The ORNL Overhead Vehicle Dataset (OOVD)

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.

道路系统对满足国家的出行需求与经济发展诉求至关重要。美国将28%的能源用于客货运输，其中约60%被乘用车、轻型卡车及摩托车消耗。因此，提升运输效率对美国的经济发展至关重要。大都会地区的交通系统日益拥堵，且能源消耗需求持续攀升，亟需研究优化交通管控的方法。对城市交通进行协调与优化，需管控数十万车辆与交通管理系统，这需要借助高性能计算（HPC）资源完成建模与管理。本数据集旨在探究机器学习、计算机视觉与高性能计算在提升交通管控能效层面的应用潜力：通过将GRIDSMART交通摄像头作为自适应交通管控的传感设备，考量摄像头视野内车流的燃油消耗特征。GRIDSMART摄像头是一款已实地部署的商业化产品，可检测交叉口的车辆存在状态，替代感应线圈等传统传感器，向交通管控系统发出调控指令。这类摄像头具备全视野拍摄能力，能够更全面地采集交通环境信息，可用于开发更优化的交通管控算法。