Abstract: With the rapid development of the technology of computer vision and autonomous driving, the ability to sense, understand and predict human behavior is becoming more and more important. The popularity of various sensors has generated a large amount of position data of moving objects in society. Predicting the movement trajectory of pedestrians based on these data has great value in social prediction and other fields. To gain insight into the development in this area, a literature review is conducted on graph neural network-based pedestrian trajectory prediction methods. The graph neural network algorithms for pedestrian trajectory prediction are compared, analyzed and summarized from multiple perspectives, and the research and development of different algorithms in this field are discussed. The comparison and analysis are carried out on the current public data sets, an overview of the corresponding performance indicators is provided, and the performance comparison results of different algorithms are given. At the same time, this paper puts forward the research problems that still exist and looks forward to the possible research directions in the future.

Key words: pedestrian trajectory prediction, visual prediction, graph neural network, deep neural network, autonomous driving