LTN: Long-Term Network for Long-Term Motion Prediction

• URL: http://arxiv.org/abs/2010.07931v1
• Date: Thu, 15 Oct 2020 17:59:09 GMT
• Title: LTN: Long-Term Network for Long-Term Motion Prediction
• Authors: YingQiao Wang
• Abstract summary: We present a two-stage framework for long-term trajectory prediction, which is named as Long-Term Network (LTN) 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. The results show that our method outperforms multiple state-of-the-art approaches in long-term trajectory prediction in terms of accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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