General Laboratory

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To date, self-driving data made available to the research community have primarily consisted of troves of static, single images that can be used to identify and track common objects found in and around the road, such as bicycles, pedestrians or traffic lights through the use of “bounding boxes.” By contrast, DriveSeg contains more precise, pixel-level representations of many of these same common road objects, but through the lens of a continuous video driving scene. This type of full scene segmentation can be particularly helpful for identifying more amorphous objects – such as road construction and vegetation – that do not always have such defined and uniform shapes. (DriveSeg is available for free and can be used by researchers and the academic community for non-commercial purposes).

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