公开(公告)号：US11518382B2

公开(公告)日：2022-12-06

申请号：US16696087

申请日：2019-11-26

Applicant: NEC Laboratories America, Inc.

Inventor： Samuel Schulter ,  Nataniel Ruiz ,  Manmohan Chandraker

IPC: B60W30/095 ,  G06N3/08 ,  G06F30/20 ,  G06V20/56 ,  B60W50/00

Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.

公开(公告)号：US20200094824A1

公开(公告)日：2020-03-26

申请号：US16696087

申请日：2019-11-26

Applicant: NEC Laboratories America, Inc.

Inventor： Samuel Schulter ,  Nataniel Ruiz ,  Manmohan Chandraker

IPC: B60W30/095 ,  G06N3/08 ,  G06K9/00 ,  G06F17/50

Abstract: A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.