Object and Obstacle Detection for Self-Driving Cars Using GoogLeNet and Deep Learning

Abstract

Self-driving cars are the latest innovation in which the car runs by itself. The self-driving cars can be called as autonomous cars. This involves many technologies like artificial intelligence, machine learning and deep learning. When coming to the self-driving cars, the main aspect which it needed to be taken care of is the obstacle detection and the object detection. The object detection by a car in more simpler words object recognition process done by a machine which involves the concepts of machine learning and deep learning. Deep learning helps in achieving the object and the obstacle detection. There are various algorithms which help in the object detection like artificial neural network, convolutional neural network, AlexNet, VGG Net, GoogleNet, etc. GoogleNet is the CNN architecture which makes the image recognition an easier task. For the self-driving cars, obstacle and object detection GoogLeNet are not much addressed in the recent works. So, it can be considered as a latest technology. In this paper, the recent works about the self-driving cars and object detection and obstacle detection and the future scope of it are discussed.