{
  "$type": "site.standard.document",
  "description": "A machine-learned model that uses sensor and/or perception data to directly determine controls for operating an autonomous vehicle may be trained by identifying a preferred trajectory between a human-driven and vehicle-controlled trajectory, and using a first loss determined between the…",
  "path": "/patents/1375674",
  "publishedAt": "2025-05-29T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/001",
    "Zoox, Inc."
  ],
  "textContent": "A machine-learned model that uses sensor and/or perception data to directly determine controls for operating an autonomous vehicle may be trained by identifying a preferred trajectory between a human-driven and vehicle-controlled trajectory, and using a first loss determined between the vehicle-controlled trajectory and the path the autonomous vehicle ultimately ended up taking in a scenario and a second loss determined between the vehicle-controlled trajectory and the human-driven trajectory to refine the machine-learned model. The machine-learned model may additionally or alternatively be refined by a learned reward model constructed by replacing one or more output heads of the machine-learned model with a regression head that is trained using performance metrics determined for the vehicle-controlled trajectory.",
  "title": "REFINEMENT TRAINING FOR MACHINE-LEARNED VEHICLE CONTROL MODEL"
}