Nowadays, the technological Artificial intelligence trend is growing rapidly in the world. This paper can help one understand trends in autonomous vehicle technology and clear your thoughts on self-driving car. There are many different approaches to create a self-driving car, Deep Q-learning, Q-learning, and through machine learning also. However, our idea is very basic and simple when comparing with advance concepts. We preferred to create a simple small environment to make a simulation of our self-driving car model. The environment was provided by OpenAi gym which is a deterministic environment. OpenAi gym provided us the virtual simulation environment as well as the parameters needed to train and test the model. Wehave focused on two methods to test our model. The basic approach was to compare the performance of the car when tested using Q-Learning and when tested using Random Action, i.e., No rein-forcement learning. We have derived a theoretical model and analyzed on how to use Q-learning to teach car to drive. We have carried out simulation and on evaluating the performance, we found that Q-learning is a more optimal approach to solve the issue of self-driving car