Abstract: Due to the uncertainty of human behaviour and the inherent multi-modality of predicting the future,it becomes an ine-vitable issue to discern the significance and likelihood of predicted pedestrian trajectories.Pedestrian motion patterns can be used as the benchmark characteristics for differentiation.In addition,most existing studies have examined pedestrian trajectories only in terms of the temporal dimension,ignoring the potentially great help of the frequency dimension of trajectories.This paper proposes a pedestrian trajectory prediction model based on the motion patterns and time-frequency domain fusion,called MPTF.The probabilistic prediction sub-network of MPTF extracts the time and frequency domain feature embedding of trajectories.By combining pedestrian motion patterns,it predicts the occurrence probability of future trajectories through a classification task.The time-domain branch of the regression prediction sub-network mines the social relationships among pedestrians,while the frequency-domain branch focuses on the contributions of different frequency components of the trajectories to the prediction.The gated fusion network fuses the features from these two dimensions and conducts regression inference to obtain multi-modal future tra-jectories.Experimental results on multiple public datasets show that the model has achieved a level equivalent to that of the latest studies in terms of the evaluation metrics of Average Displacement Error(ADE) and Final Displacement Error(FDE).On the UNIV dataset,the model obtains optimal results of 0.22 and 0.40 for ADE and FDE respectively.Moreover,on the ETH dataset,the FDE is improved by 8.5%.This validates the effectiveness of the method that combines time-frequency domain trajectory features.

Key words: Pedestrian trajectory prediction, Motion patterns, Frequency domain feature, Attention mechanism, Transformer