.png){kind=icon}
返回
Learning to predict diverse trajectory from human motion patterns
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2022.06.115.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Diverse and reasonable multi-modal trajectory prediction of pedestrian is vital for the safety of intelligent autonomous systems. Currently, most of existing methods primarily focus on modeling the extrinsic interactions of pedestrians, but neglect to take advantage of the intrinsic properties of pedestrian motion, which may generate unrealistic pedestrian movement. We investigate the natural motion of pedestrians and explore future motion patterns of pedestrians with similar observed trajectories, which provides a new perspective for multi-modal prediction of pedestrian trajectories. On this basis, we propose a novel model to learn diverse trajectories from human motion patterns, which is mainly composed of a motion pattern selector and a multi-modal trajectory generator. With a constructed pattern gallery, the motion pattern selector mines the possible future motion patterns based on the similar observed trajectories. Subsequently, the diverse future motion patterns are fed into the multi-modal trajectory generator, in which these motion patterns are selected and refined through a scoring network and a regression net-work, and then directly generate multiple diverse trajectories. We use two public pedestrian trajectory ETH/UCY datasets to compare our method with several baseline methods. Experimental results show that our model outperforms most of the state-of-the-art approaches both in accuracy and diversity by fully leveraging the intrinsic information of trajectories.(c) 2022 Published by Elsevier B.V.
Keyword:
Human motion pattern learning
Multi -modal trajectory prediction
Diverse trajectory prediction
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory predictionAst-gnn: 基于注意力的时空图神经网络，用于交互感知的行人轨迹预测.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容