A team of researchers from Intel Labs and Darmstadt University in Germany has developed a clever way to extract useful training data from Grand Theft Auto. The researchers created a software layer that sits between the game and a computer’s hardware, automatically classifying different objects in the road scenes shown in the game. This provides the labels that can then be fed to a machine-learning algorithm, allowing it to recognize cars, pedestrians, and other objects shown, either in the game or on a real street. According to a paper posted by the team recently, it would be nearly impossible to have people label all of the scenes with similar detail manually. The researchers also say that real training images can be improved with the addition of some synthetic imagery.