Self-Driving Car Using Simulator

Abstract

Due to rapid technological growth in transportation, self-driving cars became the topic of concern. The main purpose of this project is to use the CNN and train the neural network in order to drive the car in autonomous mode in a simulator environment. Front camera of a car captures the images and we use those captured images in order to train the model, in short we can say we have used the concept of behavioural cloning. In behavioural cloning, the system tries to mimic the human driving behaviour by tracking the steering angle. That means a dataset is generated in the simulator by a user driven car in training mode, and the deep neural network model then drives the car in autonomous mode. In one track a car is trained and in other tracks the car drives in autonomous mode. The dataset for Track 1, which was straightforward to drive and had good road conditions, was utilized as the training set for the automobile to drive itself on Track 2, which has abrupt curves, barriers, heights, and shadows are all things to consider. Image processing and other augmentation techniques were utilized to solve this difficulty, allowing for the extraction of as many data and features as feasible. In the end, the vehicle performed admirably on Track 2. In the future, the team hopes to achieve the same level of accuracy using real-time data.