Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks
Predicting the multimodal future motions of neighboring agents is essential for an autonomous vehicle to navigate complex scenarios. It is challenging as the motion of an agent is affected by the complex interaction among itself, other agents, and the local roads. Unlike most existing works, which p...
Saved in:
Main Authors: | Mo, Xiaoyu, Xing, Yang, Liu, Haochen, Lv, Chen |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170248 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction
by: Mo, Xiaoyu, et al.
Published: (2024) -
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
by: Mo, Xiaoyu, et al.
Published: (2022) -
Neural Graph Collaborative Filtering
by: Xiang Wang, et al.
Published: (2020) -
Spatio-temporal interaction aware and trajectory distribution aware graph convolution network for pedestrian multimodal trajectory prediction
by: Wang, Ruiping, et al.
Published: (2023) -
KGAT: Knowledge Graph Attention Network for Recommendation
by: Xiang Wang, et al.
Published: (2020)