Predict Multiple Steps at Once: A Fragment Trajectory Prediction Network

Trajectory prediction is a critical technique for autonomous driving, robot navigation and intelligent surveillance. The recurrent neural networks (RNNs) models propagate temporal information through hidden states and have been widely applied in adapting to varying input lengths and generating multi-modal trajectories. However, the high cost of computation and accumulated errors limit the development of RNN based models in the field of trajectory prediction. We suggest that RNN based models can encode or predict multiple time steps simultaneously in one iteration. In this paper, we propose a RNN based Fragment Trajectory Prediction Network (FTPN), which combines multiple time steps as a whole fragment for encoding and relationship modeling. Specifically, a proposed fragment encoder embeds the fragments equally split from a trajectory instead of time steps, and then uses a relationship estimator to get the neighbors' influence on moving targets based on prior knowledge. After fusing target and neighbors features, the model iteratively predicts future embeddings over fragment dimension, and generates multimodal trajectories through a multi style decoder. The results show that our model achieves state-of-the-art performance on ETH-UCY, SDD and NBA datasets. Additional ablation experiments demonstrate that our method can greatly reduce computation costs while improving model performance.