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 …
Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)
The traffic state in an urban transportation network is determined via spatio-temporal traffic
propagation. In early traffic forecasting studies, time-series models were adopted to …
propagation. In early traffic forecasting studies, time-series models were adopted to …
STGMN: A gated multi-graph convolutional network framework for traffic flow prediction
Q Ni, M Zhang - Applied Intelligence, 2022 - Springer
Accurate traffic flow prediction is crucial for the development of intelligent transportation. It
can not only effectively avoid traffic congestion and other traffic problems, but also provide a …
can not only effectively avoid traffic congestion and other traffic problems, but also provide a …
Semantics-aware dynamic graph convolutional network for traffic flow forecasting
Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …
stochastic features underlying complex traffic situations. Currently, Graph Convolutional …
Multi-range attentive bicomponent graph convolutional network for traffic forecasting
Traffic forecasting is of great importance to transportation management and public safety,
and very challenging due to the complicated spatial-temporal dependency and essential …
and very challenging due to the complicated spatial-temporal dependency and essential …
ST-LSTM: Spatio-temporal graph based long short-term memory network for vehicle trajectory prediction
G Chen, L Hu, Q Zhang, Z Ren, X Gao… - … Conference on Image …, 2020 - ieeexplore.ieee.org
Autonomous vehicles need the ability to predict the trajectory of surrounding vehicles, so as
to make a rational decision planning, improve driving safety and ride comfort. In this paper, a …
to make a rational decision planning, improve driving safety and ride comfort. In this paper, a …
Graph representation learning in the ITS: Car-following informed spatiotemporal network for vehicle trajectory predictions
YH Yin, X Lü, SK Li, LX Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Multimodal synchronization has become the research highlight of the ITS, where complex
driving scenarios, various types of vehicles and diverse data sources are crucial …
driving scenarios, various types of vehicles and diverse data sources are crucial …
Graph attention network with spatial-temporal clustering for traffic flow forecasting in intelligent transportation system
With the development of the Internet of Things (IoT) and 5G technologies, IoT devices
deployed on roads are able to collect a large amount of traffic data at any time. Road …
deployed on roads are able to collect a large amount of traffic data at any time. Road …
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due
to the time-varying traffic patterns and the complicated spatial dependencies on road …
to the time-varying traffic patterns and the complicated spatial dependencies on road …