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公开(公告)号：US20210150752A1
公开(公告)日：2021-05-20
申请号：US16686840
申请日：2019-11-18
Applicant: Waymo LLC
Inventor： Alper Ayvaci , Yu-Han Chen , Ruichi Yu , Chen Wu , Noha Waheed Ahmed Radwan , Jonathon Shlens
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.
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公开(公告)号：US10902272B2
公开(公告)日：2021-01-26
申请号：US16879299
申请日：2020-05-20
Applicant: Waymo LLC
Inventor： Victoria Dean , Abhijit S. Ogale , Henrik Kretzschmar , David Harrison Silver , Carl Kershaw , Pankaj Chaudhari , Chen Wu , Congcong Li
Abstract: Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.
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公开(公告)号：US20190392231A1
公开(公告)日：2019-12-26
申请号：US16018490
申请日：2018-06-26
Applicant: Waymo LLC
Inventor： Victoria Dean , Abhijit S. Ogale , Henrik Kretzschmar , David Harrison Silver , Carl Kershaw , Pankaj Chaudhari , Chen Wu , Congcong Li
Abstract: Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.
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公开(公告)号：US20230046289A1
公开(公告)日：2023-02-16
申请号：US17893376
申请日：2022-08-23
Applicant: WAYMO LLC
Inventor： Justin Thorsen , Changchang Wu , Alper Ayvaci , Tiffany Chen , Lo Po Tsui , Zhinan Xu , Chen Wu , Sean Rafferty
IPC: G06K19/067 , G09F3/00
Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data, for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.
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公开(公告)号：US11475263B2
公开(公告)日：2022-10-18
申请号：US16827835
申请日：2020-03-24
Applicant: WAYMO LLC
Inventor： Justin Thorsen , Changchang Wu , Alper Ayvaci , Tiffany Chen , Lo Po Tsui , Zhinan Xu , Chen Wu , Sean Rafferty
IPC: G06K19/067 , G09F3/00
Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.
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Patent Agency Ranking
公开(公告)号：US20210192238A1
公开(公告)日：2021-06-24
申请号：US17123185
申请日：2020-12-16
Applicant: WAYMO LLC
Inventor： Victoria Dean , Abhijit S. Ogale , Henrik Kretzschmar , David Harrison Silver , Carl Kershaw , Pankaj Chaudhari , Chen Wu , Congcong Li
Abstract: Aspects of the disclosure relate to training and using a phrase recognition model to identify phrases in images. As an example, a selected phrase list may include a plurality of phrases is received. Each phrase of the plurality of phrases includes text. An initial plurality of images may be received. A training image set may be selected from the initial plurality of images by identifying the phrase-containing images that include one or more phrases from the selected phrase list. Each given phrase-containing image of the training image set may be labeled with information identifying the one or more phrases from the selected phrase list included in the given phrase-containing images. The model may be trained based on the training image set such that the model is configured to, in response to receiving an input image, output data indicating whether a phrase of the plurality of phrases is included in the input image.
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公开(公告)号：US12056898B1
公开(公告)日：2024-08-06
申请号：US17548633
申请日：2021-12-13
Applicant: Waymo LLC
Inventor： Chen Wu , Carl Warren Craddock , Andreas Wendel
IPC: G06T7/80 , G01S17/86 , G01S17/931 , G06F18/25 , G06V20/58
CPC classification number: G06T7/80 , G01S17/86 , G01S17/931 , G06F18/25 , G06V20/584
Abstract: The disclosure relates to assessing operation of a camera. In one instance, a volume of space corresponding to a first vehicle in an environment of a second vehicle may be identified using sensor data generated by a LIDAR system of the second vehicle. An image captured by a camera of the second vehicle may be identified. The camera may have an overlapping field of view of the LIDAR system at a time when the sensor data was generated. An area of the image corresponding to the volume of space may be identified and processed in order to identify a vehicle light. The operation of the camera may be assessed based on the processing.
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公开(公告)号：US20240054772A1
公开(公告)日：2024-02-15
申请号：US18492891
申请日：2023-10-24
Applicant: Waymo LLC
Inventor： Zhinan Xu , Maya Kabkab , Chen Wu , Woojong Koh
CPC classification number: G06V10/7784 , G05D1/0246 , G06T7/60 , G05D1/0088 , G06T7/90 , G06V20/20 , G06V20/582 , G06T2207/30252
Abstract: Aspects of the disclosure relate to determining a sign type of an unfamiliar sign. The system may include one or more processors. The one or more processors may be configured to receive an image and identify image data corresponding to a traffic sign in the image. The image data corresponding to the traffic sign may be input in a sign type model. The processors may determine that the sign type model was unable to identify a type of the traffic sign and determine one or more attributes of the traffic sign. The one or more attributes of the traffic sign may be compared to known attributes of other traffic signs and based on this comparison, a sign type of the traffic sign may be determined. The vehicle may be controlled in an autonomous driving mode based on the sign type of the traffic sign.
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公开(公告)号：US11544869B2
公开(公告)日：2023-01-03
申请号：US17342434
申请日：2021-06-08
Applicant: Waymo LLC
Inventor： Alper Ayvaci , Yu-Han Chen , Ruichi Yu , Chen Wu , Noha Waheed Ahmed Radwan , Jonathon Shlens
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating object interaction predictions using a neural network. One of the methods includes obtaining a sensor input derived from data generated by one or more sensors that characterizes a scene. The sensor input is provided to an object interaction neural network. The object interaction neural network is configured to process the sensor input to generate a plurality of object interaction outputs. Each respective object interaction output includes main object information and interacting object information. The respective object interaction outputs corresponding to the plurality of regions in the sensor input are received as output of the object interaction neural network.
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公开(公告)号：US10928828B2
公开(公告)日：2021-02-23
申请号：US16220225
申请日：2018-12-14
Applicant: Waymo LLC
Inventor： Zhinan Xu , Maya Kabkab , Chen Wu , Woojong Koh
Abstract: Aspects of the disclosure relate to determining a sign type of an unfamiliar sign. The system may include one or more processors. The one or more processors may be configured to receive an image and identify image data corresponding to a traffic sign in the image. The image data corresponding to the traffic sign may be input in a sign type model. The processors may determine that the sign type model was unable to identify a type of the traffic sign and determine one or more attributes of the traffic sign. The one or more attributes of the traffic sign may be compared to known attributes of other traffic signs and based on this comparison, a sign type of the traffic sign may be determined. The vehicle may be controlled in an autonomous driving mode based on the sign type of the traffic sign.
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