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：长期轨迹预测是计算机视觉、机器学习和机器人领域中重要且具有挑战性的问题。一个根本性难点在于，随着时间跨度的增加，轨迹演化变得越来越不确定和难以预测，进而提升了问题的复杂性。为解决这一问题，本文提出Di-Long方法，该方法通过蒸馏短程轨迹模型预测器的知识，在训练过程中指导学生网络进行长程轨迹预测。给定包含学生网络允许观测序列及互补目标序列的总序列长度，我们让学生和教师模型解决基于同一完整轨迹定义的两个不同但相关的任务：学生观测短序列并预测长轨迹，而教师观测较长序列并预测剩余短目标轨迹。教师任务的不确定性较低，我们利用其精确预测通过知识蒸馏框架指导学生，从而降低长程未来不确定性。实验表明，我们提出的Di-Long方法在长程预测中表现有效，并在Intersection Drone Dataset（inD）和Stanford Drone Dataset（SDD）数据集上达到了最先进性能。