The ORNL Naturalistic Driving Study Sample (ONDSS) is a dataset created by Oak Ridge National Laboratory to help evaluate algorithms used in an analysis of automobile driver behavior. The sample was developed as part of the U.S. Department of Transportation’s Federal Highway Administration research efforts seeking to understand the role of driver performance and behavior on traffic safety.
A next-generation, digital, communicative “smart” grid will require new operational and planning capabilities and substantial infrastructure investment over several decades to meet the country’s energy goals. To address these challenges, the Department of Energy has established the Grid Modernization Initiative.
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.
The ORNL Database to Enable Face characterization in Driving Studies (DEFADS) is a dataset created by Oak Ridge National Laboratory to help evaluate algorithms used in an analysis of automobile driver behavior.
Researchers use airborne sensors and machine learning to measure risk to different components of the electric grid and monitor their functioning. The Grid Communications and Security Group develops and tests sensor packages for deployment on drones, high-altitude balloons, and high-altitude, long endurance aircraft.
The Grid Communications and Security Group combines capabilities in sensing and algorithm development to increase the efficiency and range of electric vehicles and the accuracy of automated driving support features.