Distilling Knowledge for Short-to-Long Term Trajectory Prediction

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).