Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network
B Dong, H Liu, Y Bai, J Lin, Z Xu, X Xu… - arXiv preprint arXiv …, 2021 - arxiv.org
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous
driving cars to achieve a high degree of road safety. There are several challenges in …
driving cars to achieve a high degree of road safety. There are several challenges in …
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 …
Dynamic scenario representation learning for motion forecasting with heterogeneous graph convolutional recurrent networks
Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a
challenging problem in autonomous driving. Most existing works exploit static road graphs to …
challenging problem in autonomous driving. Most existing works exploit static road graphs to …
Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph
L Tang, F Yan, B Zou, W Li, C Lv… - IET Intelligent Transport …, 2023 - Wiley Online Library
Predicting the trajectories of surrounding heterogeneous traffic agents is critical for the
decision making of an autonomous vehicle. Recently, many existing prediction methods …
decision making of an autonomous vehicle. Recently, many existing prediction methods …
Graph Neural Network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle
D Singh, R Srivastava - Applied Intelligence, 2022 - Springer
Trajectory prediction is an essential ability for the intelligent transportation system to
navigate through complex traffic scenes. In recent times, trajectory prediction has become an …
navigate through complex traffic scenes. In recent times, trajectory prediction has become an …
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 …
Trajectory prediction with graph-based dual-scale context fusion
Motion prediction for traffic participants is essential for a safe and robust automated driving
system, especially in cluttered urban environments. However, it is highly challenging due to …
system, especially in cluttered urban environments. However, it is highly challenging due to …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
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 …
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 …