福特多AV季节数据集
Ford Multi-AV Seasonal Dataset
摘要：
本文介绍了一个具有挑战性的多智能体季节性数据集，该数据集由福特自动驾驶车队在2017-18年的不同日期和时间收集。这些车辆在密歇根州平均行驶66公里，其中包括底特律机场、高速公路、市中心、大学校园和郊区等多种驾驶场景。用于此数据汇集的每辆车都是福特Fusion，配备了Applanix POS-LV GNSS系统、四台HDL-32 E Velodyne 3D激光雷达扫描仪、6台Point Grey 130万像素摄像机安装在车顶，实现360度全方位覆盖，1台Pointgrey 500万像素摄像机安装在挡风玻璃后面，实现前向视野。我们呈现了在动态城市环境中经历的天气，照明，建筑和交通条件的季节性变化。该数据集可以帮助为自动驾驶汽车和多智能体系统设计强大的算法。数据集中的每个日志都带有时间戳，包含来自所有传感器的原始数据、校准值、姿态轨迹、地面实况姿态和3D地图。所有数据都以Rosbag格式提供，可以使用开源机器人操作系统（ROS）进行可视化，修改和应用。我们还提供最先进的基于反射率的本地化输出，用于基准测试。数据集可以在我们的网站上免费下载。
英文摘要：
This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles traversed an average route of 66 km in Michigan that included a mix of driving scenarios such as the Detroit Airport, freeways, city-centers, university campus and suburban neighbourhoods, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV GNSS system, four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the rooftop for 360-degree coverage and 1 Pointgrey 5 MP camera mounted behind the windshield for the forward field of view. We present the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environments. This dataset can help design robust algorithms for autonomous vehicles and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. All data is available in Rosbag format that can be visualized, modified and applied using the open-source Robot Operating System (ROS). We also provide the output of state-of-the-art reflectivity-based localization for bench-marking purposes. The dataset can be freely downloaded at our website.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
