Metaverse_Pedestrian_SyntheticDataset

The dataset is generated specifically to simulate the essential components for driving environments in a virtual campus of Chulalongkorn University, including street blocks, various pavements, lane markings, traffic signs, lamp poles, and pedestrians, among other features. We selected this campus for our simulation due to its distinctive road and pavement environments, which are unique to Thailand and other Asian countries. This choice contrasts with many widely cited datasets that predominantly feature environments from European or other regions. The virtual environment enables the flexible placement of these elements within the scene and facilitates the generation of annotations without additional complexity. Furthermore, modifying the fundamental properties of these blocks, such as textures, colors, and shapes, can be easily achieved. Our dataset is meticulously generated for the purpose of aiding pedestrian detection, especially for rare and near-accident cases in autonomous driving. It contains numerous objects, including pedestrians, vehicles, traffic signs, buildings, and other important urban environment components.

本数据集专为模拟朱拉隆功大学(Chulalongkorn University)虚拟校园的驾驶环境核心要素而构建，涵盖街道街区、各类人行道、车道标线、交通标志、路灯杆及行人等多种场景要素。我们选择该校园作为仿真场景，缘于其独具特色的道路与人行道环境——这类环境为泰国乃至亚洲地区所独有。这一选择与诸多被广泛引用的数据集形成鲜明对比，此类数据集大多以欧洲或其他地区的环境场景为原型。该虚拟环境支持在场景内灵活排布各类要素，且可在无需额外复杂操作的前提下生成标注信息。此外，还可便捷地调整街区的基础属性，例如纹理、色彩与形状等。本数据集经精心构建，旨在助力自动驾驶领域的行人检测任务，尤其是针对罕见的近事故场景案例。数据集涵盖海量目标对象，包括行人、车辆、交通标志、建筑物及其他关键城市环境要素。