  Making accurate motion prediction of surrounding agents such as pedestrians
and vehicles is a critical task when robots are trying to perform autonomous
navigation tasks. Recent research on multi-modal trajectory prediction,
including regression and classification approaches, perform very well at
short-term prediction. However, when it comes to long-term prediction, most
Long Short-Term Memory (LSTM) based models tend to diverge far away from the
ground truth. Therefore, in this work, we present a two-stage framework for
long-term trajectory prediction, which is named as Long-Term Network (LTN). Our
Long-Term Network integrates both the regression and classification approaches.
We first generate a set of proposed trajectories with our proposed distribution
using a Conditional Variational Autoencoder (CVAE), and then classify them with
binary labels, and output the trajectories with the highest score. We
demonstrate our Long-Term Network's performance with experiments on two
real-world pedestrian datasets: ETH/UCY, Stanford Drone Dataset (SDD), and one
challenging real-world driving forecasting dataset: nuScenes. The results show
that our method outperforms multiple state-of-the-art approaches in long-term
trajectory prediction in terms of accuracy.
