公开(公告)号：US20240403647A1

公开(公告)日：2024-12-05

申请号：US18327707

申请日：2023-06-01

Applicant: Apollo Autonomous Driving USA LLC

Inventor： Weiman LIN ,  Yu CAO ,  Hao LIU ,  Ang LI

IPC: G06N3/086

Abstract: Embodiments of a methodology for generalized evolutionary training of a neural network may comprise: (i) obtaining a set of model snapshots by training a set of input models until at least one snapshot condition is satisfied for each input model from the set of input models, wherein each model snapshot comprises values of model components of its respective input model when the at least one snapshot condition was satisfied; (ii) generating model snapshot evaluation results by evaluating performance of each model snapshot; (iii) based upon the model snapshot evaluation results, selecting one or more parent models from the set of model snapshots; (iv) generating one or more child models by perturbing at least one or more model components of a parent model from the one or more parent models; and (v) setting the one or more child models as the set of input models for a subsequent training iteration.

公开(公告)号：US20250020807A1

公开(公告)日：2025-01-16

申请号：US18350209

申请日：2023-07-11

Applicant: Apollo Autonomous Driving USA LLC

Inventor： Hao LIU ,  Yu CAO ,  Ang LI

IPC: G01S17/89 ,  G01S17/931 ,  G06T7/70

Abstract: The present disclosure provides a system and method that analyzes, onboard an autonomous driving vehicle (ADV), a frame of LIDAR data to identify one or more obstacles in the frame of LIDAR data. The system and method compute, by a machine learning model onboard the ADV, a confidence value for each of the one or more obstacles to produce one or more confidence values, wherein the one or more confidence values indicate a level of prediction certainty of the machine learning model. The system and method determine whether at least one of the one or more confidence values is below a confidence threshold. The system and method upload the frame of LIDAR data from the ADV to an offboard storage area based on determining that at least one of the one or more confidence values is below the confidence threshold.