the most sophisticated systems available at VTTI and provide the capability to easily add or remove various sensors from the fleet. The instrumented vehicles will mostly be drivers’ personal vehicles. The effort to collect these data will be funded from other sources and will be used as cost share for this project. That is, without the collection of these data, this analysis project would not be possible. The data collection is expected to occur in the Northern Virginia area. For this Safe-D project, dash video from the NOVA fleet collection effort will be analyzed using machine vision to, combined with additional approaches that offer some redundancy, determine the frequency, timing, and characteristics of L2 feature activations and deactivations. These data will be used to address research questions associated with L2 automation feature usage. These questions relate to what features are activated, when they are activated, where they are activated, when they are deactivated, and what leads to system takeover requests. Takeover requests in particular will be used to examine the effectiveness of L2 systems in handling diverse roadway features and environmental conditions. The combination of these efforts will result in the creation of a framework to characterize the real-world operational domain of L2 features using naturalistic driving data. A de-identified dataset that includes summary data for the activations and deactivations used in the proposed analyses will also be generated as a product of this research effort. The project will provide funding for two master’s student for two years; the students will use the project as the topic of their graduate theses.

Language

  • English

Project

  • Status: Active
  • Funding: $869677
  • Contract Numbers:

    69A3551747115

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Harwood, Leslie

  • Performing Organizations:

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    Perez, Miguel

  • Start Date: 20190810
  • Expected Completion Date: 20210809
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01713866
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center
  • Contract Numbers: 69A3551747115
  • Files: UTC, RiP
  • Created Date: Aug 16 2019 3:01PM