HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention

arXiv:2404.06351v1 Announce Type: new
Abstract: Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps, which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames, their forecasting should have intrinsic correlation, such as overlapping predicted trajectories should be consistent, …

arxiv attention cs.cv dynamic forecasting prediction trajectory type