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 …
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 …
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 …
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 …
Scout: Socially-consistent and understandable graph attention network for trajectory prediction of vehicles and vrus
Autonomous vehicles navigate in dynamically changing environments under a wide variety
of conditions, being continuously influenced by surrounding objects. Mod-elling interactions …
of conditions, being continuously influenced by surrounding objects. Mod-elling interactions …
S2tnet: Spatio-temporal transformer networks for trajectory prediction in autonomous driving
W Chen, F Wang, H Sun - Asian conference on machine …, 2021 - proceedings.mlr.press
To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles
require to sufficiently analyze the motion patterns of surrounding traffic-agents and …
require to sufficiently analyze the motion patterns of surrounding traffic-agents and …
Trafficpredict: Trajectory prediction for heterogeneous traffic-agents
To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …
Emsin: enhanced multi-stream interaction network for vehicle trajectory prediction
Predicting the future trajectories of dynamic traffic actors is the Gordian knot for autonomous
vehicles to achieve collision-free driving. Most existing works suffer from a gap in …
vehicles to achieve collision-free driving. Most existing works suffer from a gap in …
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 …
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 …