{
  "$type": "site.standard.document",
  "description": "Described techniques receive signal state data from a controller controlling one or more signal groups of a traffic light system, with a current signal state of respective signal groups, and information about the time interval elapsed since the last state switch of respective signal groups. A…",
  "path": "/patents/1348889",
  "publishedAt": "2023-08-10T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/0015",
    "Urban Software Institute GmbH"
  ],
  "textContent": "Described techniques receive signal state data from a controller controlling one or more signal groups of a traffic light system, with a current signal state of respective signal groups, and information about the time interval elapsed since the last state switch of respective signal groups. A feature vector is generated with a first value indicating the elapsed time since the latest switch of the respective signal group to a pass-state, and a second value indicating the elapsed time since the latest switch of the respective signal group to a stop-state. Based on the current feature vector, a quantile regression neural network predicts a low conditional quantile representing the minimum-end-time, a medium conditional quantile representing the likely-time and a high conditional quantile representing the maximum-end-time for respective signal groups. A message which includes the predicted minimum-end-time, likely-time, and maximum-end-time is provided to a receiving device associated with the vehicle.",
  "title": "COMPUTER SYSTEM AND METHOD FOR DETERMINING RELIABLE VEHICLE CONTROL INSTRUCTIONS"
}