Yurtsever said driving simulations will continue to need conventional, labor-intensive graphics rendering techniques to display the primary objects of interest, such as nearby cars.

They then used a GAN-based image synthesizer to render the background elements like buildings, vegetation and even the sky, and combine them with more traditionally rendered objects. The researchers started with CARLA, an open-source driving simulator, as their base. The study was published in the journal IEEE Transactions on Intelligent Transportation Systems. 'That's why we want to make our simulations look as similar to the real world as possible.' 'When driving simulations look like computer games, most people don't take them seriously,' said Ekim Yurtsever, lead author of the study and a research associate of electrical and computer engineering at The Ohio State University.

This is especially important when testing how humans react when they are in driverless vehicles or, alternatively, on the road with them. Yet by using a machine learning framework called a Generative Adversarial Network (GAN), researchers were able to train their program to randomly generate life-like environments by improving the program's visual fidelity - the level of representation computer graphics share with reality. Conventional computer graphics use detailed models, meshes and textures to render 2D images from 3D scenes, a labor-intensive process which produces images that often fall short of being realistic, particularly in the background.

2d driving simulator games