Autonomous driving is the future of automotive. The most important challenge in autonomous driving applications is represented by safety. An autonomous driving system to be perceived safe has to be trustful in emergency situations. To recognize an emergency situation and act immediately to avoid collisions is one of the most important features of an autonomous driving system. This thesis work investigated the topic of collision avoidance in emergency situation in autonomous driving using deep reinforcement learning. According to the literature related to this problem, two kinds of ADAS controls were studied. The studied controls were: Autonomous Emergency Steering and Autonomous Emergency Braking.