Autonomous driving is a popular and promising field in artificial intelligence. Rapid decision of the next action according to the latest few actions and status, such as acceleration, brake, and steering angle, is a major concern for autonomous driving. There are some learning methods, such as reinforcement learning which automatically learns the decision. However, it usually requires large volume of samples. In this paper, to reduce the sample size, we exploit the deep Gaussian process, where a regression model is trained on small sample datasets and captures the most significant features correctly. Besides, to realize the real-time and close-loop control, we combine the feedback control into the process. Experimental results on the Torcs simulation engine illustrate smooth driving on virtual road which can be achieved. Compared with the amount of training data in deep reinforcement learning, our method uses only 0.34% of its size and obtains similar simulation results. It may be useful for real road tests in the future.

中文翻译：

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学习使用小数据进行自动驾驶决策：深度高斯过程和反馈控制

自动驾驶是人工智能领域一个流行且有前途的领域。根据最新的一些动作和状态（例如加速，制动和转向角）快速决定下一个动作是自动驾驶的主要考虑因素。有一些学习方法，例如强化学习可以自动学习决策。但是，通常需要大量样品。在本文中，为了减少样本量，我们利用了深度高斯过程，其中在小的样本数据集上训练了回归模型，并正确地捕获了最重要的特征。此外，为了实现实时和闭环控制，我们将反馈控制结合到过程中。Torcs仿真引擎上的实验结果表明，可以在虚拟道路上平稳行驶。与深度强化学习中的训练数据量相比，我们的方法仅使用其大小的0.34％并获得相似的模拟结果。将来可能对实际的路试有用。