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Patent US0180211120
Inventor

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Slightly Less than Average Length Specification


1 Independent Claims

  • Claim 2. The method of claim 1, wherein the 3D model includes a plurality of other light sources.
  • Claim 3. The method of claim 1, wherein the state of the traffic light model is one of red, amber, and green.
  • Claim 4. The method of claim 1, wherein simulating perception of the 3D model comprises simulating perception of the 3D model having one or more components of the 3D model in motion to obtain a plurality of images including the imagewherein annotating the image with the location and state of the traffic light model to obtain the annotated image comprises annotating the plurality of images with the state of the traffic light model to obtain a plurality of annotated imagesand wherein training the model according to the annotated image comprises training the model according to the plurality of annotated images.
  • Claim 5. The method of claim 1, wherein training the model according to the annotated image comprises training a machine learning algorithm according to the annotated image.
  • Claim 6. The method of claim 1, wherein training the model according to the annotated image comprises training the model to identify a state and location of an actual traffic light in a camera output.
  • Claim 7. The method of claim 1, wherein training the model according to the annotated image comprises training the model to output whether the traffic light applies to a vehicle processing camera outputs according to the model.
  • Claim 8. The method of claim 1, wherein the 3D model is a first 3D model, the image is a first image, and the annotated image is a first annotated image, the method further comprising: reading a configuration file defining location of one or more componentsgenerating a second 3D model according to the configuration filesimulating perception of the second 3D model to obtain a second imageannotating the second image with a location and state of the traffic light in the second 3D model to obtain a second annotated imageand training the model according to both of the first annotated image and the second annotated image.
  • Claim 9. The method of claim 1, wherein the 3D model is a first 3D model and the image is a first image, and the annotated image is a first annotated image, the method further comprising: defining a second 3D model having a traffic light model that does not govern a subject vehicle modelsimulating perception of the second 3D model from a point of view of a camera of to the subject vehicle model to obtain a second imageannotating the second image to that second 3D model includes no traffic light model governing the subject vehicle modeland training the model according to both of the first annotated image and the second annotated image.
  • Claim 10. The method of claim 1, wherein the 3D model is a first 3D model and the image is a first image, and the annotated image is a first annotated image, the method further comprising: defining a second 3D model having no traffic light modelsimulating perception of the second 3D model to obtain a second imageannotating the second image to that second 3D model includes no traffic light modeland training the model according to both of the first annotated image and the second annotated image.
  • Claim 11. A system comprising one or more processing devices and one or more memory devices operably coupled to the one or more processing devices, the one or more processing devices storing executable code effective to cause the one or more processing devices to: simulate perception of a 3D model having a traffic light model as a light source to obtain an imageannotate the image with a location and state of the traffic light model to obtain an annotated imageand train a model according to the annotated image.


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