论文标题
A9-DATASET：基于多传感器基础架构的移动性研究数据集
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
论文作者
论文摘要
基于数据密集型机器学习的技术越来越多地在未来的移动解决方案的开发中发挥着重要作用 - 从车辆的驾驶帮助和自动化功能到通过专用基础架构实现的实时交通管理系统。高质量现实世界数据的可用性通常是大规模开发和可靠部署此类系统的重要先决条件。为了这项努力，我们根据德国慕尼黑附近3公里长的Providentia ++测试场的路边传感器基础设施介绍了A9-DATASET。该数据集包括高分辨率的匿名和精确测试的多模式传感器和对象数据，涵盖了各种流量情况。作为第一组数据的一部分，我们在本文中描述了，我们从A9 Autobahn上的两个高架龙门桥上提供了相机和激光镜头，并带有标有3D边界盒的相应对象。第一组总共包括1000多个传感器框架和14000个流量对象。该数据集可在https://a9-dataset.com上下载。
Data-intensive machine learning based techniques increasingly play a prominent role in the development of future mobility solutions - from driver assistance and automation functions in vehicles, to real-time traffic management systems realized through dedicated infrastructure. The availability of high quality real-world data is often an important prerequisite for the development and reliable deployment of such systems in large scale. Towards this endeavour, we present the A9-Dataset based on roadside sensor infrastructure from the 3 km long Providentia++ test field near Munich in Germany. The dataset includes anonymized and precision-timestamped multi-modal sensor and object data in high resolution, covering a variety of traffic situations. As part of the first set of data, which we describe in this paper, we provide camera and LiDAR frames from two overhead gantry bridges on the A9 autobahn with the corresponding objects labeled with 3D bounding boxes. The first set includes in total more than 1000 sensor frames and 14000 traffic objects. The dataset is available for download at https://a9-dataset.com.
