Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.

翻译：异构图为交通场景提供了强大的数据表示能力，因为它们能够模拟数量可变的交通参与者与底层道路基础设施之间复杂的交互效应。随着图神经网络作为伴随的深度学习框架的近期兴起，图结构可被高效地用于诸如轨迹预测等各类机器学习应用。作为同类首创，我们提出的Python框架提供了一条易于使用且高度可定制的数据处理流水线，能够从交通场景中提取标准化的图数据集。该框架为基于图神经网络的自动驾驶研究提供了平台，提升了不同方法之间的可比性，使研究人员能够专注于模型实现而非数据集构建。