公开(公告)号：US20250162612A1

公开(公告)日：2025-05-22

申请号：US18516779

申请日：2023-11-21

Applicant: Zoox, Inc.

Inventor： Gregory Michael Woelki ,  Xiaosi Zeng ,  Gowtham Garimella

IPC: B60W60/00

Abstract: A machine-learned architecture may predict multiple paths that an object could take in the future without regard to time at which the object may occupy positions identified by one of those paths. These time-invariant paths may be used by an autonomous vehicle to filter detected objects by relevance to an autonomous vehicle's plans, improve prediction of an object's reaction to a vehicle candidate trajectory, determine right-of-way between object(s) and the autonomous vehicle, match detected objects to lanes, and/or improve prediction of odd or out-of-turn object behavior of an object.

公开(公告)号：US20250162616A1

公开(公告)日：2025-05-22

申请号：US18516618

申请日：2023-11-21

Applicant: Zoox, Inc.

Inventor： Gregory Michael Woelki ,  Xiaosi Zeng ,  Gowtham Garimella ,  Samir Parikh ,  Ethan Miller Pronovost

IPC: B60W60/00 ,  B60W50/06

Abstract: A machine-learned architecture may predict a set of spatially-diverse paths that an object may take in the future. The paths generated by this architecture may be time-invariant (e.g., not identifying a time at which the object may occupy a position along one of these paths) but can be used by a second machine-learned model to predict progress in time along these paths. This segregation of the spatial paths and progress in time along the paths improves the accuracy of the ultimate prediction and better captures rare object behavior.