In this paper a noise gradient strategy on the Adam optimizer is introduced, in order to reduce the training time of our enhanced Chauffeur hybrid deep model. This neural network was modified to take into account the time dependence of the input visual information from a time-distributed convolution, with the aim of increasing the autonomy of a self-driving vehicle. The effectiveness of the proposed optimizer and model was evaluated and quantified during training and validation with a higher performance than the original Chauffeur model in combination with the comparative optimizers. In terms of the autonomy, it can be seen that our enhanced Hybrid Convolutional-Recurrent Deep Network was better trained, achieving autonomy greater than 95% with a minimum number of human interventions.

本文介绍了一种基于Adam优化器的噪声梯度策略，以减少改进型Chauffeur混合深度模型的训练时间。对该神经网络进行了修改，以考虑到来自时间分布卷积的输入视觉信息的时间依赖性，目的是增加自动驾驶车辆的自主性。与原始的司机模型结合比较优化器相比，在培训和验证过程中评估和量化了所提出的优化器和模型的有效性。在自治方面，可以看出我们增强型混合卷积-循环深度网络得到了更好的培训，在最少的人工干预下，自治度达到了95％以上。