Tra2Tra: Trajectory-to-Trajectory Prediction With a Global Social Spatial-Temporal Attentive Neural Network

Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning a collision-free and appropriate path for the autonomous robots, especially in crowded scenes. However, it is a particularly challenging task because there are complex and subtle interactions among pedestrians. There have been many studies focusing on how to model this spatial interactions but most of them neglected the temporal characteristic. Towards this end, we propose a novel Global Social Spatial-Temporal Attentive Neural Network for trajectory-to-trajectory prediction (Tra2Tra). In this model, we first extract features of spatial interactions with decentralization operation and attention mechanism, and then iteratively extract its temporal dependency through the Long Short-Term Memory network for obtaining the global spatial-temporal feature representation. We further aggregate this global spatial-temporal feature representation and velocity features into our encoder-decoder module for prediction. In order to make multi-modality predictions, we introduce a random noise perturbation while decoding, which enhances the robustness and the generalization ability of our model. Experimental results demonstrate that our Tra2Tra model can achieve better performance than the state-of-the-art methods not only on two pedestrian-walking datasets, i.e. ETH and UCY, but also on three other complex trajectory datasets, i.e. Collisions, NGsim and Charges.

Automated summary

Accurate trajectory prediction is crucial for robot navigation, especially in crowded scenes. The proposed Global Social Spatial-Temporal Attentive Neural Network combines spatial interaction features and temporal dependency to achieve better performance on various datasets.