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The majority of road accidents occur because of human errors, includingdistraction, recklessness, and drunken driving. One of the effective ways toovercome this dangerous situation is by implementing self-driving technologiesin vehicles. In this paper, we focus on building an efficient deep-learningmodel for self-driving cars. We propose a new and effective convolutionalneural network model called `LaksNet' consisting of four convolutional layersand two fully connected layers. We conduct extensive experiments using ourLaksNet model with the training data generated from the Udacity simulator. Ourmodel outperforms many existing pre-trained ImageNet and NVIDIA models in termsof the duration of the car for which it drives without going off the track onthe simulator.

LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator