公开(公告)号：US12175773B1

公开(公告)日：2024-12-24

申请号：US18670365

申请日：2024-05-21

Applicant: Samsara Inc.

Inventor： Suryakant Kaushik

IPC: G06V20/59 ,  G06F3/0482 ,  G06V20/56

Abstract: Techniques are presented to provide an event review dashboard. One method relates to a user interface (UI) for the event review dashboard that provides customers with a comprehensive view of their event engagement over time. The UI includes interactive elements, information fields, and filters to assist in the analysis and review of customer actions in response to behavioral events detected in vehicles. These actions may include coaching or dismissal. The UI provides filters for date range selection, customer selection, dismissal rate visualization, and minimum event thresholds, enabling customers to assess the relevance and effectiveness of specific event types and their coaching program performance. Additionally, the UI also includes a table and panel for detailed analysis of event dismissal by type and direct access to dismissed event details. The UI is designed for ease of use and efficient analysis, with feedback mechanisms to improve the AI model learning process.

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.