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：长时轨迹预测是计算机视觉、机器学习和机器人学领域中一个重要且具有挑战性的问题。一个根本性困难在于轨迹的演变会随着时间范围的延长而变得越来越不确定和难以预测，从而增加了问题的复杂性。为克服这一问题，本文提出了一种新方法Di-Long，该方法在训练过程中利用短时轨迹模型预测器的蒸馏来指导学生网络进行长时轨迹预测。给定一个包含学生网络允许观测序列及其互补目标序列的总序列长度，我们让学生和教师解决定义在同一条完整轨迹上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的短目标轨迹。教师任务的不确定性较低，我们通过知识蒸馏框架利用其精确预测来指导学生，从而降低长时未来的不确定性。实验表明，我们提出的Di-Long方法在长时预测中效果显著，并在Intersection Drone Dataset (inD)和Stanford Drone Dataset (SDD)上取得了最先进的性能。