{
  "$type": "site.standard.document",
  "description": "A functional safety calibration method for a vehicle includes accessing dynamometer data for the vehicle and determining, using the dynamometer data, first output data and N input parameters for a neural network utilized by a primary process of the vehicle to model a vehicle parameter based on the…",
  "path": "/patents/1381702",
  "publishedAt": "2026-04-30T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W50/045",
    "FCA US LLC"
  ],
  "textContent": "A functional safety calibration method for a vehicle includes accessing dynamometer data for the vehicle and determining, using the dynamometer data, first output data and N input parameters for a neural network utilized by a primary process of the vehicle to model a vehicle parameter based on the N input parameters, wherein N is an integer greater than one, identifying, based on the first output data, M of the N input parameters that predominantly affect the modeling of the vehicle parameter by the neural network, wherein M is an integer that is less than N, determining a function of at least M identified input parameters based on the first output data, and calibrating a secondary process using the determined function, wherein the secondary process is configured to be utilized by the vehicle as a functional safety check for the primary process.",
  "title": "PROCESS FOR CHARACTERIZING NEURAL NETWORKS BY PREDOMINANT INPUTS FOR IMPROVED VEHICLE FUNCTIONAL SAFETY"
}