STS-GAN: Spatial-Temporal Attention Guided Social GAN for Vehicle Trajectory Prediction

Abstract

Abstract Accurately predicting the trajectories of other vehicles is crucial for autonomous driving to ensure driving safety and efficiency. Recently, deep learning techniques have been extensively employed for trajectory prediction, resulting in significant advancements in predictive accuracy. However, existing studies often fail to explicitly distinguish the impact of historical inputs at different time steps and the influence of surrounding vehicles at distinct locations. Moreover, deep learning-based approaches generally lack model interpretation. To overcome the issues, we propose the Spatial-Temporal Attention Guided Social GAN (STS-GAN). In the generator, we proposed a spatial-temporal attention mechanism to guide the utilization of trajectory features and interaction of the target vehicle with its surrounding vehicles. The spatial attention mechanism evaluates the importance of surrounding vehicles for predictions of the target vehicle, while the temporal attention mechanism learns the significance of historical trajectory information at different historical time steps, thereby enhancing the model interpretation. A convolutional social pooling module is employed to capture interaction features from surrounding vehicles, which are subsequently fused with the attributes of the target vehicle. Experimental results demonstrate that our model achieves competitive performance compared with state-of-the-art methods on publicly available datasets.