Behavior Cloning for Autonomous Driving using Convolutional Neural Networks

In this paper, we propose using a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering maneuvering as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. This data is then used to train the proposed CNN to facilitate what we call it behavioral cloning. The proposed Behavior Cloning CNN is named as “BCNet” and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam's optimization algorithm as a variant of the Scholastic Gradient Descent (SGD) technique. The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.