STF: Spatial Temporal Fusion for Trajectory Prediction
Pengqian Han Partha Roop Jiamou Liu Tianzhe Bao Yifei Wang
Trajectory prediction is a challenging task that aims to predict the future
trajectory of vehicles or pedestrians over a short time horizon based on their
historical positions. The main reason is that the trajectory is a kind of
complex data, including spatial and temporal information, which is crucial for
accurate prediction. Intuitively, the more information the model can capture,
the more precise the future trajectory can be predicted. However, previous
works based on deep learning methods processed spatial and temporal information
separately, leading to inadequate spatial information capture, which means they
failed to capture the complete spatial information. Therefore, it is of
significance to capture information more fully and effectively on vehicle
interactions. In this study, we introduced an integrated 3D graph that
incorporates both spatial and temporal edges. Based on this, we proposed the
integrated 3D graph, which considers the cross-time interaction information. In
specific, we design a Spatial-Temporal Fusion (STF) model including Multi-layer
perceptions (MLP) and Graph Attention (GAT) to capture the spatial and temporal
information historical trajectories simultaneously on the 3D graph. Our
experiment on the ApolloScape Trajectory Datasets shows that the proposed STF
outperforms several baseline methods, especially on the long-time-horizon
trajectory prediction.