  Unmanned ground vehicles (UGVs) in unstructured environments mostly operate
through teleoperation. To enable stable teleoperated driving in unstructured
environments, some research has suggested driver assistance and evaluation
methods that involve user studies, which can be costly and require lots of time
and effort. A simulation model-based approach has been proposed to complement
the user study; however, the models on teleoperated driving do not account for
unstructured environments. Our proposed solution involves simulation models of
teleoperated driving for drivers that utilize a deep generative model.
Initially, we build a teleoperated driving simulator to imitate unstructured
environments based on previous research and collect driving data from drivers.
Then, we design and implement the simulation models based on a conditional
variational autoencoder (CVAE). Our evaluation results demonstrate that the
proposed teleoperated driving model can generate data by simulating the driver
appropriately in unstructured canyon terrains.
