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：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.