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公开(公告)号：US11774583B2
公开(公告)日：2023-10-03
申请号：US17187269
申请日：2021-02-26
Applicant: Waymo LLC
Inventor： Nicholas Armstrong-Crews
IPC: G01S13/89 , G01S13/931
CPC classification number: G01S13/89 , G01S13/931
Abstract: Example embodiments relate to self-reflection filtering techniques within radar data. A computing device may use radar data to determine a first radar representation that conveys information about surfaces in a vehicle's environment. The computing device may use a predefined model to generate a second radar representation that assigns predicted self-reflection values to respective locations of the environment based on the information about the surfaces conveyed by the first radar representation. The predefined model can enable a predefined self-reflection value to be assigned to a first location based on information about a surface positioned at a second location and a relationship between the first location and the second location. The computing device may then modify the first radar representation based on the predicted self-reflection values in the second radar representation and provide instructions to a control system of the vehicle based on modifying the first radar representation.
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公开(公告)号：US20220276375A1
公开(公告)日：2022-09-01
申请号：US17187269
申请日：2021-02-26
Applicant: Waymo LLC
Inventor： Nicholas Armstrong-Crews
IPC: G01S13/89 , G01S13/931
Abstract: Example embodiments relate to self-reflection filtering techniques within radar data. A computing device may use radar data to determine a first radar representation that conveys information about surfaces in a vehicle's environment. The computing device may use a predefined model to generate a second radar representation that assigns predicted self-reflection values to respective locations of the environment based on the information about the surfaces conveyed by the first radar representation. The predefined model can enable a predefined self-reflection value to be assigned to a first location based on information about a surface positioned at a second location and a relationship between the first location and the second location. The computing device may then modify the first radar representation based on the predicted self-reflection values in the second radar representation and provide instructions to a control system of the vehicle based on modifying the first radar representation.
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公开(公告)号：US20210150280A1
公开(公告)日：2021-05-20
申请号：US16833018
申请日：2020-03-27
Applicant: Waymo LLC
Inventor： Brandyn White , Congyu Gao , Sean Rafferty , Nicholas Armstrong-Crews
Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.
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公开(公告)号：US20250162615A1
公开(公告)日：2025-05-22
申请号：US19034427
申请日：2025-01-22
Applicant: Waymo LLC
Inventor： Nicholas Armstrong-Crews , Mingcheng Chen
Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling motion pattern-assisted object classification of objects in an environment of an autonomous vehicle (AV) by obtaining, from a sensing system of the AV, a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying an association of the plurality of return points with an object in an environment of the AV, identifying, in view of the one or more velocity values of at least some of the plurality of return points, a type of the object or a type of a motion of the object, and causing a driving path of the AV to be determined in view of the identified type of the object.
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公开(公告)号：US12233905B2
公开(公告)日：2025-02-25
申请号：US17087470
申请日：2020-11-02
Applicant: Waymo LLC
Inventor： Nicholas Armstrong-Crews , Mingcheng Chen
Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling motion pattern-assisted object classification of objects in an environment of an autonomous vehicle (AV) by obtaining, from a sensing system of the AV, a plurality of return points, each return point comprising one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying an association of the plurality of return points with an object in an environment of the AV, identifying, in view of the one or more velocity values of at least some of the plurality of return points, a type of the object or a type of a motion of the object, and causing a driving path of the AV to be determined in view of the identified type of the object.
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Patent Agency Ranking
公开(公告)号：US20240402319A1
公开(公告)日：2024-12-05
申请号：US18799025
申请日：2024-08-09
Applicant: Waymo LLC
Inventor： Blaise Gassend , Nicholas Armstrong-Crews , Scott McCloskey
Abstract: One example method involves rotating a housing of a light detection and ranging (LIDAR) device about a first axis. The housing includes a first optical window and a second optical window. The method also involves transmitting a first plurality of light pulses through the first optical window to obtain a first scan of a field-of-view (FOV) of the LIDAR device. The method also involves transmitting a second plurality of light pulses through the second optical window to obtain a second scan of the FOV. The method also involves identifying, based on the first scan and the second scan, a portion of the FOV that is at least partially occluded by an occlusion.
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公开(公告)号：US12050267B2
公开(公告)日：2024-07-30
申请号：US16949657
申请日：2020-11-09
Applicant: Waymo LLC
Inventor： Nicholas Armstrong-Crews , Mingcheng Chen , Xiaoxiang Hu , Colin Andrew Braley , Yunshan Jiang
CPC classification number: G01S17/89 , B60W40/02 , G01S17/26 , G01S17/58 , B60W2420/408
Abstract: Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling efficient object identification and tracking in autonomous vehicle (AV) applications by using velocity data-assisted mapping of first set of points obtained for a first sensing data frame by a sensing system of the AV to a second set of points obtained for a second sensing data frame by the sensing system of the AV, the first set of points and the second set of points corresponding to an object in an environment of the AV, and causing a driving path of the AV to be determined in view of the performed mapping.
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公开(公告)号：US11656358B2
公开(公告)日：2023-05-23
申请号：US16671858
申请日：2019-11-01
Applicant: Waymo LLC
Inventor： Blaise Gassend , Nicholas Armstrong-Crews , Andreas Wendel , Benjamin Ingram , Clayton Kunz
CPC classification number: G01S17/89 , G01S7/4817 , G01S17/10
Abstract: One example system includes a first light detection and ranging (LIDAR) device that scans a first field-of-view defined by a first range of pointing directions associated with the first LIDAR device. The system also includes a second LIDAR device that scans a second FOV defined by a second range of pointing directions associated with the second LIDAR device. The second FOV at least partially overlaps the first FOV. The system also includes a first controller that adjusts a first pointing direction of the first LIDAR device. The system also includes a second controller that adjusts a second pointing direction of the second LIDAR device synchronously with the adjustment of the first pointing direction of the first LIDAR device.
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公开(公告)号：US20230019893A1
公开(公告)日：2023-01-19
申请号：US17947563
申请日：2022-09-19
Applicant: Waymo LLC
Inventor： Brandyn White , Congyu Gao , Sean Rafferty , Nicholas Armstrong-Crews
Abstract: Aspects of the disclosure provide for automatically generating labels for sensor data. For instance first sensor data for a first vehicle is identified. The first sensor data is defined in both a global coordinate system and a local coordinate system for the first vehicle. A second vehicle is identified based on a second location of the second vehicle within a threshold distance of the first vehicle within the first timeframe. The second vehicle is associated with second sensor data that is further associated with a label identifying a location of an object, and the location of the object is defined in a local coordinate system of the second vehicle. A conversion from the local coordinate system of the second vehicle to the local coordinate system of the first vehicle may be determined and used to transfer the label from the second sensor data to the first sensor data.
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20.发明申请公开(公告)号：US20220024484A1
公开(公告)日：2022-01-27
申请号：US16934132
申请日：2020-07-21
Applicant: WAYMO LLC
Inventor： Nicholas Armstrong-Crews
Abstract: The technology relates to determining a source of a horn honk or other noise in an environment around one or more self-driving vehicles. Aspects of the technology leverage real-time information from a group of self-driving vehicles regarding received acoustical information. The location and pose of each self-driving vehicle in the group, along with the precise arrangement of acoustical sensors on each vehicle, can be used to triangulate or otherwise identify the actual location in the environment for the origin of the horn honk or other sound. Other sensor information, map data, and additional data can be used narrow down or refine the location of a likely noise source. Once the location and source of the noise is known, each self-driving vehicle can use that information to modify current driving operations and/or use it as part of a reinforcement learning approach for future driving situations.
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