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公开(公告)号：US20190049242A1
公开(公告)日：2019-02-14
申请号：US15675487
申请日：2017-08-11
Applicant: Zoox, Inc.
Inventor： Derek Adams , Ian Baldwin , Bertrand Robert Douillard , Jesse Sol Levinson
IPC: G01B21/16
Abstract: Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.
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公开(公告)号：US20170123428A1
公开(公告)日：2017-05-04
申请号：US14756991
申请日：2015-11-04
Applicant: Zoox, Inc.
Inventor： Jesse Sol Levinson , Timothy David Kentley , Bertrand Robert Douillard
CPC classification number: G05D1/0214 , B60W2550/12 , G01S7/497 , G01S13/06 , G01S13/86 , G01S17/023 , G01S17/42 , G05D1/0088 , G05D1/0231 , G05D1/024 , G05D1/0248 , G05D1/0257 , G05D1/0274 , G05D1/0276 , G05D1/0291 , G05D2201/0213 , G07C5/08 , G07C5/0816
Abstract: Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include receiving an indication of a sensor anomaly, determining one or more sensor recovery strategies based on the sensor anomaly, and executing a course of action that ensures the autonomous vehicle system operates within accepted parameters. Alternative sensors may be relied upon to cover for the sensor anomaly, which may include a failed sensor while the autonomous vehicle is in operation.
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公开(公告)号：US20240318957A1
公开(公告)日：2024-09-26
申请号：US18652390
申请日：2024-05-01
Applicant: Zoox, Inc.
Inventor： Derek Adams , Ian Baldwin , Bertrand Robert Douillard , Jesse Sol Levinson
IPC: G01B21/16 , G01C21/16 , G01C21/36 , G01C25/00 , G01S7/40 , G01S7/497 , G01S13/06 , G01S13/86 , G01S17/06 , G01S17/87 , G01S17/88
CPC classification number: G01B21/16 , G01C21/1652 , G01C21/1656 , G01C21/3602 , G01C25/00 , G01S7/4972 , G01S17/06 , G01S17/87 , G01S17/88 , G01S7/4026 , G01S13/06 , G01S13/86
Abstract: Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.
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公开(公告)号：US11753003B2
公开(公告)日：2023-09-12
申请号：US16785219
申请日：2020-02-07
Applicant: Zoox, Inc.
Inventor： Adam Berger , Ryan McMichael , Bertrand Robert Douillard
CPC classification number: B60W30/09 , G01S17/42 , G01S17/931 , G05D1/0088 , G05D1/0214 , G05D1/0236 , G08G1/16 , B60W2420/52 , B60W2510/20 , B60W2554/00 , B60W2710/20
Abstract: A LIDAR system includes a laser emitter configured to emit a laser pulse in a sample direction of a sample area of a scene. A sensor element of the LIDAR system is configured to sense a return pulse, which is a reflection from the sample area corresponding to the emitted laser pulse. The LIDAR system may compare a width of the emitted laser pulse to a width of the return pulse in the time-domain. The comparison of the width of the emitted pulse to the width of the return pulse may be used to determine an orientation or surface normal of the sample area relative to the sample direction. Such a comparison leads to a measurement of the change of pulse width, referred to as pulse broadening or pulse stretching, from the emitted pulse to the return pulse.
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公开(公告)号：US10593042B1
公开(公告)日：2020-03-17
申请号：US15484401
申请日：2017-04-11
Applicant: Zoox, Inc.
Inventor： Bertrand Robert Douillard , Subhasis Das , Zeng Wang , Dragomir Dimitrov Anguelov
Abstract: Multi-dimensional data can be mapped to a projection shape and converted for image analysis. In some examples, the multi-dimensional data may include data captured by a LIDAR system for use in conjunction with a perception system for an autonomous vehicle. Converting operations can include converting three-dimensional LIDAR data to multi-channel two-dimensional data. Data points of the multi-dimensional data can be mapped to a projection shape, such as a sphere. Characteristics of the projection shape may include a shape, a field of view, a resolution, and a projection type. After data is mapped to the projection shape, the projection shape can be converted to a multi-channel, two-dimensional image. Image segmentation and classification may be performed on the two-dimensional data. Further, segmentation information may be used to segment the three-dimensional LIDAR data, while a rendering plane may be positioned relative to the segmented data to perform classification on a per-object basis.
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Patent Agency Ranking
公开(公告)号：US20200026292A1
公开(公告)日：2020-01-23
申请号：US16584392
申请日：2019-09-26
Applicant: Zoox, Inc.
Inventor： Bertrand Robert Douillard , Subhasis Das , Zeng Wang , Dragomir Dimitrov Anguelov , Jesse Sol Levinson
IPC: G05D1/02 , G01S17/58 , G06T7/187 , G01S17/66 , G01S17/02 , G06T7/11 , G01S17/89 , G01S17/93 , G06K9/00
Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.
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公开(公告)号：US10509947B1
公开(公告)日：2019-12-17
申请号：US15484365
申请日：2017-04-11
Applicant: Zoox, Inc.
Inventor： Bertrand Robert Douillard , Subhasis Das , Zeng Wang , Dragomir Dimitrov Anguelov
Abstract: Multi-dimensional data can be mapped to a projection shape and converted for image analysis. In some examples, the multi-dimensional data may include data captured by a LIDAR system for use in conjunction with a perception system for an autonomous vehicle. Converting operations can include converting three-dimensional LIDAR data to multi-channel two-dimensional data. Data points of the multi-dimensional data can be mapped to a projection shape, such as a sphere. Characteristics of the projection shape may include a shape, a field of view, a resolution, and a projection type. After data is mapped to the projection shape, the projection shape can be converted to a multi-channel, two-dimensional image. Image segmentation and classification may be performed on the two-dimensional data. Further, segmentation information may be used to segment the three-dimensional LIDAR data, while a rendering plane may be positioned relative to the segmented data to perform classification on a per-object basis.
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公开(公告)号：US10444759B2
公开(公告)日：2019-10-15
申请号：US15622905
申请日：2017-06-14
Applicant: Zoox, Inc.
Inventor： Bertrand Robert Douillard , Subhasis Das , Zeng Wang , Dragomir Dimitrov Anguelov , Jesse Sol Levinson
IPC: G05D1/02 , G01S17/89 , G01S17/93 , G06K9/00 , G06T7/11 , G01S17/02 , G01S17/58 , G01S17/66 , G06T7/187 , G01S15/93 , G01S15/02 , G01S13/72 , G01S13/86 , G01S13/93
Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.
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公开(公告)号：US20180364717A1
公开(公告)日：2018-12-20
申请号：US15622905
申请日：2017-06-14
Applicant: Zoox, Inc.
Inventor： Bertrand Robert Douillard , Subhasis Das , Zeng Wang , Dragomir Dimitrov Anguelov , Jesse Sol Levinson
CPC classification number: G05D1/024 , G01S13/726 , G01S13/862 , G01S13/865 , G01S13/867 , G01S13/931 , G01S15/025 , G01S15/931 , G01S17/023 , G01S17/58 , G01S17/66 , G01S17/89 , G01S17/936 , G05D1/0212 , G06K9/00791 , G06T7/11 , G06T7/187 , G06T2207/10028 , G06T2207/30252
Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.
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公开(公告)号：US11966230B1
公开(公告)日：2024-04-23
申请号：US17125388
申请日：2020-12-17
Applicant: Zoox, Inc.
Inventor： Greg Woelki , Kai Zhenyu Wang , Bertrand Robert Douillard , Michael Haggblade , James William Vaisey Philbin
CPC classification number: G05D1/0221 , B60W60/0027 , B60W60/005 , G05D1/0214 , G05D1/0231 , G05D1/0276 , G06N7/01 , G06N20/00 , G06V20/58 , B60W2420/42 , B60W2554/4026 , B60W2554/4029 , B60W2554/404 , B60W2556/10 , B60W2556/45 , G05D2201/0213
Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.
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