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公开(公告)号：US12252200B2
公开(公告)日：2025-03-18
申请号：US17957756
申请日：2022-09-30
Applicant: Zoox, Inc.
Inventor： Joseph Funke , Liam Gallagher , Marin Kobilarov , Vincent Andreas Laurense , Mark Jonathon McClelland , Sriram Narayanan , Kazuhide Okamoto , Jack Riley , Jeremy Schwartz , Jacob Patrick Thalman , Olivier Amaury Toupet , David Evan Zlotnik
Abstract: Systems and techniques for determining a sideslip vector for a vehicle that may have a direction that is different from that of a heading vector for the vehicle. The sideslip vector in a current vehicle state and sideslip vectors in predicted vehicles states may be used to determine paths for a vehicle through an environment and trajectories for controlling the vehicle through the environment. The sideslip vector may be based on a vehicle position that is the center point of the wheelbase of the vehicle and may include lateral velocity, facilitating the control of four-wheel steered vehicle while maintaining the ability to control two-wheel steered vehicles.
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公开(公告)号：US12037013B1
公开(公告)日：2024-07-16
申请号：US17515244
申请日：2021-10-29
Applicant: Zoox, Inc.
Inventor： Gary Linscott , Andreas Pasternak , Jefferson Bradfield Packer , Marin Kobilarov
CPC classification number: B60W60/0011 , B60W60/00272 , G06F11/3452 , G06F11/3457 , G06N20/00
Abstract: Automating reinforcement learning for autonomous vehicles may include assigning a probability with a scenario and varying that probability based at least in part on changes in performance by the autonomous vehicle associated with that scenario. The amount of time and computational bandwidth required to train a machine-learned component of an autonomous vehicle and the accuracy of the machine-learned component may be improved by determining a reward for performance of the autonomous vehicle in a scenario based at least in part on an severity metric. The impact severity metric may be determined based at least in part on a velocity, angle, and/or interaction area associated with the impact.
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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：US11875681B2
公开(公告)日：2024-01-16
申请号：US17187226
申请日：2021-02-26
Applicant: Zoox, Inc.
Inventor： Timothy Caldwell , Dan Xie , William Anthony Silva , Abishek Krishna Akella , Jefferson Bradfield Packer , Rick Zhang , Marin Kobilarov
CPC classification number: G08G1/166 , G08G1/0112 , G08G1/167
Abstract: Drive envelope determination is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate agent(s) in the environment and the computing system(s) can determine, based on the sensor data, a planned path through the environment relative to the agent(s). The computing system(s) can also determine lateral distance(s) to the agent(s) from the planned path. In an example, the computing system(s) can determine modified distance(s) based at least in part on the lateral distance(s) and information about the agents. The computing system can determine a drive envelope based on the modified distance(s) and can determine a trajectory in the drive envelope.
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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：US11485384B2
公开(公告)日：2022-11-01
申请号：US16872284
申请日：2020-05-11
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Janek Hudecek , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland , Marin Kobilarov
Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.
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公开(公告)号：US20220260994A1
公开(公告)日：2022-08-18
申请号：US17722819
申请日：2022-04-18
Applicant: Zoox, Inc.
Inventor： Amanda Lee Kelly Lockwood , Ravi Gogna , Gary Linscott , Timothy Caldwell , Marin Kobilarov , Paul Orecchio , Dan Xie , Ashutosh Gajanan Rege , Jesse Sol Levinson
IPC: G05D1/00 , G05D1/02 , G08G1/16 , B60W30/095 , G08G1/00
Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.
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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：US20210347382A1
公开(公告)日：2021-11-11
申请号：US16872284
申请日：2020-05-11
Applicant: Zoox, Inc.
Inventor： Zhenqi Huang , Janek Hudecek , Dhanushka Nirmevan Kularatne , Mark Jonathon McClelland , Marin Kobilarov
Abstract: The techniques discussed herein may comprise an autonomous vehicle guidance system that generates a path for controlling an autonomous vehicle based at least in part on a static object map and/or one or more dynamic object maps. The guidance system may identify a path based at least in part on determining set of nodes and a cost map associated with the static and/or dynamic object, among other costs, pruning the set of nodes, and creating further nodes from the remaining nodes until a computational or other limit is reached. The path output by the techniques may be associated with a cheapest node of the sets of nodes that were generated.
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