Data augmentation in autonomous driving simulation, also called scenario fuzzing or scenario variation, is a method that creates small variations of the input data in a simulated environment. See an example of two augmented vehicles added to the camera recording & LiDAR point cloud in an autonomous driving scenario simulation. To cope with the infinite possibilities of real-world traffic situations, augmentation enlarges the amount and variety of data that can be used to train and test self-driving AI.

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