Pedestrian Trajectory Prediction with MLP-social-GRU

When crossing a crowded area, a person can predict dangers or collisions in advance around him/her, and then makes a suitable decision which direction he/she should take. The pedestrian trajectory prediction aims at simulating such ability of humans in a crowded environment. Most of the existing trajectory predictions are all based on the traditional hand-crafted methods that often ignore critical factors and can only be adapted to specific environments. Based on deep learning technology, this paper proposes a data-driven pedestrian trajectory predictor called MLP-social-GRU. First, the proposed predictor processes a pedestrian trajectory with a Multilayer Perceptron (MLP). Then, it adopts Gated Recurrent Units (GRU) to get hidden features of a pedestrian motion patterns, from which relationships between pedestrians can be simulated. Next, the social-pooling is used to receive and merge the hidden status information to get the mutual influence of adjacent pedestrians. Finally, a unified pedestrian trajectory prediction framework is designed based on abovementioned modules. We evaluate our predictor on two publicly available datasets, ETH and UCY, and the results show that it is superior to popular models such as LSTM, social-LSTM, and goal-social-array.