Vehicle Trajectory Prediction Based on GAT and LSTM Networks in Urban Environments

Xuelong ZHENG; Xuemei CHEN; Yaohan JIA

doi:10.7307/ptt.v36i5.600

Authors

Xuelong ZHENG Beijing Institute of Technology, School of Mechanical Engineering
Xuemei CHEN Beijing Institute of Technology, Advanced Technology Research Institute; Beijing Institute of Technology, School of Mechanical Engineering
Yaohan JIA Beijing Institute of Technology, School of Mechanical Engineering

DOI:

https://doi.org/10.7307/ptt.v36i5.600

Keywords:

autonomous vehicle, trajectory prediction, hierarchical, long short-term memory network, graph attention network

Abstract

Vehicle trajectory prediction plays a critical role before the decision planning of autonomous vehicles in complex and dynamic traffic environments. It helps autonomous vehicles better understand the traffic environments and ensure safe and efficient tasks. In this study, a hierarchical trajectory prediction method is proposed. The graph attention network (GAT) model was selected to estimate the interactions of surrounding vehicles. Considering the behaviour of surrounding agents, the future trajectory of the target vehicle is predicted based on the long short-term memory network (LSTM). The model has been validated in real traffic environments. By comparing the accuracy and real-time performance of target vehicle trajectory prediction, the proposed model is superior to the traditional single trajectory prediction model. The results of this study will provide new modelling ideas and a theoretical basis for the vehicle trajectory prediction in urban traffic environments.

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