Samsara Inc.

Samsara Networks Inc.

Samsara Networks Lac.

Kenshiro Nakagawa

John Charles Bicket

Ingo Gerhard Wiegand

Derrek Harrison

Yan Wang

Bodecker John DellaMaria

James Michael Rowson

Nathan Hurst

Sylvie Lee

Sung Chun Lee

Meelap Shah

David Liang

Kevin Lai

Maisie Wang

Ava O'Neill

Shirish Nair

Cole Jurden

Rachel Demerly

Matthew Hsu

Sushant Thakur

Justin Levine

Olamide Akintewe

Jovanna Bubar

Aravindh Ramesh

Benjamin Chang

Andrew E Robbins

Neel Sheth

Prajakti Jinachandran

Aaron Zeisler

Nicholas Jurden

Bernardo Farah

Jason Kwon

Manan Agarwal

A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

Driver identification using diverse driver assignment sources

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.

Drowsy driving detection

A map-based notification system configured to perform operations that include: accessing a base-map that comprises a map-tile, the map-tile defining a speed limit of a location; receiving vehicle data that includes speed data; determining that the speed data from the vehicle data transgresses the speed limit associated with the location defined by the map-tile; and causing display of a notification in response to determining that the speed data from the vehicle data transgresses the speed limit associated with the location defined by the map-tile.

Map-based notification system

Methods for performing improving facial recognition of a driver in a vehicle are disclosed. A set of images is received. Each of the set of images includes a portion that is identified as a face. The identification is based on an application of a first machine-learned model to each of the set of images. The application of the first machine-learned model is performed by an application associated with a client camera device mounted in a vehicle. Based on a determination that the set of images matches one or more reference images stored in a database with a confidence level that is equal to or greater than a confidence threshold, a person corresponding to the one or more reference images is associated as a driver of the vehicle during a time period in which the set of images was captured.

Facial recognition for drivers

Growing community of inventors

San Francisco, CA, United States of America

Kenshiro Nakagawa