Abstract:
There has been a lot of development going on in the field of self-driving technology these
days. The development in this field is due to the breakthrough advancement in deep
learning. Tasks that usually require human interaction are executed by training deep neural
networks. The Convolutional Neural Networks (CNNs) recognize image features and
patterns using different models allowing them to be fruitful. Various factors of what makes
a vehicle automated have been addressed in the world of automobiles.
In this project we used an end-to-end learning approach that is of training a Convolutional
Neural Network with the help of images for a basic adaption of self-driving car. Raw pixels
extracted from images obtained by a single front facing camera are mapped directly to the
steering commands by this trained CNN. The CNN has an ability to extract information
from images such as the patterns and features allowing the automobile to drive
autonomously.
We used AirSim simulator to generate our dataset along for the testing purposes. Moreover,
Unreal Engine has been used to create an unreal environment for generating a dataset
through which we can train our CNN We trained our model with different network
architectures such as NVIDIA, Alex Net and ResNet 101 with a technique of Transfer
Learning.
