{
  "patent_number": "US 11436484",
  "country": "US",
  "title": "Nvidia's Method for Training Self-Driving Car AI in Simulations",
  "original_title": "Training, testing, and verifying autonomous machines using simulated environments",
  "summary": "Nvidia's 2022 patent describes how to train AI for self-driving cars by using simulated environments and virtual sensors, then matching the simulated data format to real-world sensor data for AI processing.",
  "what_it_does": "This patent details a method for training artificial intelligence (AI) systems, particularly for autonomous vehicles. It involves creating a simulated world where a virtual object, like a car, exists. Virtual sensors on this object generate data that is then encoded to match the format of data from real-world sensors. This encoded data is fed into machine learning models, which are trained to produce outputs that control the virtual object's actions within the simulation. The system updates the simulation based on these AI outputs, creating a loop for continuous learning and verification. For example, a virtual camera in the simulation generates image data, which is then processed by AI trained on real camera data to decide how the virtual car should steer.",
  "what_it_does_not_cover": [
    "Training AI using only real-world sensor data without any simulation.",
    "Testing AI models in the real world without prior simulation.",
    "Simulations where virtual sensor data format does not match real-world sensor data.",
    "Using AI models that are not based on machine learning.",
    "Updating the simulated environment without using AI model outputs."
  ],
  "filed": "2019-03-27",
  "granted": "2022-09-06",
  "expires": null,
  "status": "active",
  "holder": "Nvidia Corp",
  "holder_url": "https://patentbrief.org/company/nvidia-corp",
  "inventors": [
    {
      "name": "Zachary Taylor",
      "url": "https://patentbrief.org/inventor/zachary-taylor"
    },
    {
      "name": "Greg Heinrich",
      "url": "https://patentbrief.org/inventor/greg-heinrich"
    },
    {
      "name": "Matthew Campbell",
      "url": "https://patentbrief.org/inventor/matthew-campbell"
    },
    {
      "name": "Rev Lebaredian",
      "url": "https://patentbrief.org/inventor/rev-lebaredian"
    },
    {
      "name": "Michael Cox",
      "url": "https://patentbrief.org/inventor/michael-cox"
    },
    {
      "name": "Tony Tamasi",
      "url": "https://patentbrief.org/inventor/tony-tamasi"
    },
    {
      "name": "Claire Delaunay",
      "url": "https://patentbrief.org/inventor/claire-delaunay"
    },
    {
      "name": "Mark Daly",
      "url": "https://patentbrief.org/inventor/mark-daly"
    },
    {
      "name": "Curtis Beeson",
      "url": "https://patentbrief.org/inventor/curtis-beeson"
    },
    {
      "name": "David Auld",
      "url": "https://patentbrief.org/inventor/david-auld"
    },
    {
      "name": "John Zedlewski",
      "url": "https://patentbrief.org/inventor/john-zedlewski"
    },
    {
      "name": "Gary Hicok",
      "url": "https://patentbrief.org/inventor/gary-hicok"
    },
    {
      "name": "Clement FARABET",
      "url": "https://patentbrief.org/inventor/clement-farabet"
    }
  ],
  "times_cited": 17,
  "tags": [
    "automotive",
    "software",
    "ai_ml",
    "consumer_electronics"
  ],
  "abstract": "In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.",
  "url": "https://patentbrief.org/patent/us/11436484/alphazero",
  "markdown_url": "https://patentbrief.org/patent/us/11436484/alphazero/md",
  "google_patents_url": "https://patents.google.com/patent/US11436484",
  "relatedPatents": []
}