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.

翻译：经过培训的深神经网络(DNN)经过端对端培训后成功应用,解决了我们过去几十年未能解决的复杂问题。自主驾驶是尚未完全解决的最复杂问题之一,而自主驾驶则增加了这一问题的复杂性和令人兴奋的挑战。为了应对将端对端学习应用到自主赛的挑战,本文件展示了两个方面的结果:(1) 分析用于培训的驱动数据与DN可成功应用以预测方向的最大速度之间的关系;(2) 神经网络架构和培训方法,用于在没有任何反馈或经常性连接的情况下学习方向和节流。