DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction

J An, W Liu, Q Liu, L Guo, P Ren, T Li - Neural Networks, 2022 - Elsevier
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …
被引用次数：26 相关文章

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction.

J An, W Liu, Q Liu, L Guo, P Ren, T Li - Neural Networks: the Official …, 2022 - europepmc.org
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …

DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction

J An, W Liu, Q Liu, L Guo, P Ren… - Neural networks: the …, 2022 - pubmed.ncbi.nlm.nih.gov
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …

DGInet:: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction

J An, W Liu, Q Liu, L Guo, P Ren, T Li - 2022 - dl.acm.org
This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …