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.

本数据集旨在模拟泰国及其它亚洲国家大学校园中虚拟驾驶环境的核心要素，包括街区、各类人行道、车道标记、交通标志、电线杆以及行人等。我们选择该校园进行模拟，因其独特的道路和铺装环境，在泰国及其他亚洲国家独具特色，与普遍引用的、主要涵盖欧洲或其他地区环境的众多数据集形成鲜明对比。该虚拟环境允许灵活地在场景中放置这些元素，并便于生成标注，而无需增加额外复杂性。此外，修改这些街区的基本属性，如纹理、颜色和形状，亦能轻松实现。本数据集精心编制，旨在辅助行人检测，尤其是在自动驾驶中的罕见和近乎事故情况。其中包含众多对象，包括行人、车辆、交通标志、建筑物以及其他重要的城市环境组成部分。