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公开(公告)号：US12258040B2
公开(公告)日：2025-03-25
申请号：US17854849
申请日：2022-06-30
Applicant: Zoox, Inc.
Inventor： Gowtham Garimella , Gary Linscott , Ethan Miller Pronovost
IPC: B60W60/00 , B60W30/095 , B60W40/04 , B60W50/00
Abstract: Techniques for improving operational decisions of an autonomous vehicle are discussed herein. In some cases, a system may generate reference graphs associated with a route of the autonomous vehicle. Such reference graphs can comprise precomputed feature vectors based on grid regions and/or lane segments. The feature vectors are usable to determine scene context data associated with static objects to reduce computational expenses and compute time.
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公开(公告)号：US11958554B2
公开(公告)日：2024-04-16
申请号：US16949642
申请日：2020-11-09
Applicant: Zoox, Inc.
Inventor： Joseph Funke , Gowtham Garimella , Marin Kobilarov , Chuang Wang
CPC classification number: B62D6/003 , B62D5/046 , B62D6/001 , G05D1/0088 , G05D1/0221
Abstract: Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications system components. Generally, steering actuator dynamics can be difficult to model due to, for example, an integrated power steering control module, proprietary black box controls, etc. Further, it is difficult to capture the complex interplay of non-linear interactions, such as power steering, tire forces, etc. with sufficient accuracy. To overcome this limitation, a recurring neural network can be employed to model the steering dynamics of an autonomous vehicle. The resulting model can be used to generate feedforward steering commands for embedded control. Such a neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principle model when used for controlling the steering system of a self-driving vehicle.
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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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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：US11554790B2
公开(公告)日：2023-01-17
申请号：US16870083
申请日：2020-05-08
Applicant: Zoox, Inc.
Inventor： Kenneth Michael Siebert , Gowtham Garimella , Samir Parikh
Abstract: Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include inputting data into a model and receiving an output from the model representing a discretized representation. The discretized representation may be associated with a probability of an object reaching a location in the environment at a future time. A vehicle computing system may determine a trajectory and a weight associated with the trajectory using the discretized representation and the probability. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory and the weight output by the vehicle computing system.
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Patent Agency Ranking
公开(公告)号：US11276179B2
公开(公告)日：2022-03-15
申请号：US16719780
申请日：2019-12-18
Applicant: Zoox, Inc.
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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公开(公告)号：US20210347383A1
公开(公告)日：2021-11-11
申请号：US16870355
申请日：2020-05-08
Applicant: Zoox, Inc.
Inventor： Kenneth Michael Siebert , Gowtham Garimella , Benjamin Isaac Mattinson , Samir Parikh , Kai Zhenyu Wang
Abstract: Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.
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公开(公告)号：US20210192748A1
公开(公告)日：2021-06-24
申请号：US16719780
申请日：2019-12-18
Applicant: Zoox, Inc.
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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公开(公告)号：US20250162612A1
公开(公告)日：2025-05-22
申请号：US18516779
申请日：2023-11-21
Applicant: Zoox, Inc.
Inventor： Gregory Michael Woelki , Xiaosi Zeng , Gowtham Garimella
IPC: B60W60/00
Abstract: A machine-learned architecture may predict multiple paths that an object could take in the future without regard to time at which the object may occupy positions identified by one of those paths. These time-invariant paths may be used by an autonomous vehicle to filter detected objects by relevance to an autonomous vehicle's plans, improve prediction of an object's reaction to a vehicle candidate trajectory, determine right-of-way between object(s) and the autonomous vehicle, match detected objects to lanes, and/or improve prediction of odd or out-of-turn object behavior of an object.
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公开(公告)号：US12065171B2
公开(公告)日：2024-08-20
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 , G05D1/0274 , G06N3/08 , G06N5/04 , B60W2552/53 , B60W2554/20 , B60W2554/402 , B60W2554/4041 , B60W2554/4042 , B60W2554/4043 , B60W2554/80 , B60W2555/20 , B60W2555/60
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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