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.

本文研究了在多智能体环境中的动作预测问题，提出了一种名为 SSL-Interactions 的方法，通过预先任务增强了交互建模以进行轨迹预测。该方法引入了四种考虑智能体交互的预先任务，并通过构建交互密集场景的数据集来提高交互模型的学习性能。实证评估结果表明，SSL-Interactions 在定量上比现有的动作预测方法有着高达 8% 的改进，并在交互密集场景中在质量上表现出色。