Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets, and the tailed data is often more complicated and safety-critical. In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction. Previous methods dealing with long-tail data did not take into account the variety of motion patterns in the tailed data. In this paper, we put forward a future enhanced contrastive learning framework to recognize tail trajectory patterns and form a feature space with separate pattern clusters. Furthermore, a distribution aware hyper predictor is brought up to better utilize the shaped feature space. Our method is a model-agnostic framework and can be plugged into many well-known baselines. Experimental results show that our framework outperforms the state-of-the-art long-tail prediction method on tailed samples by 9.5% on ADE and 8.5% on FDE, while maintaining or slightly improving the averaged performance. Our method also surpasses many long-tail techniques on trajectory prediction task.

翻译：预测交通参与者未来轨迹是自动驾驶中的一项关键技术。然而，现有主流数据集存在数据不平衡问题，尾部数据通常更为复杂且关乎安全。本文聚焦于处理轨迹预测中的长尾现象。以往处理长尾数据的方法未能充分考虑尾部数据中运动模式的多样性。为此，我们提出一种未来增强对比学习框架，用于识别尾部轨迹模式并构建具有分离模式聚类的特征空间。进一步，我们引入一种分布感知超预测器，以更好地利用形成的特征空间。我们的方法是一个模型无关框架，可集成到多种知名基线模型中。实验结果表明，在尾部样本上，我们的框架在ADE（平均位移误差）和FDE（最终位移误差）上分别比最先进的长尾预测方法提升9.5%和8.5%，同时保持或略微提升平均性能。此外，我们的方法在轨迹预测任务上优于多种长尾技术。