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公开(公告)号：US20220092983A1
公开(公告)日：2022-03-24
申请号：US17542880
申请日：2021-12-06
Applicant: Zoox, Inc.
Inventor： Xi Joey Hong , Benjamin John Sapp , James William Vaisey Philbin , Kai Zhenyu Wang
Abstract: Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.
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公开(公告)号：US20210271901A1
公开(公告)日：2021-09-02
申请号：US17325562
申请日：2021-05-20
Applicant: Zoox, Inc.
Abstract: Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.
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公开(公告)号：US20210053570A1
公开(公告)日：2021-02-25
申请号：US16549704
申请日：2019-08-23
Applicant: Zoox, Inc.
Inventor： Abishek Krishna Akella , Vasiliy Karasev , Kai Zhenyu Wang , Rick Zhang
Abstract: Techniques for determining a vehicle action and controlling a vehicle to perform the vehicle action for navigating the vehicle in an environment can include determining a vehicle action, such as a lane change action, for a vehicle to perform in an environment. The vehicle can detect, based at least in part on sensor data, an object associated with a target lane associated with the lane change action sensor data. In some instances, the vehicle may determine attribute data associated with the object and input the attribute data to a machine-learned model that can output a yield score. Based on such a yield score, the vehicle may determine whether it is safe to perform the lane change action.
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公开(公告)号：US12183204B2
公开(公告)日：2024-12-31
申请号：US17542880
申请日：2021-12-06
Applicant: Zoox, Inc.
Inventor： Xi Joey Hong , Benjamin John Sapp , James William Vaisey Philbin , Kai Zhenyu Wang
Abstract: Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.
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公开(公告)号：US11734832B1
公开(公告)日：2023-08-22
申请号：US17649690
申请日：2022-02-02
Applicant: Zoox, Inc.
CPC classification number: G06T7/20 , G05D1/0221 , G05D1/0231 , G06N3/045 , G06N3/08 , G06T7/70 , G06T11/20 , G06V20/58 , G06V30/274 , G05D2201/0213 , G06T2207/20084 , G06T2207/30261 , G06T2210/12
Abstract: Techniques for determining predictions on a top-down representation of an environment based on object movement are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) may capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle, a pedestrian, a bicycle). A multi-channel image representing a top-down view of the object(s) and the environment may be generated based in part on the sensor data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) may also be encoded in the image. Multiple images may be generated representing the environment over time and input into a prediction system configured to output a trajectory template (e.g., general intent for future movement) and a predicted trajectory (e.g., more accurate predicted movement) associated with each object. The prediction system may include a machine learned model configured to output the trajectory template(s) and the predicted trajector(ies).
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Patent Agency Ranking
公开(公告)号：US20230159059A1
公开(公告)日：2023-05-25
申请号：US17535357
申请日：2021-11-24
Applicant: Zoox, Inc.
Inventor： Gowtham Garimella , Marin Kobilarov , Andres Guillermo Morales Morales , Ethan Miller Pronovost , Kai Zhenyu Wang , Xiaosi Zeng
CPC classification number: B60W60/0027 , G06N3/08 , G06N5/04 , G05D1/0274 , B60W2554/80 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/402 , B60W2554/20 , B60W2555/60 , B60W2555/20 , B60W2552/53
Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a predicted position of the object at a subsequent timestep. Further, a predicted trajectory of the object may be determined using predicted positions of the object at various timesteps.
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公开(公告)号：US11472442B2
公开(公告)日：2022-10-18
申请号：US16856826
申请日：2020-04-23
Applicant: Zoox, Inc.
Inventor： Pengfei Duan , James William Vaisey Philbin , Cooper Stokes Sloan , Sarah Tariq , Feng Tian , Chuang Wang , Kai Zhenyu Wang , Yi Xu
Abstract: Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.
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公开(公告)号：US11460850B1
公开(公告)日：2022-10-04
申请号：US16412127
申请日：2019-05-14
Applicant: Zoox, Inc.
Inventor： Vasiliy Karasev , James William Vaisey Philbin , Sarah Tariq , Kai Zhenyu Wang
Abstract: A trajectory estimate of a wheeled vehicle can be determined based at least in part on determining a wheel angle associated with the vehicle. In some examples, at least a portion of the image associated with the wheeled vehicle may be input into a machine-learned model that is trained to classify and/or regress wheel directions of wheeled vehicles. The machine-learned model may output a predicted wheel direction. The wheel direction and/or additional or historical sensor data may be used to estimate a trajectory of the wheeled vehicle. The predicted trajectory of the object can then be used to generate and refine an autonomous vehicle's trajectory as the autonomous vehicle proceeds through the environment.
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公开(公告)号：US11380108B1
公开(公告)日：2022-07-05
申请号：US16586646
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Tianyi Cai , James William Vaisey Philbin , Kai Zhenyu Wang
Abstract: The described techniques relate to predicting object behavior based on top-down representations of an environment comprising top-down representations of image features in the environment. For example, a top-down representation may comprise a multi-channel image that includes semantic map information along with additional information for a target object and/or other objects in an environment. A top-down image feature representation may also be a multi-channel image that incorporates various tensors for different image features with channels of the multi-channel image, and may be generated directly from an input image. A prediction component can generate predictions of object behavior based at least in part on the top-down image feature representation, and in some cases, can generate predictions based on the top-down image feature representation together with the additional top-down representation.
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公开(公告)号：US11280630B2
公开(公告)日：2022-03-22
申请号：US16698366
申请日：2019-11-27
Applicant: Zoox, Inc.
Inventor： Christopher James Gibson , Kai Zhenyu Wang
Abstract: Techniques are disclosed for updating map data. The techniques may include detecting a traffic light in a first image, determining, based at least in part on the traffic light detected in the first image, a proposed three-dimensional position of the traffic light in a three-dimensional coordinate system associated with map data. The proposed three-dimensional position may then be projected into a second image to determine a two-dimensional position of the traffic light in the second image and the second image may be annotated, as an annotated image, with a proposed traffic light location indicator associated with the traffic light. The techniques further include causing a display to display the annotated image to a user, receiving user input associated with the annotated images, and updating, as updated map data, the map data to include a position of the traffic light in the map data based at least in part on the user input.
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