Graph Convolution Network Combining Spatiotemporal Self-Attention and Mutual Information for Pedestrian-Trajectory Prediction

Accurate pedestrian-trajectory prediction is significant and challenging for intelligent driving. There are two challenges in this field: most existing methods ignore pedestrian–vehicle interaction in the multiple pedestrian and vehicle scenario; and research on interaction modes and objects during the interaction process remains relatively limited in depth and scope. To tackle these challenges, a novel graph convolution network combining spatiotemporal self-attention and mutual information (STSAMI-GCN) is designed for predicting pedestrian trajectories. First, to model the spatiotemporal interaction between pedestrians and vehicles, we build a spatiotemporal graph interaction module that integrates graph convolutional networks and self-attention mechanisms. Second, to filter redundant interactions and optimize feature extraction in the spatiotemporal graph interaction module, a mutual information graph network module is designed as an auxiliary network, which can reduce information redundancy and simplify graph complexity. In addition, to address the problem of propagation and accumulation of prediction errors, we design a trajectory decoder to generate future trajectories. Finally, a suitable dataset for our research scenario is built to validate the proposed model. We also evaluate it on two publicly available datasets. Experimental results show that, in the multiple pedestrian and vehicle scenario, STSAMI-GCN can be used to predict the future trajectories of multiple pedestrians, and to predict socially acceptable future trajectories. Specifically, STSAMI-GCN achieves 0.25 and 0.27 average and final deviation error, respectively, and outperforms various mainstream methods. Furthermore, ablation studies demonstrate the effectiveness of the spatiotemporal dual self-attention mechanism and mutual information graph network in improving pedestrian-trajectory prediction accuracy.