SCSG Attention: A Self-centered Star Graph with Attention for Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction enables faster progress in autonomous driving and robot navigation where complex social and environmental interactions involve. Previous models use grid-based pooling or global attention to measure social interactions and use Recurrent Neural Network (RNN) to generate sequences. However, these methods can not extract latent features from temporal and spatial information simultaneously. To address the limitation of previous work, we propose a Self-Centered Star Graph with Attention (SCSG Attention) framework. Firstly, pedestrians’ historical trajectories are encoded. Then multi-head attention mechanism plays a role as enhancement of social interaction awareness and simulation of physical attention from human beings. Lastly, the self-centered star graph decoder can aggregate temporal and spatial features and make predictions. Experiments are conducted on public benchmark datasets and measured with uniform standards. Our results show an improvement over the state-of-the-art algorithms by \(38\%\) on average displacement error (ADE) and \(19\%\) on final displacement error (FDE). Furthermore, it is demonstrated that the star graph has better performance in efficiency of training convergence and ends up with better results in limited time.