skip to main content
DOE Data Explorer title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: The ORNL Overhead Vehicle Dataset (OOVD)

Abstract

Roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that utilized by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress. The increasing congestion and energy resource requirements of transportation systems for metropolitan areas require research in methods to improve and optimize control methods. Coordinating and optimizing traffic in urban areas may introduce hundreds of thousands of vehicles and traffic management systems, which can require high performance computing (HPC) resources to model and manage. This data set was created to understand the potential for machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can bemore » used to generate better control algorithms.« less

Creator(s)/Author(s):
; ; ; ;
Publication Date:
DOE Contract Number:  
AC05-00OR22725
Product Type:
Dataset
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1525087
DOI:
10.11578/1525087

Citation Formats

Karnowski, Thomas P., Tokola, Ryan A., Kerekes, Ryan A., Henderson, Russ D., and Sewell, Johnathan M. The ORNL Overhead Vehicle Dataset (OOVD). United States: N. p., 2019. Web. doi:10.11578/1525087.
Karnowski, Thomas P., Tokola, Ryan A., Kerekes, Ryan A., Henderson, Russ D., & Sewell, Johnathan M. The ORNL Overhead Vehicle Dataset (OOVD). United States. doi:10.11578/1525087.
Karnowski, Thomas P., Tokola, Ryan A., Kerekes, Ryan A., Henderson, Russ D., and Sewell, Johnathan M. 2019. "The ORNL Overhead Vehicle Dataset (OOVD)". United States. doi:10.11578/1525087. https://www.osti.gov/servlets/purl/1525087. Pub date:Sat Jun 01 00:00:00 EDT 2019
@article{osti_1525087,
title = {The ORNL Overhead Vehicle Dataset (OOVD)},
author = {Karnowski, Thomas P. and Tokola, Ryan A. and Kerekes, Ryan A. and Henderson, Russ D. and Sewell, Johnathan M.},
abstractNote = {Roadways are critical to meeting the mobility and economic needs of the nation. The United States uses 28% of its energy in moving goods and people, with approximately 60% of that utilized by cars, light trucks, and motorcycles. Thus, improved transportation efficiency is vital to America’s economic progress. The increasing congestion and energy resource requirements of transportation systems for metropolitan areas require research in methods to improve and optimize control methods. Coordinating and optimizing traffic in urban areas may introduce hundreds of thousands of vehicles and traffic management systems, which can require high performance computing (HPC) resources to model and manage. This data set was created to understand the potential for machine learning, computer vision, and HPC to improve the energy efficiency aspects of traffic control by leveraging GRIDSMART traffic cameras as sensors for adaptive traffic control, with a sensitivity to the fuel consumption characteristics of the traffic in the camera’s visual field. GRIDSMART cameras—an existing, fielded commercial product—sense the presence of vehicles at intersections and replace more conventional sensors (such as inductive loops) to issue calls to traffic control. These cameras, which have horizon-to-horizon view, offer the potential for an improved view of the traffic environment which can be used to generate better control algorithms.},
doi = {10.11578/1525087},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Dataset:

Save / Share: