Before revision:  We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation . Even a relatively shallow convolutional neural network model was able to learn key driving behaviors from sparsely labelled datasets and was tolerant to environmental variations during deployment of the said driving behaviors .
After revision:  We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity .