{
  "$type": "site.standard.document",
  "description": "A computer implemented method and related aspects for updating a perception function of a plurality of vehicles having an Automated Driving System (ADS) are disclosed. The method includes obtaining one or more locally updated model parameters of a self-supervised machine-learning algorithm from a…",
  "path": "/patents/1351202",
  "publishedAt": "2023-09-21T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06N3/098",
    "ZENSEACT AB"
  ],
  "textContent": "A computer implemented method and related aspects for updating a perception function of a plurality of vehicles having an Automated Driving System (ADS) are disclosed. The method includes obtaining one or more locally updated model parameters of a self-supervised machine-learning algorithm from a plurality of remote vehicles, and updating one or more model parameters of a global self-supervised machine-learning algorithm based on the obtained one or more locally updated model parameters. Further, the method includes fine-tuning the global self-supervised machine-learning algorithm based on an annotated dataset in order to generate a fine-tuned global machine-learning algorithm comprising one or more fine-tuned model parameters. The method further includes forming a machine-learning algorithm for an in-vehicle perception module based on the fine-tuned global machine-learning algorithm, and transmitting one or more model parameters of the formed machine-learning algorithm for the in-vehicle perception module to the plurality of remote vehicles.",
  "title": "SYSTEM AND METHOD FOR FEDERATED LEARNING OF SELF-SUPERVISED NETWORKS IN AUTOMATED DRIVING SYSTEMS"
}