Multimodal Pedestrian Trajectory Prediction Using Probabilistic Proposal Network

Weihuang Chen, Zhigang Yang, Lingyang Xue, Jinghai Duan, Hongbin Sun, Nanning Zheng

Abstract: Forecasting multiple pedestrian trajectories is a challenging task for real-world applications, as the motion patterns of pedestrian are essentially stochastic and uncertain. Previous works have demonstrated that predicting diverse goals in advance can effectively improve the performance of pedestrian trajectory prediction. However, these methods are either unable to perform probabilistic and high-efficiency trajectory prediction, or mainly rely on the predefined template trajectories which are not high-performance and insufficient to represent the possible pedestrian behaviors. In this paper, we propose a new Probabilistic Proposal Network (PPNet) to concentrate on the generation of goals and the utilization of goal guidance. PPNet firstly generates multiple weighted goals based on the diverse latent intentions automatically obtained by unsupervised learning, and then designs the goal-conditioned Transformer networks to predict probabilistic proposals as the final trajectories. Extensive experimental results on ETH/UCY datasets and Stanford Drone Dataset indicate that PPNet achieves both state-of-the-art performance and high efficiency on pedestrian trajectory prediction.