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insight - Vehicle Trajectory Prediction
Physics Enhanced Residual Learning (PERL) Framework for Vehicle Trajectory Prediction
The PERL framework combines physics and neural networks to enhance vehicle trajectory prediction.
VT-Former: Vehicle Trajectory Prediction for Highway Surveillance
Enhancing roadway safety through innovative vehicle trajectory prediction.
VT-Former: An Innovative Approach to Vehicle Trajectory Prediction for Highway Surveillance
VT-Former combines transformers and graphs for accurate vehicle trajectory prediction in highway surveillance.
Graph Recurrent Attentive Neural Process Model for Efficient Vehicle Trajectory Prediction and Uncertainty Quantification
GRANP, a novel model combining Graph Attention Networks, LSTM, and Recurrent Attentive Neural Processes, can efficiently capture spatial-temporal relationships and quantify prediction uncertainties for vehicle trajectory forecasting.
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