Autonomous Vehicles are the future of road transportation where they can increase safety, efficiency, and productivity. In this thesis, we address a new edge case in autonomous driving when one autonomous vehicle is approached by an emergency vehicle and needs to make the best decision. To achieve the desired behavior and learn the sequence decision process, we trained our autonomous vehicle with the help of Deep Reinforcement Learning algorithms and compared the results with rule-based algorithms. The driving environment for this study was developed by using Simulation Urban Mobility as an open-source traffic simulator. The proposed solution based on Deep Reinforcement Learning has a better performance compared to the rule-based solution as a baseline both in normal driving situations and when an emergency vehicle is approaching.