  Pedestrian trajectory prediction is a critical to avoid autonomous driving
collision. But this prediction is a challenging problem due to social forces
and cluttered scenes. Such human-human and human-space interactions lead to
many socially plausible trajectories. In this paper, we propose a novel
LSTM-based algorithm. We tackle the problem by considering the static scene and
pedestrian which combine the Graph Convolutional Networks and Temporal
Convolutional Networks to extract features from pedestrians. Each pedestrian in
the scene is regarded as a node, and we can obtain the relationship between
each node and its neighborhoods by graph embedding. It is LSTM that encode the
relationship so that our model predicts nodes trajectories in crowd scenarios
simultaneously. To effectively predict multiple possible future trajectories,
we further introduce Spatio-Temporal Convolutional Block to make the network
flexible. Experimental results on two public datasets, i.e. ETH and UCY,
demonstrate the effectiveness of our proposed ST-Block and we achieve
state-of-the-art approaches in human trajectory prediction.
