  As more and more robots are envisioned to cooperate with humans sharing the
same space, it is desired for robots to be able to predict others' trajectories
to navigate in a safe and self-explanatory way. We propose a Convolutional
Neural Network-based approach to learn, detect, and extract patterns in
sequential trajectory data, known here as Social Pattern Extraction Convolution
(Social-PEC). A set of experiments carried out on the human trajectory
prediction problem shows that our model performs comparably to the state of the
art and outperforms in some cases. More importantly, the proposed approach
unveils the obscurity in the previous use of a pooling layer, presenting a way
to intuitively explain the decision-making process.
