A multi-modal states based vehicle descriptor and dilated convolutional social pooling for vehicle trajectory prediction
H Zhang, Y Wang, J Liu, C Li, T Ma, C Yin - arXiv preprint arXiv …, 2020 - arxiv.org
Precise trajectory prediction of surrounding vehicles is critical for decision-making of
autonomous vehicles and learning-based approaches are well recognized for the …
autonomous vehicles and learning-based approaches are well recognized for the …
Multiple information spatial–temporal attention based graph convolution network for traffic prediction
S Tao, H Zhang, F Yang, Y Wu, C Li - Applied Soft Computing, 2023 - Elsevier
Traffic prediction (forecasting) is a key problem in intelligent transportation. It helps
engineers to obtain traffic trends in advance so that they can make favorable decisions …
engineers to obtain traffic trends in advance so that they can make favorable decisions …
A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction
It is critical for autonomous vehicles to accurately forecast the future trajectories of
surrounding agents to avoid collisions. However, capturing the complex interactions …
surrounding agents to avoid collisions. However, capturing the complex interactions …
Optimized graph convolution recurrent neural network for traffic prediction
Traffic prediction is a core problem in the intelligent transportation system and has broad
applications in the transportation management and planning, and the main challenge of this …
applications in the transportation management and planning, and the main challenge of this …
Spatio-temporal graph convolutional networks via view fusion for trajectory data analytics
Trajectory data contains rich spatial and temporal information. Turning trajectories into
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
graphs and then analyzing them efficiently in an AI-empowered way is a representative …
Road traffic flow prediction based on dynamic spatiotemporal graph attention network
Y Chen, J Huang, H Xu, J Guo, L Su - Scientific reports, 2023 - nature.com
To improve the prediction accuracy of traffic flow under the influence of nearby time traffic
flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction …
flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction …
Graph convolutional dynamic recurrent network with attention for traffic forecasting
J Wu, J Fu, H Ji, L Liu - Applied Intelligence, 2023 - Springer
Traffic forecasting is a typical spatio-temporal graph modeling problem, which has become
one of the key technical issues in modern intelligent transportation systems. However …
one of the key technical issues in modern intelligent transportation systems. However …
LSTM variants meet graph neural networks for road speed prediction
Traffic flow prediction is a fundamental issue in smart cities and plays an important role in
urban traffic planning and management. An accurate predictive model can help individuals …
urban traffic planning and management. An accurate predictive model can help individuals …
Context-aware scene prediction network (caspnet)
M Schäfer, K Zhao, M Bühren… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Predicting the future motion of surrounding road users is a crucial and challenging task for
autonomous driving (AD) and various advanced driver-assistance systems (ADAS) …
autonomous driving (AD) and various advanced driver-assistance systems (ADAS) …
Spatiotemporal attention-based graph convolution network for segment-level traffic prediction
Traffic prediction, as a core component of intelligent transportation systems (ITS), has been
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …
investigated thoroughly in the literature. Nevertheless, timely accurate traffic prediction still …