公开(公告)号：US12112555B1

公开(公告)日：2024-10-08

申请号：US18634353

申请日：2024-04-12

Applicant: Samsara Inc.

Inventor： Sung Chun Lee ,  Nathan Hurst ,  Yan Wang ,  Olamide Akintewe ,  Justin Levine ,  Kenshiro Nakagawa ,  Cole Jurden ,  Rachel Demerly ,  Aravindh Ramesh ,  Kevin Lai ,  Jovanna Bubar ,  Shirish Nair ,  Maisie Wang

IPC: G06V20/59 ,  G06V10/774 ,  G06V40/16

CPC classification number: G06V20/597 ,  G06V10/774 ,  G06V40/161 ,  G06V40/168 ,  G06V40/174

Abstract: Techniques are presented for detecting when drivers drive while drowsy. In some implementations, a drowsiness model is trained with data associated with inward videos and outward videos captured during a trip. The inward videos capture the inside of the cabin with the driver, and the outward videos capture the view in front of the vehicle in the direction of travel. Further, a device at the vehicle periodically calculates a drowsiness scale index value that indicates the level of drowsiness of the driver. Calculating the drowsiness scale index value includes obtaining a set of inward frames from the inward videos; for each inward frame, creating a face image by cropping the inward frame; obtaining a set of outward frames from the outward videos; calculating inward embeddings of the face images and outward embeddings of the outward frames; and calculating, by the drowsiness model, the drowsiness scale index value.

公开(公告)号：US12260616B1

公开(公告)日：2025-03-25

申请号：US18743824

申请日：2024-06-14

Applicant: Samsara Inc.

Inventor： Narendran Rajan ,  Yan Wang ,  Phil Ammirato ,  Kevin Lai ,  Evan Welbourne ,  Nathan Hurst

IPC: G06K9/00 ,  G06V10/764

Abstract: A computer-implemented method for machine learning model operation can include, by one or more processors executing program instructions: providing a first training dataset comprising a plurality of images and associated object detection labels, providing a second training dataset comprising a plurality of images and associated classification labels, and providing a machine learning model comprising a model backbone, an object detection task head, and a classification task head. The method can further include training the machine learning model by training the object detection task head using the first training dataset and training the classification task head using the second training dataset. The method can further include deploying the trained machine learning model that includes the trained model backbone and the trained object detection task head but does not include the trained classification task head.

公开(公告)号：US12254699B1

公开(公告)日：2025-03-18

申请号：US18823263

申请日：2024-09-03

Applicant: Samsara Inc.

Inventor： Shaurye Agarwal ,  Akshay Raj Dhamija ,  Howard Yu ,  Margaret Irene Finch ,  Jing Wang ,  Rohit Annigeri ,  Sharan Srinivasan ,  Yan Wang ,  Nathan Hurst

IPC: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

Abstract: Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.

公开(公告)号：US12112548B1

公开(公告)日：2024-10-08

申请号：US18618182

申请日：2024-03-27

Applicant: Samsara Inc.

Inventor： Shaurye Agarwal ,  Akshay Raj Dhamija ,  Howard Yu ,  Margaret Irene Finch ,  Jing Wang ,  Rohit Annigeri ,  Sharan Srinivasan ,  Yan Wang ,  Nathan Hurst

IPC: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

CPC classification number: G06V20/56 ,  G06T3/60 ,  G06V10/44 ,  G06V10/764

Abstract: Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.

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：US12266123B1

公开(公告)日：2025-04-01

申请号：US18672665

申请日：2024-05-23

Applicant: Samsara Inc.

Inventor： Suryakant Kaushik ,  Cole Jurden ,  Marc Clifford ,  Robert Koenig ,  Abner Ayala ,  Kevin Lai ,  Jose Cazarin ,  Margaret Irene Finch ,  Rachel Demerly ,  Nathan Hurst ,  Yan Wang ,  Akshay Raj Dhamija

IPC: G06T7/50 ,  G06V10/774 ,  G06V10/94 ,  G06V20/40 ,  G06V20/58 ,  H04N7/18

Abstract: Methods, systems, and computer programs are presented for monitoring tailgating when a vehicle follows another vehicle at an unsafe distance. A method for enhancing a Following Distance (FD) machine learning (ML) model is disclosed. The method includes providing a management user interface (UI) for configuring FD parameters, followed by receiving FD events. A UI for manual FD annotation and another for customer review of filtered FD events are also provided. Annotations and customer review information are collected to improve the training set for the FD ML model. The FD model is then trained with the new data and downloaded to a vehicle. Once installed, the FD model is utilized to detect FD events within the vehicle, thereby enhancing the vehicle's safety and performance in driving scenarios by improving the accuracy and reliability of FD event predictions or detections.