Predicting a Pedestrian Trajectory Using Seq2Seq for Mobile Robot Navigation

This paper proposes a method to predict the future trajectory of a pedestrian as sequence data by using massive trajectory records collected by various sensor devices. We aim to use the method for safely and efficient path planning of autonomous mobile robots in a human-robot coexisting environment. For the prediction, we use a sequence-to-sequence model, which is frequently used in the field of natural language processing and enables to treat long-term sequence data. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall. The result shows that our method can predict sequences sufficiently by converting the trajectory data to adequate sequence data.