Netradyne, Inc.

Avneesh Agrawal

David Jonathan Julian

Venkata Sreekanta Reddy Annapureddy

Michael Campos

Adam David Kahn

Sandeep Pandya

Vinay Kumar Rai

Tejeswara Rao Gudena

Arvind Yedla

Arun Valiaparambil

Suresh Babu Yanamala

Stephan Franklin Dunn

Venkat Ramanan Venkatachalam Jayaraman

Matthew Karl Drake

Hemanth Roy Badugu

Aravind P (Padmasanan)

Venkata Ramanan Venkatachalam Jayaraman

Praveen Kumar

Veeresh Taranalli

Badugu Naveen Chakravarthy

Lance Steven Hetherington

Anirudh Maringanti

Jaeyoon Kim

Ankur Nigam

Alexander Thomas

Priyam Bakliwal

Sneha Aenugu

Karan Kishore

Manoj Venkata Tutika

Dale Alan Willis

Anubhav

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

Detection of driving actions that mitigate risk

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. Combinations of co-occurring driving events may be detected and used to warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.

Combination alerts

Systems and methods provide, implement, and use using a computer-vision based methods of context-sensitive monitoring and characterization of driver behavior. Additional systems and methods are provided for unsupervised learning of action values, monitoring of a driver's environment, and transmitting visual information from a client to a server.

Driver behavior monitoring

Systems and methods for curating video and other driving-related data for use in driver coaching, which may include selecting or ranking driving behaviors for coaching, selecting or ranking drivers for coaching, selecting or ranking video and other data to be used in coaching, preparing for, scheduling, and summarizing coaching sessions, matching the format of coaching to the behavior or person being coached, preventing unsafe driving situations, influencing job dispatch decisions based on safety scores, and/or reducing data bandwidth usage based on a determined coaching effectiveness of video data.

Virtual safety manager

Systems and methods are provided for distributed video storage and search over edge computing devices having a short-term memory and a long-term memory. 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.

Short-term and long-term memory on an edge device

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

Methods and systems for joint processing for data inference in a vehicle-to-cloud communication system are provided. The method includes processing sensor data from a first sensor in a first vehicle using a first model at a first device, resulting in a first inference data. The first communication data derived from the first sensor data is sent to a cloud device, where it undergoes further processing using a second model to generate cloud inference data. Subsequently, the cloud communication data based on the cloud inference data is sent to a second device in a second vehicle from the cloud device.

Joint processing for embedded data inference

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

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Bengaluru, India

Avneesh Agrawal