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公开(公告)号：US12231674B2
公开(公告)日：2025-02-18
申请号：US18101851
申请日：2023-01-26
Applicant: Zoox, Inc.
Inventor： Philippe Martin Burlina , Subhasis Das
IPC: H04N19/52 , G06V10/26 , G06V10/764 , G06V10/77 , G06V20/56
Abstract: Techniques are described herein for encoding of an input data, to handle both variable bitrate requirements and varying importance of content of different portions of the input data. The encoding vectors may be based on a subset of data from the input data. Potential distortion in the reconstruction on a decoder side may be alleviated by transmitting a difference dataset as a complement to encoding vectors encoded from the input data. The difference dataset may be determined taking into account the importance of content of different portions of the input image to reduce the size, for example by masking out portion of the input data that is considered less important. The difference dataset may be compressed based on an available bandwidth.
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公开(公告)号：US20240282115A1
公开(公告)日：2024-08-22
申请号：US18651548
申请日：2024-04-30
Applicant: Zoox, Inc.
Inventor： Adrian Michael Costantino , Subhasis Das , Francesco Papi
Abstract: A vehicle computing system may implement techniques to determine whether two objects in an environment are related as an articulated object. The techniques may include applying heuristics and algorithms to object representations (e.g., bounding boxes) to determine whether two objects are related as a single object with two portions that articulate relative to each other. The techniques may include predicting future states of the articulated object in the environment. One or more model(s) may be used to determine presence of the articulated object and/or predict motion of the articulated object in the future. Based on the presence and/or motion of the articulated object, the vehicle computing system may control operation of the vehicle.
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公开(公告)号：US20240241257A1
公开(公告)日：2024-07-18
申请号：US18417356
申请日：2024-01-19
Applicant: Zoox, Inc.
Inventor： Subhasis Das , Chuang Wang , Sabeek Mani Pradhan
CPC classification number: G01S17/89 , G01C21/3492 , G01S19/393 , G06N20/00
Abstract: Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive image data, lidar data, and/or radar data to determine information about an object in the environment. As different sensors may be associated with different time periods for capturing and/or processing operations, the techniques include updating object data with data from sensors associated with a shorter time period to generate intermediate object data. Such intermediate object data may reduce a delay in updating a position of an object in an environment, which may improve reaction time(s) and/or safety outcomes in systems implementing such perception systems, such as an autonomous vehicle.
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公开(公告)号：US12039784B1
公开(公告)日：2024-07-16
申请号：US17491301
申请日：2021-09-30
Applicant: Zoox, Inc.
Inventor： Adrian Michael Costantino , Subhasis Das , Francesco Papi
Abstract: A vehicle computing system may implement techniques to determine whether two objects in an environment are related as an articulated object. The techniques may include applying heuristics and algorithms to object representations (e.g., bounding boxes) to determine whether two objects are related as a single object with two portions that articulate relative to each other. The techniques may include predicting future states of the articulated object in the environment. One or more model(s) may be used to determine presence of the articulated object and/or predict motion of the articulated object in the future. Based on the presence and/or motion of the articulated object, the vehicle computing system may control operation of the vehicle.
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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
Applicant: Zoox, Inc.
Inventor： Subhasis Das , Francesco Papi , Shida Shen
CPC classification number: G05D1/0212 , G05D1/0238 , G05D1/0246 , G05D1/0257 , G05D2201/0212 , G05D2201/0213
Abstract: 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：US11814084B2
公开(公告)日：2023-11-14
申请号：US17553938
申请日：2021-12-17
Applicant: Zoox, Inc.
Inventor： Subhasis Das , Jifei Qian , Liujiang Yan
IPC: B60W60/00 , G06V10/40 , G06T7/246 , G06V20/58 , G01S17/931 , G01S13/931 , G01S13/86
CPC classification number: B60W60/0027 , G01S13/865 , G01S13/867 , G01S13/931 , G01S17/931 , G06T7/246 , G06V10/40 , G06V20/58 , B60W2420/42 , B60W2420/52 , G06T2207/10028 , G06T2207/20081 , G06T2207/30252
Abstract: Techniques for determining an output from a plurality of sensor modalities are discussed herein. Features from a radar sensor, a lidar sensor, and an image sensor may be input into respective models to determine respective intermediate outputs associated with a tracks associated with an object and associated confidence levels. The Intermediate outputs from a radar model, a lidar model, and an vision model may be input into a fused model to determine a fused confidence level and fused output associated with the track. The fused confidence level and the individual confidence levels are compared to a threshold to generate the track to transmit to a planning system or prediction system of an autonomous vehicle. Additionally, a vehicle controller can control the autonomous vehicle based on the track and/or on the confidence level(s).
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公开(公告)号：US11741274B1
公开(公告)日：2023-08-29
申请号：US17100787
申请日：2020-11-20
Applicant: Zoox, Inc.
Inventor： Andrew Scott Crego , Sai Anurag Modalavalasa , Subhasis Das , Siavosh Rezvan Behbahani , Aditya Pramod Khadilkar
Abstract: Fast simulation of a scenario (e.g., simulating the scenario once as opposed to multiple times) to determine performance metric(s) of a configuration of one or more components of an autonomous vehicle may include training a perception error model based at least in part on a difference between a prediction output by a perception component associated with a future time and a perception output associated with that future time once that future time has arrived. A contour or heat map output by the perception error model may be used to determine one or more performance metric(s) associated with a component of the autonomous vehicle and identify which component may cause a degradation of a performance metric.
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公开(公告)号：US11714423B2
公开(公告)日：2023-08-01
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 , G01S17/86 , G01S17/931 , G06V20/56 , G01S13/86 , G01S15/931 , G01S13/931 , G01S13/72 , G01S15/86
CPC classification number: G05D1/024 , G01S17/58 , G01S17/66 , G01S17/86 , G01S17/931 , G05D1/0212 , G06T7/11 , G06T7/187 , G06V20/56 , G01S13/726 , G01S13/862 , G01S13/865 , G01S13/867 , G01S13/931 , G01S15/86 , G01S15/931 , 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：US20210181758A1
公开(公告)日：2021-06-17
申请号：US16779576
申请日：2020-01-31
Applicant: Zoox, Inc.
Inventor： Subhasis Das , Benjamin Isaac Zwiebel , Kai Yu , James William Vaisey Philbin
IPC: G05D1/02 , G01S17/89 , G06T7/215 , G06T7/246 , G06T7/292 , G01S17/931 , G01S13/89 , G01S13/931
Abstract: Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.
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10.发明公开
公开(公告)号：US20240320985A1
公开(公告)日：2024-09-26
申请号：US18621922
申请日：2024-03-29
Applicant: Zoox, Inc.
Inventor： Subhasis Das , Oytun Ulutan , Yi-Ting Lin , Derek Xiang Ma
Abstract: A system for faster object attribute and/or intent classification may include an machine-learned (ML) architecture that processes temporal sensor data (e.g., multiple instances of sensor data received at different times) and includes a cache in an intermediate layer of the ML architecture. The ML architecture may be capable of classifying an object's intent to enter a roadway, idling near a roadway, or active crossing of a roadway. The ML architecture may additionally or alternatively classify indicator states, such as indications to turn, stop, or the like. Other attributes and/or intentions are discussed herein.
