Pedestrian Trajectory Prediction Model Based on Self-Attention Mechanism and Group Behavior Characteristics

Understanding and accurately predicting the trajectory of pedestrians is of great significance to improve the level of auto-driving technology and reduce the occurrence of traffic accidents. Aiming at the problems of low prediction accuracy and insufficient utilization of pedestrian interaction information, a pedestrian trajectory prediction model based on self-attention mechanism and group behavior characteristics is presented. The model considers the movement information of each pedestrian and its interaction with the surrounding pedestrians. The recurrent neural network and graph convolutional network are used to model the pedestrian's walking state and the pedestrian's interaction separately. In the graph convolutional network, nodes of the graph represent the movement information of pedestrians, and edges of the graph represent the interaction between pedestrians. Self-attention mechanism is used to calculate pedestrian interaction. In addition, in order to increase the ability of the network to capture the group walk behavior and decrease the prediction error, a peer loss function is proposed. Experiments on public datasets show that the proposed model in this paper improves prediction accuracy significantly.