Deep Neural Networks (DNNs) which are trained end-to-end have been successfully applied to solve complex problems that we have not been able to solve in past decades. Autonomous driving is one of the most complex problems which is yet to be completely solved and autonomous racing adds more complexity and exciting challenges to this problem. Towards the challenge of applying end-to-end learning to autonomous racing, this paper shows results on two aspects: (1) Analyzing the relationship between the driving data used for training and the maximum speed at which the DNN can be successfully applied for predicting steering angle, (2) Neural network architecture and training methodology for learning steering and throttle without any feedback or recurrent connections.

中文翻译：

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走向端到端的自动赛车深度学习：关于数据收集和转向和节气门预测的统一体系结构

经过端到端训练的深度神经网络（DNN）已成功应用于解决过去几十年来我们无法解决的复杂问题。无人驾驶是最复杂的问题之一，有待彻底解决，而无人驾驶赛车给这一问题增加了更多的复杂性和令人兴奋的挑战。针对将端到端学习应用于自动赛车的挑战，本文显示了两个方面的结果：（1）分析用于训练的驾驶数据与DNN可以成功应用于预测的最大速度之间的关系。转向角，（2）用于学习转向和油门的神经网络体系结构和训练方法，无需任何反馈或循环连接。