Learn how to boost your deep learning training process by utilizing features of a driving simulation. Besides a customizable source of video camera input, enhanced driving simulations can also provide information from non-visual sensors like lidar, radar, or ultrasound simultaneously. Train deep learning algorithms with visual, non-visual, or intermediate data like point clouds, bounding boxes, or object lists. Instead of labeling real videos by hand, use the information of the simulation to feedback and correct the results of your neural network. Run your simulation in faster than real time for distributed headless simulations or trigger every frame of the simulation to capture data for further processing. Embed your algorithms within the simulation (software in the loop) and test your AI in unusual situations, which are too risky in reality. Artificial reality? Not perfect, but a perfect complement in developing AI algorithms for autonomous driving.