{
  "$type": "site.standard.document",
  "description": "First training sensor data detected by a plurality of real-world sensors are obtained. The first training sensor data is associated with physical environment conditions. Second training sensor data detected by a plurality of virtual sensors are obtained. The second training sensor data is…",
  "path": "/patents/1348978",
  "publishedAt": "2023-08-10T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G05D1/0088",
    "Luminar, LLC"
  ],
  "textContent": "First training sensor data detected by a plurality of real-world sensors are obtained. The first training sensor data is associated with physical environment conditions. Second training sensor data detected by a plurality of virtual sensors are obtained. The second training sensor data is associated with simulated physical conditions of a virtual environment. A machine learning model is trained using both real-world and virtual training datasets including the first training sensor data, the second training sensor data, and respective sensor setting parameters of the plurality of real-world sensors and the plurality of virtual sensors. The real-world and virtual training datasets used to train the machine learning model include indications associated with the respective sensor parameter settings including one or more of the following: different scan line settings or different exposure settings. The machine learning model is provided for use in generating current parameters of an environment in which a vehicle operates.",
  "title": "GENERATING ENVIRONMENTAL PARAMETERS BASED ON SENSOR DATA USING MACHINE LEARNING"
}