Spatio-Temporal Interaction Aware and Trajectory Distribution Aware Graph Convolution Network for Pedestrian Multimodal Trajectory Prediction

Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. To tackle these challenges, we propose a spatio-temporal interaction aware and trajectory distribution aware graph convolution network. First, we propose a spatio-temporal interaction aware module that integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions among pedestrians. Second, we design a trajectory distribution aware module to learn latent trajectory distribution information from the measured trajectories at observed and future times. This can provide knowledge-rich trajectory distribution information for the multimodality of the predicted trajectories. Finally, to address the problem of the propagation and accumulation of prediction errors, we design a trajectory decoder to generate the multimodal future trajectories. The proposed model is evaluated utilizing videos recorded by a camera sensor in crowded areas and can be applied to predict multiple pedestrians' future trajectories from in-vehicle cameras. Experimental results demonstrate that the proposed approach can achieve superior results on the average displacement error (ADE) and final displacement error (FDE) metrics to state-of-the-art approaches and can predict socially acceptable future trajectories.

Automated summary

Pedestrian trajectory prediction is an important research area that plays a crucial role in blind navigation, autonomous driving systems, and service robots. This field faces two challenges: modeling spatio-temporal interactions among pedestrians and dealing with the uncertainty of pedestrian trajectories. To address these challenges, we propose a graph convolution network that is aware of spatio-temporal interactions and trajectory distribution. Our model integrates a graph convolutional network and self-attention mechanism to model spatio-temporal interactions, and learns latent trajectory distribution information from measured trajectories at observed and future times. Experimental results demonstrate the effectiveness of our approach in predicting socially acceptable future trajectories.