Social-Scene-Aware Generative Adversarial Networks for Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction is crucial across a wide range of applications like self-driving vehicles and social robots. Such prediction is challenging because crowd behavior is inherently determined by various factors, such as obstacles, stationary crowd groups and destinations which were difficult to effectively represent. Especially pedestrians tend to be greatly affected by the pedestrians in front of them more than those behind them, which were often ignored in literature. In this paper, we propose a novel framework of Social-Scene-Aware Generative Adversarial Networks (SSA-GAN), which includes three modules, to predict the future trajectory of pedestrians in dynamic scene. Specifically, in the Scene module, we model the original scene image into a scene energy map by combining various scene factors and calculating the probability of pedestrians passing at each location. And the modeling formula is inspired by the distance relationship between pedestrians and scene factors. Moreover, the Social module is used to aggregate neighbors’ interactions on the basis of the correlation between the motion history of pedestrians. This correlation is captured by the self-attention pooling module and limited by the field of view. And then the Generative Adversarial module with variety loss can solve the multimodal problem of pedestrian trajectory. Extensive experiments on publicly available datasets validate the effectiveness of our method for crowd behavior understanding and trajectory prediction.