主数据集为一个350 MB的轨迹数据文件（TGSIM-Foggy Bottom-Data.csv），包含行人、自行车、电动滑板车、非自动驾驶乘用车、自动驾驶车辆（AV）、摩托车、公交车及卡车在城市环境中的位置、速度和加速度数据。配套文件包括一张航拍参考图像（Reference_Image_Foggy Bottom.png）、一份多边形边界列表（Foggy_Bottom_boundaries.txt）以及与之对应的图像文件（i1.png、i2.png、…、i49.png，存于名为“Annotation on Regions.zip”的压缩包中），用于将物理道路区段映射至数值ID（该ID在轨迹数据集中被引用）。本数据集是‘第三代仿真数据（TGSIM）：深入探究自动驾驶系统（ADS）对人类行为影响’项目的一部分。该项目共采集并处理了六个轨迹数据集，旨在表征高速公路与城市环境中多样化场景下人与自动驾驶车辆的交互行为。更多信息请参见项目报告：https://rosap.ntl.bts.gov/view/dot/74647。本数据集为TGSIM项目所采集的六个数据集之一，数据来源于安装在美国华盛顿特区雾谷（Foggy Bottom）社区的十二台4K固定式基础设施摄像头。这些摄像头覆盖四个交叉口、相邻的人行横道、交叉口之间的路段，以及从交叉口向外延伸的部分路段，总覆盖范围超过一个完整街区。上述路段以多边形形式表示，用以界定行车道、停车道、人行横道及交叉口，服务于目标检测与分析任务（详见Reference_Image_Foggy Bottom.png）。摄像头在工作日通勤时段持续拍摄视频，起始时间为下午3:

The main dataset is a 350 MB file of trajectory data (TGSIM-Foggy Bottom-Data.csv) that contains position, speed, and acceleration data for pedestrians, bicycles, scooters, non-automated passenger cars, automated vehicles, motorcycles, buses, and trucks in an urban environment. Supporting files include an aerial reference image (Reference_Image_Foggy Bottom.png) and a list of polygon boundaries (Foggy_Bottom_boundaries.txt) and associated images (i1.png, i2.png, …, i49.png stored in the folder titled “Annotation on Regions.zip”) to map physical roadway segments to numerical IDs (as referenced in the trajectory dataset). This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset. As part of this dataset, the following files were provided: <ul><li>TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion.</li> <li>Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments.</li> <li>Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image.</li> <li>Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.</li></ul>