Because of the limitations of autonomous driving technologies, teleoperation is widely used in dangerous environments such as military operations. However, the teleoperated driving performance depends considerably on the driver's skill level. Moreover, unskilled drivers need extensive training time for teleoperations in unusual and harsh environments. To address this problem, we propose a novel denoising-based driver assistance method, namely GoonDAE, for real-time teleoperated off-road driving. The unskilled driver control input is assumed to be the same as the skilled driver control input but with noise. We designed a skip-connected long short-term memory (LSTM)-based denoising autoencoder (DAE) model to assist the unskilled driver control input by denoising. The proposed GoonDAE was trained with skilled driver control input and sensor data collected from our simulated off-road driving environment. To evaluate GoonDAE, we conducted an experiment with unskilled drivers in the simulated environment. The results revealed that the proposed system considerably enhanced driving performance in terms of driving stability.

翻译：由于自动驾驶技术的局限性，远程操作广泛用于军事行动等危险环境。然而，远程驾驶的性能在很大程度上取决于驾驶员的技能水平。此外，非熟练驾驶员在异常恶劣环境中需要大量的远程操作训练时间。为解决这一问题，我们提出一种新颖的基于去噪的驾驶辅助方法，即GoonDAE，用于实时远程越野驾驶。假设非熟练驾驶员的控制输入与熟练驾驶员相同但带有噪声。我们设计了一种基于跳跃连接的长短期记忆网络（LSTM）去噪自编码器（DAE）模型，通过去噪来辅助非熟练驾驶员的控制输入。所提出的GoonDAE使用从模拟越野驾驶环境采集的熟练驾驶员控制输入及传感器数据进行训练。为评估GoonDAE，我们让非熟练驾驶员在模拟环境中进行了实验。结果表明，所提出的系统在驾驶稳定性方面显著提升了驾驶性能。