Long-term trajectory forecasting is an important and challenging problem in the fields of computer vision, machine learning, and robotics. One fundamental difficulty stands in the evolution of the trajectory that becomes more and more uncertain and unpredictable as the time horizon grows, subsequently increasing the complexity of the problem. To overcome this issue, in this paper, we propose Di-Long, a new method that employs the distillation of a short-term trajectory model forecaster that guides a student network for long-term trajectory prediction during the training process. Given a total sequence length that comprehends the allowed observation for the student network and the complementary target sequence, we let the student and the teacher solve two different related tasks defined over the same full trajectory: the student observes a short sequence and predicts a long trajectory, whereas the teacher observes a longer sequence and predicts the remaining short target trajectory. The teacher's task is less uncertain, and we use its accurate predictions to guide the student through our knowledge distillation framework, reducing long-term future uncertainty. Our experiments show that our proposed Di-Long method is effective for long-term forecasting and achieves state-of-the-art performance on the Intersection Drone Dataset (inD) and the Stanford Drone Dataset (SDD).

翻译：长程轨迹预测是计算机视觉、机器学习和机器人领域中一个重要且具有挑战性的问题。其根本难点在于，随着时间跨度的增长，轨迹的演化变得越来越不确定和难以预测，进而增加了问题的复杂性。为解决这一问题，本文提出Di-Long方法，这是一种新的方法，在训练过程中利用短程轨迹模型预测器的知识蒸馏来指导学生网络进行长程轨迹预测。考虑包含学生网络允许观测序列和互补目标序列的总序列长度，我们让学生网络和教师网络解决定义在同一完整轨迹上的两个不同但相关的任务：学生网络观测短序列并预测长轨迹，而教师网络观测较长序列并预测剩余短程目标轨迹。教师网络的任务不确定性较低，我们利用其精确预测，通过知识蒸馏框架指导学生网络，从而降低长程未来的不确定性。实验表明，我们提出的Di-Long方法在长程预测方面效果显著，并在Intersection Drone数据集（inD）和Stanford Drone数据集（SDD）上达到了当前最优性能。