STAG: A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles
MN Azadani, A Boukerche - Ad Hoc Networks, 2023 - Elsevier
Understanding social interactions between a vehicle and its surrounding agents enables
effective path prediction, which is critical for the motion planning and safe navigation of …
effective path prediction, which is critical for the motion planning and safe navigation of …
PGCN: Progressive graph convolutional networks for spatial–temporal traffic forecasting
The complex spatial-temporal correlations in transportation networks make the traffic
forecasting problem challenging. Since transportation system inherently possesses graph …
forecasting problem challenging. Since transportation system inherently possesses graph …
AMGB: Trajectory prediction using attention-based mechanism GCN-BiLSTM in IOV
R Li, Y Qin, J Wang, H Wang - Pattern Recognition Letters, 2023 - Elsevier
Accurate and reliable prediction of vehicle trajectories is closely related to the path planning
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Dynamic Graph Neural Networks (DGNNs) have become one of the most promising
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed …
MVHGN: Multi-view adaptive hierarchical spatial graph convolution network based trajectory prediction for heterogeneous traffic-agents
D Xu, X Shang, H Peng, H Li - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
The future trajectory prediction of heterogeneous traffic-agents for autonomous vehicles in
mixed traffic scene is of great significance for safe and reliable driving. Thus, we propose the …
mixed traffic scene is of great significance for safe and reliable driving. Thus, we propose the …
Gated residual recurrent graph neural networks for traffic prediction
Traffic prediction is of great importance to traffic management and public safety, and very
challenging as it is affected by many complex factors, such as spatial dependency of …
challenging as it is affected by many complex factors, such as spatial dependency of …
STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction
X Yu, YX Bao, Q Shi - Heliyon, 2023 - cell.com
Nowadays, as a crucial component of intelligent transportation systems, traffic flow
prediction has received extensive concern. However, most of the existing studies extracted …
prediction has received extensive concern. However, most of the existing studies extracted …
Vehicle trajectory prediction using hierarchical graph neural network for considering interaction among multimodal maneuvers
E Jo, M Sunwoo, M Lee - Sensors, 2021 - mdpi.com
Predicting the trajectories of surrounding vehicles by considering their interactions is an
essential ability for the functioning of autonomous vehicles. The subsequent movement of a …
essential ability for the functioning of autonomous vehicles. The subsequent movement of a …
[PDF][PDF] LSGCN: Long short-term traffic prediction with graph convolutional networks.
Traffic prediction is a classical spatial-temporal prediction problem with many real-world
applications such as intelligent route planning, dynamic traffic management, and smart …
applications such as intelligent route planning, dynamic traffic management, and smart …