Pedestrian trajectory prediction is an essential task in robotic applications
such as autonomous driving and robot navigation. State-of-the-art trajectory
predictors use a conditional variational autoencoder (CVAE) with recurrent
neural networks (RNNs) to encode observed trajectories and decode multi-modal
future trajectories. This process can suffer from accumulated errors over long
prediction horizons (>=2 seconds). This paper presents BiTraP, a
goal-conditioned bi-directional multi-modal trajectory prediction method based
on the CVAE. BiTraP estimates the goal (end-point) of trajectories and
introduces a novel bi-directional decoder to improve longer-term trajectory
prediction accuracy. Extensive experiments show that BiTraP generalizes to both
first-person view (FPV) and bird's-eye view (BEV) scenarios and outperforms
state-of-the-art results by ~10-50%. We also show that different choices of
non-parametric versus parametric target models in the CVAE directly influence
the predicted multi-modal trajectory distributions. These results provide
guidance on trajectory predictor design for robotic applications such as
collision avoidance and navigation systems.

本论文介绍了一种基于条件变分自编码器的双向多模态轨迹预测方法 BiTraP，该方法采用目标条件化方法和双向编 / 解码器结构以提高长期轨迹预测准确性，其在第一人称视角和鸟瞰视角均表现良好，比最先进的轨迹预测方法提高了约 10-50%。研究还发现，CVAE 中的目标模型对预测的多模态轨迹分布产生直接影响，这为碰撞回避和导航系统等机器人应用的轨迹预测器设计提供了指导。

BiTraP: 双向行人轨迹预测与多模态目标估计

BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal  Goal Estimation

Human trajectory forecasting with multiple socially interacting agents is of
critical importance for autonomous navigation in human environments, e.g., for
self-driving cars and social robots. In this work, we present Predicted
Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
PECNet infers distant trajectory endpoints to assist in long-range multi-modal
trajectory prediction. A novel non-local social pooling layer enables PECNet to
infer diverse yet socially compliant trajectories. Additionally, we present a
simple "truncation-trick" for improving few-shot multi-modal trajectory
prediction performance. We show that PECNet improves state-of-the-art
performance on the Stanford Drone trajectory prediction benchmark by ~20.9% and
on the ETH/UCY benchmark by ~40.8%. Project homepage:
this https URL

该研究提出了 Predicted Endpoint Conditioned Network (PECNet) 方法用于灵活的人类轨迹预测，在 Drobe 和 ETH/UCY 数据集上的结果分别比之前最好结果提高了约 20.9% 和 40.8%。