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.

翻译：无人地面车辆（UGVs）在非结构化环境中主要通过遥操作运行。为实现在非结构化环境中的稳定遥操作驾驶，部分研究提出了涉及用户实验的驾驶员辅助与评估方法，但这类方法成本高昂且需耗费大量时间和精力。已有研究提出基于仿真模型的方法来补充用户实验，然而现有遥操作驾驶模型并未考虑非结构化环境。我们的解决方案采用基于深度生成模型的遥操作驾驶仿真模型。首先，基于前期研究构建用于模拟非结构化环境的遥操作驾驶模拟器，并采集驾驶员的行驶数据；随后，设计并实现基于条件变分自编码器（CVAE）的仿真模型。评估结果表明，所提出的遥操作驾驶模型能够在非结构化峡谷地形中通过模拟驾驶员行为生成合理数据。