Samsara Inc.

Bruce Kellerman

John Charles Bicket

Brian Tuan

Muhammad Ali Akhtar

Sharan Srinivasan

Vincent Shieh

Jing Wang

Abner Ayala Acevedo

Justin Joel Delegard

Jason Frederic Symons

Kavya U Joshi

Margaret Irene Finch

Sabrina Quinn Shemet

Mathew Chasan Calmer

Cassandra Lee Rommel

Meelap Shah

Justin Pan

Saumya Jain

Siddhartha Datta Roy

Marvin Arroz

Mitch Lin

Tina Quach

A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Refining event triggers using machine learning model feedback

A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.

Dash cam with artificial intelligence safety event detection

A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.

Ensemble neural network state machine for detecting distractions

Tuning layers of a modular neural network

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Atlanta, GA, United States of America

Bruce Kellerman