Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks
The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
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 …
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …
Recog: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction
Predicting the future trajectory of surrounding vehicles is essential for the navigation of
autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's …
autonomous vehicles in complex real-world driving scenarios. It is challenging as a vehicle's …
Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving
Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
Graph-based interaction-aware multimodal 2D vehicle trajectory prediction using diffusion graph convolutional networks
Predicting vehicle trajectories is crucial to ensuring automated vehicle operation efficiency
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …
and safety, particularly on congested multi-lane highways. In such dynamic environments, a …
Group vehicle trajectory prediction with global spatio-temporal graph
D Xu, X Shang, Y Liu, H Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Vehicle trajectory prediction is a challenging problem in the field of autonomous driving,
which is of great significance to the safety of autonomous driving and traffic roads. In view of …
which is of great significance to the safety of autonomous driving and traffic roads. In view of …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …
Graph and recurrent neural network-based vehicle trajectory prediction for highway driving
Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
Trajectory prediction for autonomous driving using spatial-temporal graph attention transformer
K Zhang, X Feng, L Wu, Z He - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …
A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction
Traffic flow forecasting is indispensable in today's society and regarded as a key problem for
Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious …
Intelligent Transportation Systems (ITS), as emergency delays in vehicles can cause serious …