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.

本研究提出了一种集成的三维图形方法，将空间和时间信息融合在一起，并考虑跨时间的交互信息，以更准确地预测车辆轨迹。实验证明，该方法在长时间范围的轨迹预测中表现优于其他基线方法。

STF：空间时间融合的轨迹预测

STF: Spatial Temporal Fusion for Trajectory Prediction

Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.

该论文提出了一种将 2D 图像曲线组合成 3D 曲线集合，构建具有拓扑连接性的 3D 图形的方法，以实现对 3D 场景的结构化表示和重建。