Long-term trajectory forecasting is a challenging problem in the field of computer vision and machine learning. In this paper, we propose a new method dubbed Di-Long ("Distillation for Long-Term trajectory") for long-term trajectory forecasting, which is based on knowledge distillation. Our approach involves training a student network to solve the long-term trajectory forecasting problem, whereas the teacher network from which the knowledge is distilled has a longer observation, and solves a short-term trajectory prediction problem by regularizing the student's predictions. Specifically, we use a teacher model to generate plausible trajectories for a shorter time horizon, and then distill the knowledge from the teacher model to a student model that solves the problem for a much higher time horizon. Our experiments show that the proposed Di-Long approach is beneficial for long-term forecasting, and our model achieves state-of-the-art performance on the Intersection Drone Dataset (inD) and the Stanford Drone Dataset (SDD).

翻译：长期轨迹预测是计算机视觉与机器学习领域的一项具有挑战性的问题。本文提出了一种名为Di-Long（基于知识蒸馏的长期轨迹预测）的新方法，该方法基于知识蒸馏技术。我们的核心思路是训练一个学生网络来解决长期轨迹预测问题，而提供蒸馏知识的教师网络则拥有更长的观测时间，并通过正则化学生网络的输出来解决短期轨迹预测问题。具体而言，我们使用教师模型生成较短时间范围内的合理轨迹，然后将知识从教师模型蒸馏到学生模型中，使学生模型能够在更长的时间范围内解决问题。实验表明，所提出的Di-Long方法对长期轨迹预测具有显著优势，我们的模型在交叉路口无人机数据集（inD）和斯坦福无人机数据集（SDD）上均达到了最先进的性能水平。