Multi-personnel underground trajectory prediction method based on Social Transformer

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