公开(公告)号：US12130624B2

公开(公告)日：2024-10-29

申请号：US17585650

申请日：2022-01-27

Applicant: Aurora Operations, Inc.

Inventor： Michael Lee Phillips ,  Don Burnette ,  Kalin Vasilev Gochev ,  Somchaya Liemhetcharat ,  Harishma Dayanidhi ,  Eric Michael Perko ,  Eric Lloyd Wilkinson ,  Colin Jeffrey Green ,  Wei Liu ,  Anthony Joseph Stentz ,  David Mcallister Bradley ,  Samuel Philip Marden

IPC: G05D1/00 ,  B60W30/095 ,  B60W30/12 ,  B60W30/16 ,  B60W30/18 ,  B60W50/00 ,  G01C21/20 ,  G01C21/34 ,  G05D1/02

CPC classification number: G05D1/0088 ,  B60W30/0953 ,  B60W30/0956 ,  B60W30/12 ,  B60W30/16 ,  B60W30/18163 ,  B60W50/0097 ,  G01C21/20 ,  G01C21/3453 ,  G05D1/0212 ,  G05D1/0214 ,  G05D1/0221 ,  G05D1/0223 ,  B60W2554/00

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

公开(公告)号：US20250121856A1

公开(公告)日：2025-04-17

申请号：US18990620

申请日：2024-12-20

Applicant: Aurora Operations, Inc.

Inventor： Colin Jeffrey Green ,  Wei Liu ,  David McAllister Bradley ,  Vijay Subramanian

IPC: B60W60/00 ,  B60W30/095 ,  B60W30/18 ,  G08G1/01 ,  G08G1/0962 ,  G08G1/16

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.