TY - GEN
T1 - Continuous Geodesic Self-Attention Models with Gated Fusion for Trajectory Prediction
AU - Ke, Kexin
AU - Li, Zhengyu
AU - Chen, Huining
AU - Wang, Hao
AU - Wei, Xian
AU - Yang, Jian
AU - Tang, Xuan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Driven by the rapid development of intelligent driving vehicles, predicting the trajectories of pedestrians on the road is crucial for decision-making during driving and even road safety. In this paper, we propose a novel method for trajectory prediction, namely, Continuous Geodesic Self-Attention Models with Gated Fusion (CGSAG). We use geodesic attention to measure the similarity between trajectory points, and utilize a gating mechanism to fuse the geodesic features extracted by multi-layer graph convolution. We then use Neural Ordinary Differential Equations (Neural ODE) to model the continuous-time dynamics of the trajectory. We show that CGSAG improves state-of-the-art performances on several human trajectory prediction datasets, including ETH/UCY, SDD, and Ind. At the same time, we conduct ablation studies to prove the effectiveness and efficiency of our proposed method.
AB - Driven by the rapid development of intelligent driving vehicles, predicting the trajectories of pedestrians on the road is crucial for decision-making during driving and even road safety. In this paper, we propose a novel method for trajectory prediction, namely, Continuous Geodesic Self-Attention Models with Gated Fusion (CGSAG). We use geodesic attention to measure the similarity between trajectory points, and utilize a gating mechanism to fuse the geodesic features extracted by multi-layer graph convolution. We then use Neural Ordinary Differential Equations (Neural ODE) to model the continuous-time dynamics of the trajectory. We show that CGSAG improves state-of-the-art performances on several human trajectory prediction datasets, including ETH/UCY, SDD, and Ind. At the same time, we conduct ablation studies to prove the effectiveness and efficiency of our proposed method.
KW - Geodesic Self-Attention
KW - Neural Ordinary Differential Equations
KW - Trajectory Prediction
UR - https://www.scopus.com/pages/publications/85204943332
U2 - 10.1109/IJCNN60899.2024.10650786
DO - 10.1109/IJCNN60899.2024.10650786
M3 - 会议稿件
AN - SCOPUS:85204943332
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Joint Conference on Neural Networks, IJCNN 2024
Y2 - 30 June 2024 through 5 July 2024
ER -