  Inferring relational behavior between road users as well as road users and
their surrounding physical space is an important step toward effective modeling
and prediction of navigation strategies adopted by participants in road scenes.
To this end, we propose a relation-aware framework for future trajectory
forecast. Our system aims to infer relational information from the interactions
of road users with each other and with the environment. The first module
involves visual encoding of spatio-temporal features, which captures
human-human and human-space interactions over time. The following module
explicitly constructs pair-wise relations from spatio-temporal interactions and
identifies more descriptive relations that highly influence future motion of
the target road user by considering its past trajectory. The resulting
relational features are used to forecast future locations of the target, in the
form of heatmaps with an additional guidance of spatial dependencies and
consideration of the uncertainty. Extensive evaluations on the public benchmark
datasets demonstrate the robustness and efficacy of the proposed framework as
observed by performances higher than the state-of-the-art methods.
