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Object identification and labeling tool for training autonomous vehicle controllers

  • US 10,169,678 B1
  • Filed: 02/27/2018
  • Issued: 01/01/2019
  • Est. Priority Date: 12/21/2017
  • Status: Active Grant
First Claim
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1. A computer-implemented method for identifying and labeling objects within images for training machine-learning based models that are used to autonomously operate vehicles, the method comprising:

  • displaying, on a first display area of a user interface, a three-dimensional (3-D) video of an environment in which vehicles operate, the 3-D environment video including respective 3-D images of one or more physical objects located in the environment;

    displaying, on a second display area of the user interface and in a time-synchronized manner with the 3-D video, a two-dimensional (2-D) video of at least a portion of the environment depicted in the 3-D environment video so that an image of the environment that was obtained at a particular time and that is included in the 2-D video is presented simultaneously on the user interface with an image of the environment that was obtained at the particular time and that is included in the 3-D video;

    receiving, via one or more user controls provided by the user interface, an indication of a boundary of a particular physical object depicted within a 3-D environment image included in the 3-D video;

    generating data indicative of the boundary of the particular physical object within the 3-D environment image;

    receiving an indication of a particular label for the particular physical object;

    associating the particular label for the particular physical object with the data indicative of the boundary of the particular physical object within the 3-D environment image, thereby distinguishing a set of data points that are representative of the particular physical object within the 3-D environment image from other data points included in the 3-D environment image; and

    storing an indication of the association between the particular label and the data indicative of the boundary of the particular physical object within the 3-D environment image in one or more tangible memories as a part of a training data set utilized to train one or more machine-learning based models, the one or more machine-learning based models used to autonomously operate vehicles.

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