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：异构图凭借其能够建模数量变化的交通参与者与底层道路基础设施之间复杂交互效应的能力，为交通领域提供了强大的数据表示方法。随着图神经网络（GNN）作为配套深度学习框架的近期兴起，图结构可被高效应用于轨迹预测等多种机器学习任务。作为首个此类框架，我们提出的Python工具包提供了一个易于使用且完全可定制的数据处理流水线，用于从交通场景中提取标准化图数据集。通过为基于GNN的自动驾驶研究提供平台，该框架提升了不同方法之间的可比较性，使研究人员能够专注于模型实现而非数据集构建。