Pedestrian intention estimation and trajectory prediction based on data and knowledge-driven method

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.