Netradyne, Inc.

Everguard, Inc.

Sandeep Pandya

David Jonathan Julian

Venkata Sreekanta Reddy Annapureddy

Michael Campos

Adam David Kahn

Avneesh Agrawal

Tejeswara Rao Gudena

Lance Steven Hetherington

Christopher Ro

Michael Korkin

Yong Wu

Sundeep Ahluwalia

Changsoo Jeong

Kyungsuk Lee

Vikram Makam Gupta

Arun Valiaparambil

Hisham Rahman

Sneha Aenugu

Priyam Bakliwal

Alexander Thomas

Suresh Babu Yanamala

Venkata Ramanan Venkatachalam Jayaraman

Arvind Yedla

Praveen Kumar

Vinay Kumar Rai

Veeresh Taranalli

Badugu Naveen Chakravarthy

Ankur Nigam

Vikram Gupta

Jaeyoon Kim

Manoj Venkata Tutika

Dale Alan Willis

Karan Kishore

Anubhav

Anubhav Gupta

Systems and methods are provided for detecting a driving action that mitigates risk.

Detection of driving actions that mitigate risk

A mobile system is provided for managing safety in a dynamic environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) process the data stream with aid of a machine learning algorithm trained model to generate a safety related result and feedback data, and (ii) deliver the feedback data to the object via the mobile tag device, and a mobile platform configured to move the mobile system in the environment.

Mobile safety systems

Systems and methods are provided for recording video of a driver in a vehicle and a surrounding visual field of the driver. The system includes a detachable body coupled to a windshield of a vehicle. The system further includes three or more cameras coupled to the detachable body. The three or more cameras are configured to capture surrounding views from the detachable body. One of the three or more cameras faces a driver seat of the vehicle in response to the detachable body being coupled to the windshield of the vehicle. The video may be processed by the system, the cloud, or a combination of them.

Recording video of an operator and a surrounding visual field

Systems and methods are provided for determining cause of atypical traffic events and/or encouraging good driving behavior. The systems and methods may involve a camera sensor and/or inertial sensors to detect traffic events, as well analytical methods that may attribute a cause to the traffic event. The systems and methods include a processor that is configured to determine an occurrence of an atypical traffic event at or near a monitored vehicle; and determine a cause of the atypical traffic event based on data collected at the monitored vehicle, wherein the cause of the atypical traffic event is at least one of: a driver or control system of the monitored vehicle; a second driver or second control system of a second vehicle; and a road condition.

Determining causation of traffic events and encouraging good driving behavior

Systems and methods are provided for distributed video storage and search with edge computing. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.

Distributed video storage and search with edge computing

Multimodal systems are provided for managing safety in an industrial environment. The system comprises: (a) a computer vision component for generating a first output data; (b) a real-time locating component for generating a second output data about an object within the industrial environment and a mobile tag device deployed to the object; (c) a LIDAR component for generating a third output data; and (d) an edge computing device connected to the computer vision component, the real-time locating component and the LIDAR component via a local network, and is configured to: (i) receive a data stream including the first output data, the second output data and the third output data, (ii) process the data stream using a machine learning algorithm trained model to generate a safety related result and feedback data, and (iii) deliver the feedback data to the object via the mobile tag device.

Multimodal safety systems and methods

Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle, based on anomalies detected between driver behavior and environment captured by the outward facing camera. Various aspects of the driver, which may include his direction of sight, point of focus, posture, gaze, is determined by image processing of the upper visible body of the driver, by a driver facing camera in the vehicle. Other aspects of environment around the vehicle captured by the multitude of cameras in the vehicle are used to correlate driver behavior and actions with what is happening outside to detect and warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.

Inward/outward vehicle monitoring for remote reporting and in-cab warning enhancements

An adaptive multimodal system is provided for managing safety in an industrial environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) obtain an identity of the object and the location data, and (ii) adjust, based at least in part on the identity and the location data, a pixel distribution for acquiring the 3D point cloud data, the process for generating the computer vision output data, or one or more parameters for acquiring an image data by the computer vision component.

Adaptive multimodal safety systems and methods

A multimodal system is provided for managing safety in an industrial environment. The system comprises: a computer vision component for generating a computer vision output data; a real-time locating component for generating location data about an object within the industrial environment; a LIDAR component for generating 3D point cloud data of the industrial environment; and one or more processors coupled to the computer vision component, the real-time locating component and the LIDAR component and configured to: (i) generate label data using the location data and identity about one or more objects generated by the real-time locating component, and (ii) update one or more predictive models utilized by the component vision component or the multimodal system using the label data.

Auto-labeling method for multimodal safety systems

Growing community of inventors

San Diego, CA, United States of America

Sandeep Pandya