  Forecasting human trajectories is critical for tasks such as robot crowd
navigation and autonomous driving. Modeling social interactions is of great
importance for accurate group-wise motion prediction. However, most existing
methods do not consider information about coherence within the crowd, but
rather only pairwise interactions. In this work, we propose a novel framework,
coherent motion aware graph convolutional network (CoMoGCN), for trajectory
prediction in crowded scenes with group constraints. First, we cluster
pedestrian trajectories into groups according to motion coherence. Then, we use
graph convolutional networks to aggregate crowd information efficiently. The
CoMoGCN also takes advantage of variational autoencoders to capture the
multimodal nature of the human trajectories by modeling the distribution. Our
method achieves state-of-the-art performance on several different trajectory
prediction benchmarks, and the best average performance among all benchmarks
considered.
