It is highly evident that autonomous vehicles will be the future and it will be a prominent vehicle category in the next decade. For this to be a success, the vehicle should be safe, reliable and provide a comfortable user experience. Autonomous driving must have sophisticated negotiating skills while taking right, left turns and while pushing ahead in urban areas. Reinforcement learning is considered as the main domain for learning driving policy. We propose a reinforcement learning approach using deep Q-learning approach which will extract the maximum reward from a large state space. We use CARLA, an open-source simulator for autonomous driving research. The outcome of this experiment is to resemble a real-life environment where the agent tries to overcome the obstacles using the data from the virtual sensors attached to the agent.