{
  "$type": "site.standard.document",
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreiesjnrmdx7b5djury4ubzghnxedvaktzk2pvnhls7fne67v2u2zoe"
    },
    "mimeType": "image/png",
    "size": 103373
  },
  "description": "Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and…",
  "path": "/patents/1285941",
  "publishedAt": "2021-03-18T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/001",
    "Waymo LLC"
  ],
  "textContent": "Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning a trajectory of a vehicle. One of the methods includes obtaining input data for planning a driving trajectory for a vehicle, the input data comprising an intended route for the vehicle and data characterizing an environment in a vicinity of the vehicle; processing the input data using an input encoder neural network to generate feature data that includes a respective feature representation for each of a plurality of locations in the environment; applying spatial attention to the feature representations to generate a respective attention weight for each of the plurality of locations; generating a respective attended feature representation for each of the plurality of locations; generating a bottlenecked representation of the attended feature representations; and generating a planned future trajectory from at least the bottlenecked representation.",
  "title": "NEURAL NETWORKS WITH ATTENTIONAL BOTTLENECKS FOR TRAJECTORY PLANNING"
}