'Grand Theft Auto' Is Teaching AI How to Drive

And hopefully not much else.

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A team of researchers is using Grand Theft Auto V to teach artificial intelligence algorithms how to drive.

Why use a game instead of something more real? It comes down to the trouble of making a convincing world for the AI to mess around in. As the team from Intel and Darmstadt University in Germany writes in their paper's abstract, "creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required."

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Video games, on the other hand, are already made and just waiting to be put to use. As the paper puts it:

Their realism is not only in the high fidelity of material
appearance and light transport simulation. It is also in the content of the game worlds:
the layout of objects and environments, the realistic textures, the motion of vehicles and
autonomous characters, the presence of small objects that add detail, and the interaction
between the player and the environment.

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Although GTA provides an extremely fleshed-out world—one that has inspired not one but two nature documentaries—it doesn't offer an immediate way to transfer data to AIs and vice-versa. So using a moment in the game's loading process, the researchers would essentially pose as the game's graphic library and inject additional software into the game, code that labels to all the items one sees driving the roads in GTA, including cars, pedestrians, signs, bicyclists, sidewalks, trees, poles, fences, and even the sky. The researchers were then able to track how the AI interacted with all of the objects.


Video games offer a chance to take AIs through through various scenarios that would be expensive and unwieldy to perform in the real world, like scenarios that might involve almost hitting a pedestrian. Having tracked how the AI did with various classes of items, the researchers are now preparing to cook up some specific scenarios for the AI to handle. Hopefully it won't learn how to drive the way a normal human plays GTA.

Source: MIT Technology Review

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