  This paper addresses motion forecasting in multi-agent environments, pivotal
for ensuring safety of autonomous vehicles. Traditional as well as recent
data-driven marginal trajectory prediction methods struggle to properly learn
non-linear agent-to-agent interactions. We present SSL-Interactions that
proposes pretext tasks to enhance interaction modeling for trajectory
prediction. We introduce four interaction-aware pretext tasks to encapsulate
various aspects of agent interactions: range gap prediction, closest distance
prediction, direction of movement prediction, and type of interaction
prediction. We further propose an approach to curate interaction-heavy
scenarios from datasets. This curated data has two advantages: it provides a
stronger learning signal to the interaction model, and facilitates generation
of pseudo-labels for interaction-centric pretext tasks. We also propose three
new metrics specifically designed to evaluate predictions in interactive
scenes. Our empirical evaluations indicate SSL-Interactions outperforms
state-of-the-art motion forecasting methods quantitatively with up to 8%
improvement, and qualitatively, for interaction-heavy scenarios.
