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US Patent 10592785 Integrated system for detection of driver condition

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10592785
Date of Patent
March 17, 2020
Patent Application Number
15647748
Date Filed
July 12, 2017
Patent Citations
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US Patent 10262226 Systems and methods regarding 2D image and 3D image ensemble prediction models
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US Patent 10163018 Apparatuses, systems, and methods for inferring a driving enviroment based on vehicle occupant actions
Patent Citations Received
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US Patent 12136030 System and method for adapting a neural network model on a hardware platform
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US Patent 12079723 Optimizing neural network structures for embedded systems
7
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US Patent 12086097 Vector computational unit
8
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US Patent 11487288 Data synthesis for autonomous control systems
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US Patent 11537811 Enhanced object detection for autonomous vehicles based on field view
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US Patent 11561791 Vector computational unit receiving data elements in parallel from a last row of a computational array
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US Patent 11562231 Neural networks for embedded devices
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US Patent 11567514 Autonomous and user controlled vehicle summon to a target
...
Patent Primary Examiner
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Eric Rush
Patent abstract

Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

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