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公开(公告)号：US11995546B1
公开(公告)日：2024-05-28
申请号：US17811512
申请日：2022-07-08
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC分类号： G06N3/08 , B60W40/09 , B60W50/14 , G06N7/01 , G06N20/20 , B60W2420/403 , B60W2420/54 , B60W2540/223 , B60W2540/225 , B60W2540/229
摘要： 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.
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公开(公告)号：US11352014B1
公开(公告)日：2022-06-07
申请号：US17454790
申请日：2021-11-12
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
摘要： 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.
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公开(公告)号：US11352013B1
公开(公告)日：2022-06-07
申请号：US17454773
申请日：2021-11-12
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
摘要： 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.
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公开(公告)号：US11866055B1
公开(公告)日：2024-01-09
申请号：US17662622
申请日：2022-05-09
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC分类号： B60W40/09 , G06N3/045 , G06N3/082 , G06V40/20 , B60W2400/00
摘要： 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.
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公开(公告)号：US11780446B1
公开(公告)日：2023-10-10
申请号：US17661689
申请日：2022-05-02
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
CPC分类号： B60W40/09 , G06N3/045 , G06T7/73 , G06V20/597
摘要： 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.
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公开(公告)号：US11386325B1
公开(公告)日：2022-07-12
申请号：US17454799
申请日：2021-11-12
申请人： Samsara Inc.
发明人： Sharan Srinivasan , Brian Tuan , John Bicket , Jing Wang , Muhammad Ali Akhtar , Abner Ayala Acevedo , Bruce Kellerman , Vincent Shieh
摘要： 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.
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