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公开(公告)号：US11966230B1
公开(公告)日：2024-04-23
申请号：US17125388
申请日：2020-12-17
Applicant: Zoox, Inc.
Inventor： Greg Woelki , Kai Zhenyu Wang , Bertrand Robert Douillard , Michael Haggblade , James William Vaisey Philbin
CPC classification number: G05D1/0221 , B60W60/0027 , B60W60/005 , G05D1/0214 , G05D1/0231 , G05D1/0276 , G06N7/01 , G06N20/00 , G06V20/58 , B60W2420/42 , B60W2554/4026 , B60W2554/4029 , B60W2554/404 , B60W2556/10 , B60W2556/45 , G05D2201/0213
Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.
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公开(公告)号：US11753036B1
公开(公告)日：2023-09-12
申请号：US16653463
申请日：2019-10-15
Applicant: Zoox, Inc.
Inventor： Marc Wimmershoff , James William Vaisey Philbin , Sarah Tariq
CPC classification number: B60W60/0023 , B60L50/60 , G05D1/0212 , G07C5/085 , G01S13/931 , G01S2013/9323 , G01S2013/9324 , H04N7/18
Abstract: Energy consumption for a vehicle may be reduced based at least in part on an environment characteristic associated with the environment through which the vehicle travels or an operation characteristic associated with operation of the vehicle, thereby increasing an operational time of the vehicle. In some situations, reducing energy consumption may be associated with operation of one or more of a sensor (e.g., turning the sensor off, reducing a frequency or resolution of the sensor, etc.) and/or one or more processors associated with the vehicle (e.g., turning a processor off, reducing a rate of computation, etc.) based at least in part on one or more of the environment characteristic signal or the operation characteristic signal.
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公开(公告)号：US11734473B2
公开(公告)日：2023-08-22
申请号：US16708019
申请日：2019-12-09
Applicant: Zoox, Inc.
Inventor： Sai Anurag Modalavalasa , Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Ashutosh Gajanan Rege , Andreas Christian Reschka , Marc Wimmershoff
CPC classification number: G06F30/20 , G05D1/0027 , G05D1/0088 , G05D1/0214 , G06F18/23 , G06V20/56 , G06V20/58 , G07C5/0816 , G05D2201/0213
Abstract: Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.
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公开(公告)号：US11605236B2
公开(公告)日：2023-03-14
申请号：US16706623
申请日：2019-12-06
Applicant: Zoox, Inc.
Inventor： Kratarth Goel , James William Vaisey Philbin , Sarah Tariq
Abstract: Low variance detection training is described herein. In an example, annotated data can be determined based on sensor data received from a sensor associated with a vehicle. The annotated data can comprise an annotated low variance region and/or an annotated high variance region. The sensor data can be input into a model, and the model can determine an output comprising a high variance output and a low variance output. In an example, a difference between the annotated data and the output can be determined and one or more parameters associated with the model can be altered based at least in part on the difference. The model can be transmitted to a vehicle configured to be controlled by another output of the model.
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公开(公告)号：US11592818B2
公开(公告)日：2023-02-28
申请号：US16013748
申请日：2018-06-20
Applicant: Zoox, Inc.
Inventor： Sarah Tariq , James William Vaisey Philbin , Kratarth Goel
Abstract: Techniques for utilizing multiple scales of images as input to machine learning (ML) models are discussed herein. Operations can include providing an image associated with a first scale to a first ML model. An output of the first ML model can include a first bounding box indicative of a first region of the image representing a first object, with the first bounding box falling within a first range of sizes. Next, a scaled image can be generated by scaling the image. The scaled image can be provided to a second ML model, which can output a second bounding box indicative of a second region of the image representing a second object, the second bounding falling within a second range of sizes. Thus, inputting a scaled image to a same ML model (or to different ML models) can result in different detected features in the images.
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Patent Agency Ranking
公开(公告)号：US20210192234A1
公开(公告)日：2021-06-24
申请号：US16722931
申请日：2019-12-20
Applicant: Zoox, Inc.
Inventor： Yuanyuan Chen , Janek Hudecek , David Pfeiffer , James William Vaisey Philbin , Zeng Wang
Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.
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公开(公告)号：US20210142078A1
公开(公告)日：2021-05-13
申请号：US17088447
申请日：2020-11-03
Applicant: Zoox, Inc.
Inventor： Bryce A. Evans , James William Vaisey Philbin , Sarah Tariq
Abstract: Techniques are disclosed for implementing a neural network that outputs embeddings. Furthermore, techniques are disclosed for using sensor data to train a neural network to learn such embeddings. In some examples, the neural network may be trained to learn embeddings. The embeddings may be used for object identification, object matching, object classification, and/or object tracking in various examples.
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公开(公告)号：US10984290B1
公开(公告)日：2021-04-20
申请号：US16732243
申请日：2019-12-31
Applicant: Zoox, Inc.
Inventor： Kratarth Goel , James William Vaisey Philbin , Praveen Srinivasan , Sarah Tariq
Abstract: Training a machine-learning (ML) architecture to determine three or more outputs at a rate of 30 or more frames per second on consumer grade hardware may comprise jointly training components of the ML using loss(es) determined across the components and/or consistency losses determined between outputs of two or more components. The ML architecture discussed herein may comprise one or more sets of neural network layers and/or respective components for determining a two and/or three-dimensional region of interest, semantic segmentation, direction logits, depth data, and/or instance segmentation associated with an object in an image.
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公开(公告)号：US20210097148A1
公开(公告)日：2021-04-01
申请号：US16586838
申请日：2019-09-27
Applicant: Zoox, Inc.
Inventor： Gerrit Bagschik , Andrew Scott Crego , Antoine Ghislain Deux , Rodin Lyasoff , James William Vaisey Philbin , Marc Wimmershoff , Andreas Christian Reschka , Ashutosh Gajanan Rege
Abstract: Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.
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公开(公告)号：US20200272148A1
公开(公告)日：2020-08-27
申请号：US16282201
申请日：2019-02-21
Applicant: Zoox, Inc.
Inventor： Vasiliy Karasev , Tencia Lee , James William Vaisey Philbin , Sarah Tariq , Kai Zhenyu Wang
Abstract: Techniques for determining and/or predicting a trajectory of an object by using the appearance of the object, as captured in an image, are discussed herein. Image data, sensor data, and/or a predicted trajectory of the object (e.g., a pedestrian, animal, and the like) may be used to train a machine learning model that can subsequently be provided to, and used by, an autonomous vehicle for operation and navigation. In some implementations, predicted trajectories may be compared to actual trajectories and such comparisons are used as training data for machine learning.
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