1 research outputs found
A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction
It remains challenging to automatically predict the multi-agent trajectory
due to multiple interactions including agent to agent interaction and scene to
agent interaction. Although recent methods have achieved promising performance,
most of them just consider spatial influence of the interactions and ignore the
fact that temporal influence always accompanies spatial influence. Moreover,
those methods based on scene information always require extra segmented scene
images to generate multiple socially acceptable trajectories. To solve these
limitations, we propose a novel model named spatial-temporal attentive network
with spatial continuity (STAN-SC). First, spatial-temporal attention mechanism
is presented to explore the most useful and important information. Second, we
conduct a joint feature sequence based on the sequence and instant state
information to make the generative trajectories keep spatial continuity.
Experiments are performed on the two widely used ETH-UCY datasets and
demonstrate that the proposed model achieves state-of-the-art prediction
accuracy and handles more complex scenarios.Comment: bad model settings and unclear result