MRGTraj: A Novel Non-Autoregressive Approach for Human Trajectory Prediction

Forecasting human trajectory is an essential technology in intelligent surveillance systems, robot navigation systems, autonomous driving systems, etc. Most of the trajectory prediction models based on RNN and Transformers use autoregressive methods to generate future trajectories, which may accumulate displacement errors and are inefficient for training and testing. To address these problems, we propose a novel decoder named MRG decoder, which introduces a Mapping-Refinement-Generation structure to generate trajectory in a non-autoregressive manner. Furthermore, we design the MRGTraj trajectory prediction model based on the proposed MRG decoder. Firstly, we employ a Transformer as an encoder to extract encoded features from the past trajectory. Secondly, we introduce an interaction-aware latent code generator to learn a Gaussian distribution from the social context among pedestrians for latent code sampling. Finally, we feed the encoded features to the MRG decoder and sample the latent code multiple times from the learned Gaussian distribution, providing additional inputs to the MRG decoder to generate multiple socially acceptable future trajectories. Experimental results on two public datasets, ETH and UCY, validate the effectiveness of the MRGTraj model. Besides, the MRGTraj model achieves superior prediction performance, with improvements of 13.21% on FDE metrics and a 71.29% speed-up compared to state-of-the-art models. The code is available at https://github.com/wisionpeng/MRGTraj .