A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction

TL;DR

This paper introduces STAN-SC, a novel spatial-temporal attentive network that effectively models both spatial and temporal influences for multi-agent trajectory prediction, improving accuracy without relying on scene segmentation.

Contribution

The paper proposes a new spatial-temporal attention mechanism and joint feature sequence modeling to enhance trajectory prediction accuracy and handle complex scenarios.

Findings

Achieves state-of-the-art accuracy on ETH-UCY datasets.

Effectively models spatial and temporal influences.

Handles complex multi-agent scenarios.

Abstract

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.