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公开(公告)号：US11906967B1
公开(公告)日：2024-02-20
申请号：US16836568
申请日：2020-03-31
申请人： Zoox, Inc.
发明人： Subhasis Das , Francesco Papi , Shida Shen
CPC分类号： G05D1/0212 , G05D1/0238 , G05D1/0246 , G05D1/0257 , G05D2201/0212 , G05D2201/0213
摘要： Techniques to use a trained model to determine a yaw of an object are described. For example, a system may implement various techniques to generate multiple representations for an object in an environment. Each representation vary based on the technique and data used. An estimation component may estimate a representation from the multiple representations. The model may be implemented to output a yaw for the object using the multiple representations, the estimated representation, and/or additional information. The output yaw may be used to track an object, generate a trajectory, or otherwise control a vehicle.
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公开(公告)号：US11537819B1
公开(公告)日：2022-12-27
申请号：US16862911
申请日：2020-04-30
申请人： Zoox, Inc.
发明人： Subhasis Das , Shida Shen
摘要： Techniques are disclosed for a covariance model that may generate observation covariances based on observation data of object detections. Techniques may include determining observation data for an object detection of an object represented in sensor data, determining that track data of a track is associated with the object, and inputting the observation data associated with the object detection into a machine-learned model configured to output a covariance (a covariance model). The covariance model may output one or more observation covariance values for the observation data. In some examples, the techniques may include determining updated track data based on the track data, the one or more observation covariance values, and the observation data.
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公开(公告)号：US11625041B2
公开(公告)日：2023-04-11
申请号：US16797656
申请日：2020-02-21
申请人： Zoox, Inc.
发明人： Subhasis Das , Shida Shen , Kai Yu , Benjamin Isaac Zwiebel
摘要： Techniques are disclosed for a combined machine learned (ML) model that may generate a track confidence metric associated with a track and/or a classification of an object. Techniques may include obtaining a track. The track, which may include object detections from one or more sensor data types and/or pipelines, may be input into a machine-learning (ML) model. The model may output a track confidence metric and a classification. In some examples, if the track confidence metric does not satisfy a threshold, the ML model may cause the suppression of the output of the track to a planning component of an autonomous vehicle.
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公开(公告)号：US20210263525A1
公开(公告)日：2021-08-26
申请号：US16797656
申请日：2020-02-21
申请人： Zoox, Inc.
发明人： Subhasis Das , Shida Shen , Kai Yu , Benjamin Isaac Zwiebel
摘要： Techniques are disclosed for a combined machine learned (ML) model that may generate a track confidence metric associated with a track and/or a classification of an object. Techniques may include obtaining a track. The track, which may include object detections from one or more sensor data types and/or pipelines, may be input into a machine-learning (ML) model. The model may output a track confidence metric and a classification. In some examples, if the track confidence metric does not satisfy a threshold, the ML model may cause the suppression of the output of the track to a planning component of an autonomous vehicle.
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