The ORNL Overhead Vehicle Dataset (OOVD)
收藏资源简介：
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.
道路交通网络对于保障国家出行需求与推动经济发展至关重要。美国将全国28%的能源用于人员与货物的运输作业，其中约60%的能耗来自轿车、轻型卡车及摩托车的运行。因此，提升运输效率对于美国的经济发展至关重要。大都市地区交通系统日益严峻的拥堵问题与不断攀升的能源消耗需求，亟需学界研发用于优化交通管控的相关方法。城市区域的交通协调与优化涉及数十万计的运行车辆与交通管理系统，其建模与管控工作需依托高性能计算（High Performance Computing, HPC）资源予以支撑。本数据集的构建旨在探究机器学习、计算机视觉及高性能计算技术在提升交通管控能效层面的应用潜力：以GRIDSMART交通摄像头作为自适应交通管控的传感节点，同时针对摄像头视野内车流的燃油消耗特性进行敏感度适配。GRIDSMART摄像头作为一款已实现实地部署的商用产品，可检测交叉口的车辆存在状态，替代感应线圈等传统交通传感器，向交通管控系统发出调控指令。该类摄像头具备全景视野，可获取更全面的交通环境信息，进而支撑更优化的交通管控算法研发。
