Autonomous cars are increasingly utilizing artificial intelligence in their systems. The problem of collision avoidance for autonomous cars can be approached using reinforcement learning (RL). In this thesis we look at two approaches to RL; one where an RL agent learns a direct mapping from observations to actions called model-free RL, and one where an RL agent learns to act by using a separate, learned predictive model of the environment called model-based RL. Both model-based and model-free RL for collision avoidance has been researched and shown to be useful and effective solutions to the problem of collision avoidance. However, research comparing these two methods for collision avoidance in a controlled and systematic manner seems to be lacking. In this thesis, the costs and benefits of model-free reinforcement learning versus model-based reinforcement learning for predicting and avoiding collisions in traffic situations are investigated.