Cooperative Vehicle-Highway Automation (CVHA) Technology : Simulation of Benefits and Operational Issues

The past few years have witnessed a rapidly growing market in assistive driving technologies, designed to improve safety and operations by supporting driver performance. Often referred to as cooperative vehicle–highway automation (CVHA) systems, these assistive technologies commonly utilize radar, light detection and ranging (LiDAR), or other machine-vision technologies, as well as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technology, to obtain surrounding roadway and traffic data. Extensive research has been conducted on CVHA technology since the late 1990s. Findings have been generally positive, including potential safety benefits, high potential acceptance rates, and reductions in driver workload, though operations and capacity impacts have been mixed, depending on the technology. Numerous opportunities for further advancement in traffic control strategies that leverage V2V and V2I have been identified and are under development. However, from the current literature, it is not clear: (1) how some of these systems will operate on the existing infrastructure (e.g., autonomous vehicles), (2) how they will impact traffic congestion and safety, and (3) how state departments of transportation (DOTs) should incorporate this changing vehicle and driver environment in their planning, design, safety, and construction processes. The objective of the current study was to begin to address these concerns to ensure that state DOTs and other practitioners will have the information necessary to make effective policies, procedures, and management decisions regarding CVHA technology. In seeking to address these concerns, a key finding from this study is related to the underlying modeling approaches utilized to study many of these potential technologies. It viii is clear that current simulation models are not capable of readily modeling cooperative assist technologies or autonomous vehicles. A critical component in the determination of the impact of many of these technologies is the human interaction with the technology, both those individuals inside the equipped vehicle and those driving other vehicles that interact with the equipped vehicle.

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  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Georgia Institute of Technology

    Civil and Environmental Engineering
    790 Atlantic Drive
    Atlanta, GA  United States  30332-0355

    Georgia Department of Transportation

    15 Kennedy Drive
    Forest Park, GA  United States  30297

    Federal Highway Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590

    National Center for Transportation Systems Productivity and Management (NCTSPM)

    Lamar Allen Sustainable Education Building
    788 Atlantic Drive
    Atlanta, GA  United States  30332-0355

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Hunter, Michael
    • Guin, Angshuman
    • Rodgers, Michael O
    • Huang, Ziwei
    • Greenwood, Aaron Todd
  • Publication Date: 2017-3

Language

  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Photos; References; Tables;
  • Pagination: 94p

Subject/Index Terms

Filing Info

  • Accession Number: 01644678
  • Record Type: Publication
  • Report/Paper Numbers: RP 14-36
  • Contract Numbers: DTRT12GUTC12
  • Files: UTC, NTL, TRIS, ATRI, USDOT, STATEDOT
  • Created Date: Jul 20 2017 10:28AM