Samsara Inc.

Sharan Srinivasan

Jing Wang

John Charles Bicket

Brian Tuan

Muhammad Ali Akhtar

Bruce Kellerman

Vincent Shieh

Abner Ayala Acevedo

Margaret Irene Finch

Yan Wang

Nathan Hurst

Akshay Raj Dhamija

Rohit Annigeri

Justin Joel Delegard

Jason Frederic Symons

Kavya U Joshi

Mathew Chasan Calmer

Sabrina Quinn Shemet

Justin Pan

Cassandra Lee Rommel

Meelap Shah

Saumya Jain

Shirish Nair

Tina Quach

Siddhartha Datta Roy

Marvin Arroz

Mitch Lin

Howard Yu

Shaurye Agarwal

William Evan Welbourne

Ingo Gerhard Wiegand

Kevin Lai

Cole Jurden

Brian Westphal

Rachel Demerly

Jose Cazarin

Douglas Boyle

Shiva Bala

Shaurye Aggarwal

Ivan Stoev

Maya Krupa

Faraz Abbasi

The present disclosure is related to systems and methods of dashcam installation for providing instructions on placing a dashcam for a person or dashcam installer. Aspects of the present disclosure are related to providing a digital assistant for installing the dashcam in an acceptable position, where the dashcam can capture images of objects with acceptable quality (e.g., as determined by a machine learning model). In some embodiments, the dashcam detects a driver and provides inferences related to the current dashcam position. The inferences can be based on analyzing images captured by the dashcam in the current position, where the analysis can utilize a machine learning model. The inferences can also provide one or more instructions to move and/or tilt the dashcam to the acceptable installation position.

Systems and methods for dashcam installation

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

Methods, systems, and programs are presented for detecting impaired views in monitoring cameras. One method includes training a rotation classifier with unsupervised learning utilizing a first set of images. The rotation classifier is configured to receive an input image and generate a rotation feature embedding for the input image. In addition, the method includes training an impairment classifier with supervised learning utilizing a second set of images, impairment labels for each of the second set of images, and the rotation feature embedding, generated by the rotation classifier, for each of the second set of images. The method further includes accessing a vehicle image captured by a camera on a vehicle, and providing the vehicle image to the impairment classifier as input, and the impairment classifier outputs a camera impairment from a set of camera impairment categories. Further, the vehicle image and the camera impairment are presented on a user interface.

Detection of camera with impaired view

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

Growing community of inventors

Sunnyvale, CA, United States of America

Sharan Srinivasan