ViF-GTAD: A new Automotive Data Set with Ground Truth for ADAS/AD Development, Testing and Validation

A new dataset for automated driving, which is the subject matter of this paper, identifies and addresses a gap in existing similar perception data sets. While the most state-of-the-art perception data sets primarily focus on provision of various on-board sensor measurements along with the semantic information under various driving conditions, the provided information is often insufficient since the object list and position data provided include unknown and time-varying errors. The current paper and the associated data-set describes the first publicly available perception measurement data that include not only the on-board sensor information from camera, Lidar and radar with semantically classified objects, but also the high precision ground-truth position measurements enabled by the accurate RTK assisted GPS localization systems available on both the ego vehicle and the dynamic target objects. This paper provides insight on the capturing of the data, explicitly explaining the meta data structure and the content, as well as the potential application examples where it has been, and can potentially be, applied and implemented in relation to automated driving and environmental perception systems development, testing and validation.本论文所研究的全新自动驾驶数据集，精准识别并填补了现有同类感知数据集的研究空白。尽管当前主流的先进感知数据集主要聚焦于在各类驾驶工况下提供多样化的车载传感器测量数据与语义标注信息，但此类数据集往往存在信息局限性：其提供的目标列表与位置数据包含未知且随时间动态变化的误差。本论文及配套数据集介绍了全球首个公开可用的感知测量数据集，其不仅涵盖来自摄像头、激光雷达（Lidar）、雷达的车载传感器信息与语义分类目标，还包含由自车与动态目标对象均搭载的高精度RTK辅助GPS定位系统所生成的高精度位置真值（ground-truth）测量数据。本文详细阐述了该数据集的采集流程，明确说明了其元数据结构与内容构成，并结合自动驾驶与环境感知系统的开发、测试与验证场景，介绍了该数据集已落地应用及可潜在推广应用的典型案例。