Forward to the Future: A Deep Learning-based Approach to Mitigate Control Transmission Delay of Teleoperated Driving in Unstructured Environments

Abstract

Teleoperated driving is a leading approach for operating unmanned ground vehicles (UGVs) in unstructured environments, where driving stability is crucial. However, time delays may compromise this stability. In this letter, we propose a computational delay compensation method based on deep learning models to address the control transmission delay. Initially, we collect teleoperated driving data from simulated unstructured environments and then design a delay compensation method utilizing time-series forecasting models. This method generates future control inputs equivalent to the delayed time steps. Our evaluation demonstrates the possibility of our delay compensation method for teleoperated driving in unstructured environments.