论文标题
LTN：长期运动预测的长期网络
LTN: Long-Term Network for Long-Term Motion Prediction
论文作者
论文摘要
当机器人试图执行自主导航任务时，对行人和车辆等周围代理进行准确的运动预测是一项关键任务。在短期预测中，有关多模式轨迹预测的最新研究，包括回归和分类方法。但是，在长期预测方面，大多数基于短期的短期记忆（LSTM）模型往往会远离地面真相。因此，在这项工作中，我们提出了长期轨迹预测的两阶段框架，该框架被称为长期网络（LTN）。我们的长期网络既集成回归和分类方法。我们首先使用条件变化自动编码器（CVAE）生成一组提出的轨迹，并使用我们的建议分布，然后将它们与二进制标签进行分类，然后以最高分数输出轨迹。我们通过在两个真实世界的行人数据集上进行实验来展示我们的长期网络的性能：ETH/UCY，Stanford Drone DataSet（SDD），以及一个具有挑战性的现实世界驱动器预测数据集：Nuscenes。结果表明，我们的方法在准确性方面超过了长期轨迹预测的多种最新方法。
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.
