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公开(公告)号：US20240304003A1
公开(公告)日：2024-09-12
申请号：US18666598
申请日：2024-05-16
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Dhaval Shroff , Arvind Ramanandan , James Robert Howard Hakewill
IPC: G06V20/56 , G06F18/214 , G06N20/00 , G06T7/20 , G06V10/764 , G06V10/82
CPC classification number: G06V20/588 , G06F18/214 , G06N20/00 , G06T7/20 , G06V10/764 , G06V10/82 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30241 , G06T2207/30256 , G06V2201/10
Abstract: A processor coupled to memory is configured to receive image data based on an image captured by a camera of a vehicle. The image data is used as a basis of an input to a trained machine learning model trained to predict a three-dimensional trajectory of a machine learning feature. The three-dimensional trajectory of the machine learning feature is provided for automatically controlling the vehicle.
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公开(公告)号：US11748620B2
公开(公告)日：2023-09-05
申请号：US17301965
申请日：2021-04-20
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
CPC classification number: G06N3/08 , G05D1/0221 , G06F18/28 , G06V20/588 , G16Y20/10 , G06N3/04
Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
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公开(公告)号：US12223428B2
公开(公告)日：2025-02-11
申请号：US18459954
申请日：2023-09-01
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
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公开(公告)号：US20200257317A1
公开(公告)日：2020-08-13
申请号：US16272273
申请日：2019-02-11
Applicant: Tesla, Inc.
Inventor： Elon Musk , Kate Park , Nenad Uzunovic , Christopher Coleman Moore , Francis Havlak , Stuart Bowers , Andrej Karpathy , Arvind Ramanandan , Ashima Kapur Sud , Paul Chen , Paril Jain , Alexander Hertzberg , Jason Kong , Li Wang , Oktay Arslan , Nicklas Gustafsson , Charles Shieh , David Seelig
Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.
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公开(公告)号：US12164310B2
公开(公告)日：2024-12-10
申请号：US18160565
申请日：2023-01-27
Applicant: Tesla, Inc.
Inventor： Elon Musk , Kate Park , Nenad Uzunovic , Christopher Coleman Moore , Francis Havlak , Stuart Bowers , Andrej Karpathy , Arvind Ramanandan , Ashima Kapur Sud , Paul Chen , Paril Jain , Alexander Hertzberg , Jason Kong , Li Wang , Oktay Arslan , Nicklas Gustafsson , Charles Shieh , David Seelig
Abstract: A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.
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Patent Agency Ranking
公开(公告)号：US20210342637A1
公开(公告)日：2021-11-04
申请号：US17301965
申请日：2021-04-20
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
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公开(公告)号：US12014553B2
公开(公告)日：2024-06-18
申请号：US17450914
申请日：2021-10-14
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Dhaval Shroff , Arvind Ramanandan , James Robert Howard Hakewill
IPC: G06V20/56 , G05D1/00 , G06F18/214 , G06N20/00 , G06T7/20 , G06V10/764 , G06V10/82
CPC classification number: G06V20/588 , G05D1/0214 , G05D1/0221 , G05D1/0223 , G05D1/0231 , G06F18/214 , G06N20/00 , G06T7/20 , G06V10/764 , G06V10/82 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30241 , G06T2207/30256 , G06V2201/10
Abstract: A processor coupled to memory is configured to receive image data based on an image captured by a camera of a vehicle. The image data is used as a basis of an input to a trained machine learning model trained to predict a three-dimensional trajectory of a machine learning feature. The three-dimensional trajectory of the machine learning feature is provided for automatically controlling the vehicle.
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公开(公告)号：US11150664B2
公开(公告)日：2021-10-19
申请号：US16265720
申请日：2019-02-01
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Dhaval Shroff , Arvind Ramanandan , James Robert Howard Hakewill
Abstract: A processor coupled to memory is configured to receive image data based on an image captured by a camera of a vehicle. The image data is used as a basis of an input to a trained machine learning model trained to predict a three-dimensional trajectory of a machine learning feature. The three-dimensional trajectory of the machine learning feature is provided for automatically controlling the vehicle.
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公开(公告)号：US10997461B2
公开(公告)日：2021-05-04
申请号：US16265729
申请日：2019-02-01
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
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公开(公告)号：US20200250473A1
公开(公告)日：2020-08-06
申请号：US16265729
申请日：2019-02-01
Applicant: Tesla, Inc.
Inventor： Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
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