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.

翻译：本文研究了多智能体环境下的运动预测问题，这对确保自动驾驶安全性至关重要。传统方法以及最新的数据驱动边际轨迹预测方法在正确学习非线性智能体间交互方面存在困难。我们提出SSL-Interactions方法，通过设计预文本任务增强轨迹预测中的交互建模能力。我们引入四种交互感知预文本任务，分别从不同维度刻画智能体交互特征：距离间隔预测、最近距离预测、运动方向预测和交互类型预测。进一步提出从数据集中筛选高交互场景的策略，该策略具有双重优势：既能为交互模型提供更强的学习信号，又能促进交互中心预文本任务的伪标签生成。另外，我们设计了三种专用于评估交互场景预测性能的新指标。实验表明，SSL-Interactions在交互密集场景中，定量指标上相较于最先进的运动预测方法取得最高8%的性能提升，定性分析也展现出显著优势。