{
  "$type": "site.standard.document",
  "description": "The present disclosure relates to methods and systems for spatiotemporal graph modelling of road users in observed frames of an environment in which an autonomous vehicle operates (i.e. a traffic scene), clustering of the road users into categories, and providing the spatiotemporal graph to a…",
  "path": "/patents/1349296",
  "publishedAt": "2023-08-17T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/0027",
    "Saber MALEKMOHAMMADI"
  ],
  "textContent": "The present disclosure relates to methods and systems for spatiotemporal graph modelling of road users in observed frames of an environment in which an autonomous vehicle operates (i.e. a traffic scene), clustering of the road users into categories, and providing the spatiotemporal graph to a trained graphical convolutional neural network (GNN) to predict a future pedestrian action. The future pedestrian action can be: one of the pedestrian will cross a road and the pedestrian will not cross the road. The spatiotemporal graph includes a better understanding of the observed frames (i.e. traffic scene).",
  "title": "METHOD AND SYSTEM FOR GRAPH NEURAL NETWORK BASED PEDESTRIAN ACTION PREDICTION IN AUTONOMOUS DRIVING SYSTEMS"
}