{
  "$type": "site.standard.document",
  "description": "Autonomous vehicles are tested and evaluated on the road and in simulated road environments. When testing and evaluating autonomous vehicles in a simulated road environment, having detailed three-dimensional geometries and characteristics of road surfaces in the form of road model data can greatly…",
  "path": "/patents/1364171",
  "publishedAt": "2024-05-30T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W50/04",
    "GM Cruise Holdings LLC"
  ],
  "textContent": "Autonomous vehicles are tested and evaluated on the road and in simulated road environments. When testing and evaluating autonomous vehicles in a simulated road environment, having detailed three-dimensional geometries and characteristics of road surfaces in the form of road model data can greatly improve a simulated environment's ability to test and evaluate vehicle controls and dynamics, and can generate a more accurate pose for the autonomous vehicle in simulation. However, generating road model data is computationally expensive, and it is not desirable to generate road model data for an entire map to cover all test scenarios. An optimizer can reduce the amount of resources needed by selecting a least number of map sections to be generated and determining the locations of the map sections to be generated that achieves a desired coverage amount for the test suite.",
  "title": "OPTIMIZING RESOURCES NEEDED FOR ROAD MODEL DATA GENERATION WHILE ACHIEVING A DESIRED COVERAGE AMOUNT FOR A TEST SUITE"
}