公开(公告)号：WO2021037760A1

公开(公告)日：2021-03-04

申请号：PCT/EP2020/073562

申请日：2020-08-21

Applicant: FIVE AI LIMITED

Inventor： REDFORD, John ,  KALTWANG, Sebastian ,  ROGERS, Blain ,  SADEGHI, Jonathan ,  GUNN, James ,  ELSON, Torran ,  CHARYTONIUK, Adam

IPC: G06N3/00 ,  G06N3/08 ,  G06N3/04 ,  G06N5/00 ,  G06N7/02 ,  G06N7/00 ,  G05D1/00 ,  G06T15/06 ,  G06K9/00 ,  G06N20/00 ,  G06N5/02

Abstract: Herein, a "perception statistical performance model" (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled. The PSPM comprises a time-dependent model such that the perception output sampled at the current time instant depends on at least one of: an earlier one of the perception outputs sampled at a previous time instant, and an earlier one of the perception ground truths computed for a previous time instant.

公开(公告)号：WO2021037763A1

公开(公告)日：2021-03-04

申请号：PCT/EP2020/073565

申请日：2020-08-21

Applicant: FIVE AI LIMITED

Inventor： REDFORD, John ,  WALKER, Simon ,  PETERS, Benedict ,  KALTWANG, Sebastian ,  ROGERS, Blain ,  SADEGHI Jonathan ,  GUNN, James ,  ELSON, Torran ,  CHARYTONIUK, Adam

IPC: G06N3/00 ,  G06N3/04 ,  G06N3/08 ,  G05D1/00 ,  G06K9/00 ,  G06N7/00 ,  G06T15/06 ,  G06N20/00 ,  G06N5/02 ,  G06N5/00

Abstract: Herein, a "perception statistical performance model" (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety /performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.