{
  "$type": "site.standard.document",
  "description": "Embodiments relate to the detection of edge cases through application of a neural network to predict future vehicle environment data and identifying an edge case when the prediction error exceeds a given threshold. This allows edge cases to be identified based on unexpected vehicle environmental…",
  "path": "/patents/1374315",
  "publishedAt": "2026-06-02T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W50/04",
    "POST URBAN IP LIMITED"
  ],
  "textContent": "Embodiments relate to the detection of edge cases through application of a neural network to predict future vehicle environment data and identifying an edge case when the prediction error exceeds a given threshold. This allows edge cases to be identified based on unexpected vehicle environmental conditions or conditions that otherwise cause the neural network to make inaccurate predictions. These edge cases can then be utilized to better train machine learning systems, for instance, to train autonomous vehicle control systems. Alternatively, the identification of an edge case can highlight the need for remedial action and can therefore trigger an alert to a vehicle control system to take remedial action. Further methods and systems described herein improve environmental sensing by providing a computationally efficient and accurate means for fusing sensor data and using this fused data to control sensors to focus on areas that would most reduce the uncertainty in the sensing system.",
  "title": "Network for detecting edge cases for use in training autonomous vehicle control systems"
}