Samsara Inc.

Brian Westphal

Nathan Hurst

John Charles Bicket

Sven Eberhardt

Kevin Lai

Cole Jurden

Akshay Raj Dhamija

Shirish Nair

Rohit Annigeri

Jose Cazarin

Douglas Boyle

Yan Wang

Sharan Srinivasan

Margaret Irene Finch

Jevon Yeoh

Rachel Demerly

Lucas Doyle

Saurabh Tripathi

Miranda Huey

Abner Ayala

Evan Welbourne

Ewelina Sieradzka

Maya Krupa

Harry Lu

Sanjit Biswas

Robert Bales

Yangyong Zhang

Alexander Stevenson

Ivan Stoev

Rudy Wen

Gerrit Steinbach

Allen Lu

Young Guo

Jason Liu

Pang Wu

Shiva Bala

Shaurye Aggarwal

An Applicant Assistant System may receive, from a user device, a user input indicating a query or task associated with information displayed in a user interface. The device may determine one or more components associated with the user interface and access component metadata of the determined one or more components, the component metadata for each of the one or more components indicating one or more of a fact, a data link, or an action. A prompt comprising at least some of the component metadata, at least some context information, and an indication of one or more available response elements may be provided to a large language model (LLM). The LLM may response with one or more response elements that may be processed by the System, such as to cause updates to the user interface on the user device.

Artificial intelligence application assistant

An inspection application may display, on a user device, a user interface including at least a portion of a vehicle inspection report including a plurality of inspection categories. The inspection application may configure the user device to obtain inspection information associated with a vehicle, the inspection information comprising photographs, videos, audio, and/or text. The inspection application and/or a network-accessible inspection assistant system may generate a prompt including at least a portion of the inspection information and information indicating potential vehicle defects. The prompt may be transmitted to a large language model that returns a response indicating any potential vehicle defects identified in the inspection information. The vehicle inspection report may then be updated to indicate the potential vehicle defects identified by the language model.

Artificial intelligence inspection assistant

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.

Forward collision warning

A driver monitoring system includes internal and external cameras attached to a vehicle. The cameras capture video of the vehicle's driver and the area surrounding the vehicle for later review by a coach or supervisor. To ensure privacy of people who appear in video, portions of the video may be processed to blur faces, license plates, and/or other features. Furthermore, access control mechanisms exist so that only users in specific roles have access to review certain types of video.

Data privacy in driver monitoring system

Methods, systems, and computer programs are presented for the management of lane-departure (LD) events. One method includes training a classifier for LD events and loading the classifier into a vehicle. LD events are detected based on outward images using the classifier, while the turn signal is monitored to prevent false triggers. If an LD event is detected, rules are checked for alerting the driver and deciding whether to alert the driver or not. Subsequently, additional rules are checked for reporting the event and deciding whether to report the event to a Behavior Monitoring System (BMS) or to discard it. The method also includes a solid line departure model that identifies crossing dashed, solid-white, and solid-yellow lanes, delaying alerts and event generation until a significant portion of the vehicle crosses over the lane. The model also outputs a confidence score reflecting the amount of vehicle deviation from the driving lane.

Lane departure monitoring

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Livermore, CA, United States of America

Brian Westphal