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：US20240400045A1

公开(公告)日：2024-12-05

申请号：US18738744

申请日：2024-06-10

Applicant: Aurora Operations, Inc.

Inventor： Kalin Vasilev Gochev ,  Michael Lee Phillips ,  David McAllister Bradley ,  Bradley Nicholas Emi

IPC: B60W30/095 ,  B60W50/00 ,  G05D1/617 ,  G05D1/81 ,  G08G1/16

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.