| | |
| | |
Stat |
Members: 3643 Articles: 2'488'730 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
Graph Neural Networks for Modelling Traffic Participant Interaction | Frederik Diehl
; Thomas Brunner
; Michael Truong Le
; Alois Knoll
; | Date: |
4 Mar 2019 | Abstract: | By interpreting a traffic scene as a graph of interacting vehicles, we gain a
flexible abstract representation which allows us to apply Graph Neural Network
(GNN) models for traffic prediction. These naturally take interaction between
traffic participants into account while being computationally efficient and
providing large model capacity. We evaluate two state-of-the art GNN
architectures and introduce several adaptations for our specific scenario. We
show that prediction error in scenarios with much interaction decreases by 30%
compared to a model that does not take interactions into account. This suggests
a graph interpretation of interacting traffic participants is a worthwhile
addition to traffic prediction systems. | Source: | arXiv, 1903.1254 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |