公开(公告)号：US12136278B2

公开(公告)日：2024-11-05

申请号：US18302979

申请日：2023-04-19

Applicant: Verizon Connect Development Limited

Inventor： Douglas Coimbra De Andrade ,  Andrea Benericetti ,  Aurel Pjetri ,  Leonardo Taccari ,  Francesco Sambo ,  Alex Quintero Garcia ,  Luca Bravi

IPC: G06V20/59 ,  B60W40/09 ,  B60W50/14 ,  G06N20/00 ,  G06T7/10 ,  G06V20/40 ,  G06V40/16

Abstract: A device may process the video data, with a first machine learning model, to identify a driver of a vehicle and may process the video data associated with the driver, with a second machine learning model, to detect behavior data identifying a behavior of the driver. The device may process the behavior data, with a third machine learning model, to determine distraction data identifying whether the behavior is classified as a distracted behavior. The device may process the behavior data, with a fourth machine learning model, to determine policy compliance data identifying whether the behavior satisfies one or more policies. The device may calculate a distraction score based on the distraction data and the video data, and may calculate a policy compliance score based on the policy compliance data and vehicle data. The device may perform one or more actions based on the distraction score and the policy compliance score.

公开(公告)号：US11651599B2

公开(公告)日：2023-05-16

申请号：US16947780

申请日：2020-08-17

Applicant: Verizon Connect Development Limited

Inventor： Douglas Coimbra De Andrade ,  Andrea Benericetti ,  Aurel Pjetri ,  Leonardo Taccari ,  Francesco Sambo ,  Alex Quintero Garcia ,  Luca Bravi

IPC: G06K9/00 ,  G06V20/59 ,  G06T7/10 ,  B60W40/09 ,  G06N20/00 ,  B60W50/14 ,  G06V20/40 ,  G06V40/16

CPC classification number: G06V20/59 ,  B60W40/09 ,  B60W50/14 ,  G06N20/00 ,  G06T7/10 ,  G06V20/40 ,  G06V40/172 ,  B60W2540/229 ,  G06T2207/20081 ,  G06T2207/20132 ,  G06T2207/30201

Abstract: A device may process the video data, with a first machine learning model, to identify a driver of a vehicle and may process the video data associated with the driver, with a second machine learning model, to detect behavior data identifying a behavior of the driver. The device may process the behavior data, with a third machine learning model, to determine distraction data identifying whether the behavior is classified as a distracted behavior. The device may process the behavior data, with a fourth machine learning model, to determine policy compliance data identifying whether the behavior satisfies one or more policies. The device may calculate a distraction score based on the distraction data and the video data, and may calculate a policy compliance score based on the policy compliance data and vehicle data. The device may perform one or more actions based on the distraction score and the policy compliance score.