DACT: Dataset of Annotated Car Trajectories

DACT contains two subsets of annotated car trajectories data. The dataset contains 50 trajectories which cover about 13 hours of driving data. In DACT, we manually specified significant driving patterns by using an interactive framework. A significant driving pattern can be anything like a turn, speed-up, slow-down, etc. The annotation process consists of a crowd-sourcing task followed by comprehensive aggregation phases. The aggregation is done by two different strategies: Strict and Easy. For the first one, we used some strict constraints to aggregate crowd-sourcing results, while we used flexible constraints to generate the second subset of DACT. More information about this dataset may be find here: https://arxiv.org/abs/1705.05219 .Please cite this paper "Trajectory Annotation by Discovering Driving Patterns (UrbanGIS'17)", available at https://dl.acm.org/citation.cfm?doid=3152178.3152184, if you want to use this dataset.DACT包含两个带标注的车辆轨迹数据子集。本数据集共包含50条轨迹，覆盖约13小时的驾驶数据。在DACT数据集中，我们通过交互式框架手动定义了典型驾驶行为模式，此类模式可涵盖转弯、加速、减速等各类驾驶场景。该数据集的标注流程为先开展众包标注任务，随后进入多阶段综合聚合环节。聚合环节采用两种不同策略：严格（Strict）模式与宽松（Easy）模式。其中严格模式通过设定严格约束条件对众包标注结果进行聚合，而宽松模式则借助灵活约束条件生成DACT的第二子集。关于该数据集的更多详情可参阅：https://arxiv.org/abs/1705.05219。若您使用本数据集，请引用以下论文："Trajectory Annotation by Discovering Driving Patterns (UrbanGIS'17)"，获取链接：https://dl.acm.org/citation.cfm?doid=3152178.3152184。