Grip: Graph-based interaction-aware trajectory prediction
Nowadays, autonomous driving cars have become commercially available. However, the
safety of a self-driving car is still a challenging problem that has not been well studied …
safety of a self-driving car is still a challenging problem that has not been well studied …
Grip++: Enhanced graph-based interaction-aware trajectory prediction for autonomous driving
Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …
driving car is still a challenging problem that has not been well studied. Motion prediction is …
AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving
K Zhang, L Zhao, C Dong, L Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
Gisnet: Graph-based information sharing network for vehicle trajectory prediction
The trajectory prediction is a critical and challenging problem in the design of an
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber …
Scale-net: Scalable vehicle trajectory prediction network under random number of interacting vehicles via edge-enhanced graph convolutional neural network
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is
one of the most challenging problems in developing an autonomous vehicle. Since there is …
one of the most challenging problems in developing an autonomous vehicle. Since there is …
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 …
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 …
Interaction-aware trajectory prediction of connected vehicles using CNN-LSTM networks
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
Trajectory forecasting based on prior-aware directed graph convolutional neural network
Predicting the motion trajectories of moving agents in complex traffic scenes, such as
crossroads and roundabouts, plays an important role in cooperative intelligent transportation …
crossroads and roundabouts, plays an important role in cooperative intelligent transportation …
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 …