公开(公告)号：US11386325B1

公开(公告)日：2022-07-12

申请号：US17454799

申请日：2021-11-12

Applicant: Samsara Inc.

Inventor： Sharan Srinivasan ,  Brian Tuan ,  John Bicket ,  Jing Wang ,  Muhammad Ali Akhtar ,  Abner Ayala Acevedo ,  Bruce Kellerman ,  Vincent Shieh

IPC: G06N3/08 ,  G06N7/00 ,  B60W40/09 ,  B60W50/14 ,  G06N20/20

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

公开(公告)号：US12272138B1

公开(公告)日：2025-04-08

申请号：US18750793

申请日：2024-06-21

Applicant: Samsara Inc.

Inventor： Rohit Annigeri ,  Sharan Srinivasan ,  Kevin Lai ,  Jose Cazarin ,  Brian Westphal ,  Shiva Bala ,  Ivan Stoev ,  Douglas Boyle ,  Cole Jurden ,  Margaret Irene Finch ,  Rachel Demerly ,  Maya Krupa ,  Shirish Nair ,  Nathan Hurst ,  Yan Wang ,  Shaurye Aggarwal ,  Akshay Raj Dhamija

IPC: G06V20/40 ,  B60Q9/00 ,  G06V10/764 ,  G06V10/774 ,  G06V10/776 ,  G06V10/94 ,  G06V20/58

Abstract: Techniques are presented for the detection and management of collision warning (CW) events. A training dataset comprising videos of vehicle collisions and non-collisions, sensor readings, environmental conditions, and more is utilized to train a CW classification model for detecting potential collision events in vehicles. A backend CW classification model, with greater computational resources, employs a more complex neural network to review CW events received by the Behavioral Monitoring System (BMS) based on video data, achieving higher precision and reducing false positives. The CW model is installed in vehicles for real-time detection, while the backend model is deployed at the BMS. The BMS validates detected CW events, filters out false positives, and streamlines the review process for fleet administrators and customers. Additional BMS filtering operations include assessing non-proximity-related CW events and camera impairments, with the filtered CW events presented for review in the safety inbox.

公开(公告)号：US11643102B1

公开(公告)日：2023-05-09

申请号：US17475114

申请日：2021-09-14

Applicant: Samsara Inc.

Inventor： Mathew Chasan Calmer ,  Justin Delegard ,  Justin Pan ,  Sabrina Shemet ,  Meelap Shah ,  Kavya Joshi ,  Brian Tuan ,  Sharan Srinivasan ,  Muhammad Ali Akhtar ,  John Charles Bicket ,  Margaret Finch ,  Vincent Shieh ,  Bruce Kellerman ,  Mitch Lin ,  Marvin Arroz ,  Siddhartha Datta Roy ,  Jason Symons ,  Tina Quach ,  Cassandra Lee Rommel ,  Saumya Jain

IPC: G07C5/08 ,  B60W50/14 ,  B60W40/08 ,  G07C5/00 ,  G06F3/01 ,  H04N5/225 ,  H04N19/132 ,  G06N3/02 ,  G06V20/56 ,  G06V20/59

CPC classification number: B60W50/14 ,  B60W40/08 ,  G06F3/012 ,  G06N3/02 ,  G06V20/56 ,  G06V20/597 ,  G07C5/008 ,  G07C5/0866 ,  H04N5/2252 ,  H04N19/132 ,  B60W2050/143 ,  B60W2520/105 ,  B60W2540/229 ,  G06V2201/10

Abstract: A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.

公开(公告)号：US11352013B1

公开(公告)日：2022-06-07

申请号：US17454773

申请日：2021-11-12

Applicant: Samsara Inc.

Inventor： Sharan Srinivasan ,  Brian Tuan ,  John Bicket ,  Jing Wang ,  Muhammad Ali Akhtar ,  Abner Ayala Acevedo ,  Bruce Kellerman ,  Vincent Shieh

IPC: B60W40/09 ,  G06N3/04 ,  G06T7/73 ,  G06V20/59

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.