{
  "$type": "site.standard.document",
  "description": "Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement—for example, a protected left-turn signal or all-way stop signs—at every turn that preserves a traffic phase exclusively for left turns…",
  "path": "/patents/1285454",
  "publishedAt": "2021-03-11T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G01C21/3492",
    "THE REGENTS OF THE UNIVERSITY OF MICHIGAN"
  ],
  "textContent": "Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement—for example, a protected left-turn signal or all-way stop signs—at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This disclosure presents a system that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns.",
  "title": "Inferring Left-Turn Information From Mobile Crowdsensing"
}