Pedestrian Trajectory Prediction Based on Motion Pattern De-Perturbation Strategy

Abstract

Pedestrian trajectory prediction is extremely challenging due to the complex social attributes of pedestrians. Introducing latent vectors to model trajectory multimodality has become the latest mainstream solution idea. However, previous approaches have overlooked the effects of redundancy that arise from the introduction of latent vectors. Additionally, they often fail to consider the inherent interference of pedestrians with no trajectory history during model training. This results in the model’s inability to fully utilize the training data. Therefore, we propose a two-stage motion pattern de-perturbation strategy, which is a plug-and-play approach that introduces optimization features to model the redundancy effect caused by latent vectors, which helps to eliminate the redundancy effects in the trajectory prediction phase. We also propose loss masks to reduce the interference of invalid data during training to accurately model pedestrian motion patterns with strong physical interpretability. Our comparative experiments on the publicly available ETH and UCY pedestrian trajectory datasets, as well as the Stanford UAV dataset, show that our optimization strategy achieves better pedestrian trajectory prediction accuracies than a range of state-of-the-art baseline models; in particular, our optimization strategy effectively absorbs the training data to assist the baseline models in achieving optimal modeling accuracy.