Convolution Neural Networks (CNN) have proven themselves to be a very powerful identifier of road features in self-driving automotive technologies. A team of engineers trained CNN to detect road types and roadside features. With the help of various datasets used for such a training, the model teaches driving skills to an automobile similar to how a toddler learns how to walk.

This article describes how the CNN model can be trained and integrated within an existing Driver Assessor System. The essence of this model along with its improved version, VGGNet, are also described for greater understanding of this relatively unknown topic. The usage of VGGNet provides a huge boost in the accuracy of predictions specifically when in-cabin driver facing cameras are to be considered. As embedded systems, driver assessor systems come with limited computational capabilities. The engineering team undertook the mission to develop CNN using real world data against the model and existing Driver Assessor System and the consequent outcome is published in the conclusion section of this article. The paper also describes the model integration of CNN and training process optimization, while emphasizing the reusability of neural networks.