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 …
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 …
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 …
Social-wagdat: Interaction-aware trajectory prediction via wasserstein graph double-attention network
Effective understanding of the environment and accurate trajectory prediction of surrounding
dynamic obstacles are indispensable for intelligent mobile systems (like autonomous …
dynamic obstacles are indispensable for intelligent mobile systems (like autonomous …
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 …
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 …
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 …
Exploring dynamic context for multi-path trajectory prediction
To accurately predict future positions of different agents in traffic scenarios is crucial for
safely deploying intelligent autonomous systems in the real-world environment. However, it …
safely deploying intelligent autonomous systems in the real-world environment. However, it …
Context‐aware trajectory prediction for autonomous driving in heterogeneous environments
The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …
remains a challenging task for autonomous driving due to several critical issues, such as …
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 …