Pedestrian intention prediction is crucial for autonomous driving. In particular, knowing if pedestrians are going to cross in front of the ego-vehicle is core to performing safe and comfortable maneuvers. Creating accurate and fast models that predict such intentions from sequential images is challenging. A factor contributing to this is the lack of datasets with diverse crossing and non-crossing (C/NC) scenarios. We address this scarceness by introducing a framework, named ARCANE, which allows programmatically generating synthetic datasets consisting of C/NC video clip samples. As an example, we use ARCANE to generate a large and diverse dataset named PedSynth. We will show how PedSynth complements widely used real-world datasets such as JAAD and PIE, so enabling more accurate models for C/NC prediction. Considering the onboard deployment of C/NC prediction models, we also propose a deep model named PedGNN, which is fast and has a very low memory footprint. PedGNN is based on a GNN-GRU architecture that takes a sequence of pedestrian skeletons as input to predict crossing intentions.

为了实现安全和舒适的驾驶操控，行人意图预测对于自动驾驶至关重要。本研究提出了一个名为ARCANE的框架，可以通过生成C/NC视频段样本的方式来解决现有数据集中缺乏多样性的问题。同时，还介绍了一个名为PedSynth的大型多样化数据集，该数据集补充了常用的真实世界数据集，例如JAAD和PIE，以改进C/NC预测模型的准确性。考虑到C/NC预测模型在车载环境中的部署，研究还提出了一种名为PedGNN的深度模型，该模型速度快、内存占用低，基于GNN-GRU架构，以行人骨架序列作为输入来预测过马路的意图。

行人意图预测的合成数据生成框架、数据集和高效深度模型