Hyundai was granted a patent for AI-based depth estimation in autonomous driving.

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  "detected_at": "2026-09-30T17:23:23+00:00",
  "company": {
    "name": "Hyundai",
    "domain": "hyundaiusa.com"
  },
  "data": {
    "cpc": [
      {
        "code": "G06T 7/0002",
        "label": "Image data processing or generation, in general"
      },
      {
        "code": "B60W 50/06",
        "label": "Conjoint control of vehicle sub-units; road-vehicle drive control systems"
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        "label": "Conjoint control of vehicle sub-units; road-vehicle drive control systems"
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    "tags": [
      "Intellectual Property",
      "Research & Development",
      "Automotive",
      "Autonomous Technology",
      "Artificial Intelligence"
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    "detail": "In September 2026 Hyundai patented a view-synthesis training method for estimating scene depth from camera images used by autonomous vehicles.",
    "summary": "Hyundai was granted a patent for AI-based depth estimation in autonomous driving.",
    "event_at": "2026-09-29",
    "inventors": [
      {
        "name": "Jin Ho Park",
        "status": "resolved",
        "is_primary": true,
        "linkedin_url": "https://www.linkedin.com/in/jinho-park-9010"
      }
    ],
    "tech_area": "image processing / graphics",
    "event_kind": "grant_date",
    "grant_date": "2026-09-29",
    "source_url": "https://patents.google.com/patent/US12749170",
    "cpc_primary": {
      "code": "G06T 7/0002",
      "label": "Image data processing or generation, in general"
    },
    "patent_title": "Method and device for learning depth estimation based on view synthesis",
    "patent_number": "US12749170",
    "uspto_assignee": "Hyundai Motor Company",
    "patent_abstract": "A method for controlling autonomous driving of a vehicle is introduced. The method may comprise, training, based on an inference depth and an inference pose, a synthetic image model for generating a synthetic image, generating, based on the synthetic image, a first virtual image to be associated with the original image, generating, based on the original image, a second virtual image, training a generative adversarial network (GAN) for determining, based on the original image, authenticity of the first virtual image and the second virtual image, training, based on the trained GAN, a depth network, wherein the trained GAN outputs a determination of the authenticity of the first virtual image, outputting, based on the trained depth network, signal, and controlling, based on the signal, autonomous driving of the vehicle.",
    "precision_class": "structural",
    "invention_explanation": "A way to train driving software to judge distance by creating alternate views of camera scenes and having an AI model compare realistic and generated images.",
    "technologies_mentioned": [
      "autonomous driving",
      "depth estimation",
      "computer vision",
      "generative adversarial networks"
    ]
  }
}

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curl -X POST https://signals.autobound.ai/v1/companies/enrich \
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