With the development of deep learning technology, the problem of data-driven trajectory prediction and intention recognition has been widely studied. However, the pedestrian trajectory prediction and intention recognition methods based solely on data-driven have weak data description ability and black-box characteristics, which cannot reason about pedestrian crossing intention and predict pedestrian crossing trajectory as humans do. To address the above problems, the authors proposed a data and knowledge-driven pedestrian intention estimation and trajectory prediction method by imitating human cognitive mechanisms. In the pedestrian intention inference process, the authors adopted the knowledge-driven method. As a first step, the authors built a knowledge graph of pedestrian crossing scenes, and then paired it with a Bayesian network to estimate pedestrian crossing intentions. In the pedestrian trajectory prediction process, the authors used a data-driven approach, combining pedestrian crossing trajectory features and knowledge-based pedestrian intentions. Experiments show that all evaluation metrics of pedestrian trajectory prediction were improved after adding pedestrian intentions obtained by knowledge-driven.

中文翻译：

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基于数据和知识驱动方法的行人意图估计和轨迹预测

随着深度学习技术的发展，数据驱动的轨迹预测和意图识别问题得到了广泛的研究。然而，单纯基于数据驱动的行人轨迹预测和意图识别方法存在数据描述能力弱和黑箱特性，无法像人类一样推理行人过街意图并预测行人过街轨迹。针对上述问题，作者模仿人类认知机制，提出了一种数据和知识驱动的行人意图估计和轨迹预测方法。在行人意图推理过程中，作者采用了知识驱动的方法。第一步，作者构建了行人过路场景的知识图，然后将其与贝叶斯网络配对以估计行人过路意图。在行人轨迹预测过程中，作者采用了数据驱动的方法，结合了行人过街轨迹特征和基于知识的行人意图。实验表明，加入知识驱动获得的行人意图后，行人轨迹预测的所有评价指标均得到改善。