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公开(公告)号：US11087173B2
公开(公告)日：2021-08-10
申请号：US16233960
申请日：2018-12-27
发明人： Tingbo Hou , Yan Zhang
摘要： Systems and processes can reduce an amount of training data used to generate a machine learning model while maintaining or improving a resultant of the machine learning model. The amount of training data may be reduced by pre-processing the training data to normalize the training data. The training data may include images of portions of an elongated object, such as a road. Each of the images can be normalized by, for example, rotating each of the images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to reduce the amount of training data and to increase the accuracy of the machine learning model. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to apply data to the machine learning model to, for example, identify lane markings within images.
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公开(公告)号：US11023745B2
公开(公告)日：2021-06-01
申请号：US16233989
申请日：2018-12-27
发明人： Tingbo Hou , Yan Zhang
摘要： Systems and processes can automatically identify lane markings within images through the use of a machine learning model. The machine learning model may use a reduced set of data and output an improved estimate of lane markings by applying normalized data or images to the machine learning model. Each image applied to the model can be normalized by, for example, rotating each of the images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to reduce the amount of data applied to the model or to model generation, and to increase the accuracy of the machine learning model. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to apply data to the machine learning model to, for example, identify lane markings within images.
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公开(公告)号：US12117307B2
公开(公告)日：2024-10-15
申请号：US17208769
申请日：2021-03-22
发明人： Yan Zhang , Tingbo Hou
IPC分类号： G01C21/36 , G01S17/89 , G06F3/04815
CPC分类号： G01C21/3638 , G01C21/3614 , G01S17/89 , G06F3/04815
摘要： Systems and methods are disclosed related to generating an interactive user interface that enables a user to move, rotate or otherwise edit 3D point cloud data in virtual 3D space to align or match point clouds captured from LiDAR scans prior to generation of a high definition map. A system may obtain point cloud data for two or more point clouds, render the point clouds for display in a user interface, then receive a user selection of one of the point clouds and commands from the user to move and/or rotate the selected point cloud. The system may adjust the displayed position of the selected point cloud relative to the other simultaneously displayed point cloud(s) in real time in response to the user commands, and store the adjusted point cloud position data for use in generating a new high definition map.
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公开(公告)号：US20210270628A1
公开(公告)日：2021-09-02
申请号：US17208769
申请日：2021-03-22
发明人： Yan Zhang , Tingbo Hou
IPC分类号： G01C21/36 , G06F3/0481 , G01S17/89
摘要： Systems and methods are disclosed related to generating an interactive user interface that enables a user to move, rotate or otherwise edit 3D point cloud data in virtual 3D space to align or match point clouds captured from LiDAR scans prior to generation of a high definition map. A system may obtain point cloud data for two or more point clouds, render the point clouds for display in a user interface, then receive a user selection of one of the point clouds and commands from the user to move and/or rotate the selected point cloud. The system may adjust the displayed position of the selected point cloud relative to the other simultaneously displayed point cloud(s) in real time in response to the user commands, and store the adjusted point cloud position data for use in generating a new high definition map.
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公开(公告)号：US10990815B2
公开(公告)日：2021-04-27
申请号：US16234130
申请日：2018-12-27
发明人： Tingbo Hou , Yan Zhang
IPC分类号： G06K9/00
摘要： Systems and processes can reduce or divide images of road networks into sub-images that depict straight or substantially straight sections of roads in the road networks. These sub-images or image segments can be normalized by, for example, rotating each of the sub-images such that the depicted roads are horizontal or otherwise share the same angle. By aligning disparate images of roads, it is possible to both reduce the amount of training data used to generate a machine learning model and to increase the accuracy of an automated lane marking or labelling system. Further, the use of normalized images by the machine learning model enables a reduction in computing resources used to perform automated lane marking processes while maintaining or improving accuracy of the lane marking processes.
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