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公开(公告)号：US11610423B2
公开(公告)日：2023-03-21
申请号：US17099642
申请日：2020-11-16
Applicant: Waymo LLC
Inventor： Junhua Mao , Jiyang Gao , Yukai Liu , Congcong Li , Zhishuai Zhang , Dragomir Anguelov
IPC: G06V40/10 , G06K9/62 , B60W30/095 , G06V10/25
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing point cloud data using spatio-temporal-interactive networks.
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公开(公告)号：US11562573B2
公开(公告)日：2023-01-24
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：US20220156965A1
公开(公告)日：2022-05-19
申请号：US17505900
申请日：2021-10-20
Applicant: Waymo LLC
Inventor： Jingxiao Zheng , Xinwei Shi , Alexander Gorban , Junhua Mao , Andre Liang Cornman , Yang Song , Ting Liu , Ruizhongtai Qi , Yin Zhou , Congcong Li , Dragomir Anguelov
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.
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公开(公告)号：US20210294346A1
公开(公告)日：2021-09-23
申请号：US17343187
申请日：2021-06-09
Applicant: Waymo LLC
Inventor： Junhua Mao , Congcong Li , Alper Ayvaci , Chen Sun , Kevin Murphy , Ruichi Yu
IPC: G05D1/02 , G05D1/00 , G06K9/00 , G06K9/62 , B60W30/095 , G01S17/931
Abstract: Aspects of the disclosure relate to training and using a model for identifying actions of objects. For instance, LIDAR sensor data frames including an object bounding box corresponding to an object as well as an action label for the bounding box may be received. Each sensor frame is associated with a timestamp and is sequenced with respect to other sensor frames. Each given sensor data frame may be projected into a camera image of the object based on the timestamp associated with the given sensor data frame in order to provide fused data. The model may be trained using the fused data such that the model is configured to, in response to receiving fused data, the model outputs an action label for each object bounding box of the fused data. This output may then be used to control a vehicle in an autonomous driving mode.
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公开(公告)号：US20210103744A1
公开(公告)日：2021-04-08
申请号：US17063553
申请日：2020-10-05
Applicant: Waymo LLC
Inventor： Jiyang Gao , Zijian Guo , Congcong Li
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a spatio-temporal embedding of a sequence of point clouds. One of the methods includes obtaining a temporal sequence comprising a respective point cloud input corresponding to each of a plurality of time points, each point cloud input comprising point cloud data generated from sensor data captured by one or more sensors of a vehicle at the respective time point; processing each point cloud input using a first neural network to generate a respective spatial embedding that characterizes the point cloud input; and processing the spatial embeddings of the point cloud inputs using a second neural network to generate a spatio-temporal embedding that characterizes the point cloud inputs in the temporal sequence.
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Patent Agency Ranking
公开(公告)号：US10699141B2
公开(公告)日：2020-06-30
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：US11987265B1
公开(公告)日：2024-05-21
申请号：US17387852
申请日：2021-07-28
Applicant: Waymo LLC
Inventor： Hang Zhao , Jiyang Gao , Chen Sun , Yi Shen , Yuning Chai , Cordelia Luise Schmid , Congcong Li , Benjamin Sapp , Dragomir Anguelov , Tian Lan , Yue Shen
CPC classification number: B60W60/001 , G06N3/02 , B60W2420/42 , B60W2554/4049
Abstract: A system obtains scene context data characterizing the environment. The scene context data includes data that characterizes a trajectory of an agent in a vicinity of a vehicle up to a current time point. The system identifies a plurality of initial target locations, and generates, for each of a plurality of target locations that each corresponds to one of the initial target locations, a respective predicted likelihood score that represents a likelihood that the target location will be an intended final location for a future trajectory of the agent. For each target location in a first subset of the target locations, the system generates a predicted future trajectory for the agent given that the target location is the intended final location for the future trajectory. The system further selects, as likely future trajectories of the agent, one or more of the predicted future trajectories.
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公开(公告)号：US11880758B1
公开(公告)日：2024-01-23
申请号：US17391627
申请日：2021-08-02
Applicant: Waymo LLC
Inventor： Congcong Li , Ury Zhilinsky , Yun Jiang , Zhaoyin Jia
Abstract: Disclosed herein are neural networks for generating target classifications for an object from a set of input sequences. Each input sequence includes a respective input at each of multiple time steps, and each input sequence corresponds to a different sensing subsystem of multiple sensing subsystems. For each time step in the multiple time steps and for each input sequence in the set of input sequences, a respective feature representation is generated for the input sequence by processing the respective input from the input sequence at the time step using a respective encoder recurrent neural network (RNN) subsystem for the sensing subsystem that corresponds to the input sequence. For each time step in at least a subset of the multiple time steps, the respective feature representations are processed using a classification neural network subsystem to select a respective target classification for the object at the time step.
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公开(公告)号：US11830203B2
公开(公告)日：2023-11-28
申请号：US17728682
申请日：2022-04-25
Applicant: Waymo LLC
Inventor： Ruichi Yu , Sachithra Madhawa Hemachandra , Ian James Mahon , Congcong Li
CPC classification number: G06T7/248 , G06N3/045 , G06N3/084 , G05D1/0088 , G06T2207/20084 , G06T2207/30252
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating a new measurement of an object surrounding a vehicle with a maintained track. One of the methods includes receiving an object track for a particular object, receiving a new measurement characterizing a new object at a new time step, and determining whether the new object is the same as the particular object, comprising: generating a representation of the new object at the new and preceding time steps; generating a representation of the particular object at the new and preceding time steps; processing a first network input comprising the representations using a first neural network to generate an embedding of the first network input; and processing the embedding of the first network input using a second neural network to generate a predicted likelihood that the new object and the particular object are the same.
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公开(公告)号：US20220366263A1
公开(公告)日：2022-11-17
申请号：US17313655
申请日：2021-05-06
Applicant: Waymo LLC
Inventor： Ming Ji , Edward Stephen Walker, JR. , Yang Song , Zijian Guo , Congcong Li
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student machine learning model using a teacher machine learning model that has a pre-trained feature extractor. In one aspect, a method includes obtaining data specifying the teacher machine learning model that is configured to perform a machine learning task; obtaining first training data; training the teacher machine learning model on the first training data to obtain a trained teacher machine learning model; generating second, automatically labeled training data by using the trained teacher machine learning model to process unlabeled training data; and training a student machine learning model to perform the machine learning task using at least the second, automatically labeled training data, wherein the student machine learning model does not include the pre-trained feature extractor and instead includes a different feature extractor having fewer parameters than the pre-trained feature extractor.
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