VNAGT: Variational Non-Autoregressive Graph Transformer Network for Multi-Agent Trajectory Prediction

Abstract

Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging because the movement patterns of agents are complex and stochastic, not only depending on their own past trajectories but also being closely related to the social interaction with other surrounding agents. Besides accuracy, efficient prediction with low inference latency is also a highly desirable feature for the practical application of trajectory prediction. To address these issues, we propose the VNAGT model, a variational non-autoregressive graph transformer that adopts the framework of conditional variational autoencoder and incorporates the non-autoregressive approach such that diverse trajectories and low prediction latency can be achieved simultaneously. In order to capture the social and temporal interaction, we put forward a unified graph attention-based module that is applicable for homogeneous and heterogeneous multi-agents such that the class information can be seamlessly integrated when it is available. Non-autoregressive decoding is combined with variational learning to produce multiple plausible predictions with low latency. We train and validate the model on two real-world homogeneous and heterogeneous trajectory datasets. The experimental results demonstrate the superior performance of our model in comparison with the state-of-the-art methods.