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公开(公告)号：US11720799B2
公开(公告)日：2023-08-08
申请号：US17406454
申请日：2021-08-19
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
IPC: G06K9/00 , G06N3/084 , G06V20/58 , G06V10/44 , G06V40/10 , G06F18/25 , G06N3/045 , G06V10/80 , G06V10/82 , G06N3/00
CPC classification number: G06N3/084 , G06F18/25 , G06N3/00 , G06N3/045 , G06V10/454 , G06V10/80 , G06V10/82 , G06V20/58 , G06V40/103
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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公开(公告)号：US11113548B2
公开(公告)日：2021-09-07
申请号：US16436754
申请日：2019-06-10
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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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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Patent Agency Ranking
4.发明授权公开(公告)号：US11093819B1
公开(公告)日：2021-08-17
申请号：US15381389
申请日：2016-12-16
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：US20190294896A1
公开(公告)日：2019-09-26
申请号：US16436754
申请日：2019-06-10
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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公开(公告)号：US20210383139A1
公开(公告)日：2021-12-09
申请号：US17406454
申请日：2021-08-19
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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公开(公告)号：US10318827B2
公开(公告)日：2019-06-11
申请号：US15383648
申请日：2016-12-19
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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公开(公告)号：US20180173971A1
公开(公告)日：2018-06-21
申请号：US15383648
申请日：2016-12-19
Applicant: Waymo LLC
Inventor： Zhaoyin Jia , Ury Zhilinsky , Yun Jiang , Yimeng Zhang
CPC classification number: G06K9/00805 , G06K9/00369 , G06K9/4628 , G06K9/6288 , G06N3/00
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating object detection predictions from a neural network. In some implementations, an input characterizing a first region of an environment is obtained. The input includes a projected laser image generated from a three-dimensional laser sensor reading of the first region, a camera image patch generated from a camera image of the first region, and a feature vector of features characterizing the first region. The input is processed using a high precision object detection neural network to generate a respective object score for each object category in a first set of one or more object categories. Each object score represents a respective likelihood that an object belonging to the object category is located in the first region of the environment.
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