  Forecasting human trajectories in traffic scenes is critical for safety
within mixed or fully autonomous systems. Human future trajectories are driven
by two major stimuli, social interactions, and stochastic goals. Thus, reliable
forecasting needs to capture these two stimuli. Edge-based relation modeling
represents social interactions using pairwise correlations from precise
individual states. Nevertheless, edge-based relations can be vulnerable under
perturbations. To alleviate these issues, we propose a region-based relation
learning paradigm that models social interactions via region-wise dynamics of
joint states, i.e., the changes in the density of crowds. In particular,
region-wise agent joint information is encoded within convolutional feature
grids. Social relations are modeled by relating the temporal changes of local
joint information from a global perspective. We show that region-based
relations are less susceptible to perturbations. In order to account for the
stochastic individual goals, we exploit a conditional variational autoencoder
to realize multi-goal estimation and diverse future prediction. Specifically,
we perform variational inference via the latent distribution, which is
conditioned on the correlation between input states and associated target
goals. Sampling from the latent distribution enables the framework to reliably
capture the stochastic behavior in test data. We integrate multi-goal
estimation and region-based relation learning to model the two stimuli, social
interactions, and stochastic goals, in a prediction framework. We evaluate our
framework on the ETH-UCY dataset and Stanford Drone Dataset (SDD). We show that
the diverse prediction better fits the ground truth when incorporating the
relation module. Our framework outperforms the state-of-the-art models on SDD
by $27.61\%$/$18.20\%$ of ADE/FDE metrics.
