公开(公告)号：EP3929824A3

公开(公告)日：2022-01-26

申请号：EP20214413.5

申请日：2020-12-16

申请人： INTEL Corporation

发明人： AHUJA, Nilesh ,  ALVAREZ, Ignacio ,  KRISHNAN, Ranganath ,  NDIOUR, Ibrahima ,  SUBEDAR, Mahesh ,  TICKOO, Omesh

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N7/00 ,  G06N20/00

摘要： Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

公开(公告)号：EP3916623A1

公开(公告)日：2021-12-01

申请号：EP20204938.3

申请日：2020-10-30

申请人： INTEL Corporation

发明人： AHUJA, Nilesh ,  NDIOUR, Ibrahima ,  FELIP LEON, Javier ,  GOMEZ GUTIERREZ, David ,  KRISHNAN, Ranganath ,  SUBEDAR, Mahesh ,  TICKOO, Omesh

IPC分类号： G06K9/00

摘要： Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

公开(公告)号：EP3929824A2

公开(公告)日：2021-12-29

申请号：EP20214413.5

申请日：2020-12-16

申请人： INTEL Corporation

发明人： AHUJA, Nilesh ,  ALVAREZ, Ignacio ,  KRISHNAN, Ranganath ,  NDIOUR, Ibrahima ,  SUBEDAR, Mahesh ,  TICKOO, Omesh

IPC分类号： G06N3/08 ,  G06N3/04 ,  G06N7/00 ,  G06N20/00

摘要： Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.