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