Noise gradient strategy for an enhanced hybrid convolutional-recurrent deep network to control a self-driving vehicle

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.