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...
Main Authors: | Mo, Xiaoyu, Xing, Yang, Liu, Haochen, Lv, Chen |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170248 |
Similar Items
-
Predictive neural motion planner for autonomous driving using graph networks
by: Mo, Xiaoyu, et al.
Published: (2024) -
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) -
Polarized message-passing in graph neural networks
by: He, Tiantian, et al.
Published: (2024) -
Graph neural networks with a distribution of parametrized graphs
by: Lee, See Hian, et al.
Published: (2024)