Global Temporal Attention Optimization for Human Trajectory Prediction

Predicting human trajectory is one of the key knowledge required for autonomous driving and social robots in real scenarios. Recent studies based on Transformer networks have shown a great ability to model social behaviors. As far as we know, global trajectory information has an essential influence on prediction at a certain step. However, these methods only rely on the previous trajectory states/attention but ignore the important following states/attention of the trajectory for each pedestrian, which will generally collapse on some irregular movements (e.g. acceleration, deceleration, and motionless). To solve this issue, we propose a Global Temporal Attention optimization model (GTAO), which activates the utilization of the following states/attention of the trajectory, and jointly and iteratively optimizes the preliminary trajectory prediction through a global temporal attention (GTA) module. To effectively address the decline in the generalizability and abnormal processing of the model, we further introduce global temporal guidance (GTG) module to instruct the GTA to learn the features closer to realistic trajectories. Experimental results on commonly used real-world human trajectory prediction datasets (ETH and UCY) indicate that our GTAO can achieve better performance in terms of prediction accuracy.