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：由于自主驾驶技术的局限性,远程操作在军事行动等危险环境中广泛使用,然而,远程操作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和恶劣环境中的远程操作需要大量培训时间。为解决这一问题,我们提议采用新型的基于降职的驾驶员援助方法,即GoonDAE,用于实时远程越野驾驶。非熟练驾驶员控制输入假定与熟练驾驶员控制输入相同,但有噪音。我们设计了一个跳过连接的长期短期内存(LSTM)基于调试自动驾驶员(DAE)模型,以协助非熟练驾驶员在异常和恶劣环境中进行控制输入。拟议的GoonDAE经过熟练驾驶员控制输入和从我们模拟越野驾驶环境中收集的感官数据的培训。为了评价GoonDAE,我们在模拟环境中与非熟练驾驶员进行了试验。结果显示,拟议的系统大大提高了驾驶员在驾驶稳定性方面的性能。</s>
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