Samsara Inc.

Suryakant Kaushik

Yan Wang

Nathan Hurst

Matthew Lee Basham

Margaret Irene Finch

Pierre Gavaret

Kevin Lai

Faiz Abbasi

Cole Jurden

Katherine Lee Heddleston

Akshay Raj Dhamija

Rachel Demerly

Jose Cazarin

Abner Ayala

Jason Smith

Marc Clifford

Jaiveer Kothari

Rachel Hiu Tung Cheng

Frieda Bobay

Matthew Geddie

Darrin Joseph Yuhn, Jr

Robert Koenig

Dynamic geofences may be generated and updated based on locations of related assets. For example, locations of a group of related assets, including a primary asset and one or more secondary assets, may be determined. A geofence associated with the primary asset may then be analyzed to identify any geofences of secondary assets that overlap. This process may be repeated for each overlapping geofence to identify additional overlap and expand or move a combined dynamic geofence. Environmental aspects such as time of day, weather conditions, and risk levels may be considered for dynamic geofence adjustments. The combined geofence and asset locations may be displayed on a user interface.

Dynamic geofence generation and adjustment for asset tracking and monitoring

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.

Monitoring the safe distance between vehicles while driving

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.

Event analysis and review tool

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Austin, TX, United States of America

Suryakant Kaushik