基于Social Transformer的井下多人轨迹预测方法OA北大核心CSTPCD

Currently,in the prediction methods of underground personnel trajectories in coal mines,Transformer not only has lower computational complexity compared to recurrent neural network(RNN)and long short-term memory(LSTM),but also effectively solves the problem of long-term dependence caused by gradient disappearance when processing data.But when multi personnel are moving simultaneously in the environment,the Transformer's prediction of the future trajectories of all personnel in the scene will have a significant deviation.And currently,there is no model in the field of underground multi personnel trajectory prediction that simultaneously uses Transformer and considers the mutual influence between individuals.In order to solve the above problems,a multi personnel underground trajectory prediction method based on Social Transformer is proposed.Firstly,each individual is independently modeled to obtain their historical trajectory information.Feature extraction is performed using a Transformer encoder,followed by a fully connected layer to better represent the features.Secondly,an interactive layer based on graph convolution is used to connect each other,allowing spatially close networks to share information with each other.This layer calculates the attention that the predicted object allocates to its neighbors when influenced by them,extracts their motion patterns,and updates the feature matrix.Finally,the new feature matrix are decoded by the Transformer decoder to output predictions of future position information.The experimental results show that the average displacement error of Social Transformer is reduced by 45.8%compared to Transformer.Compared with other mainstream trajectory prediction methods such as LSTM,S-GAN,Trajectoron++,and S-STGCNN,the prediction errors are reduced by 67.1%,35.9%,30.1%,and 10.9%,respectively.This can effectively overcome the problem of inaccurate prediction trajectories caused by mutual influence among personnel in the underground multi personnel scenario of coal mines and improve prediction precision.