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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：US11195418B1
公开(公告)日：2021-12-07
申请号：US16420050
申请日：2019-05-22
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：US20210331703A1
公开(公告)日：2021-10-28
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：US11126873B2
公开(公告)日：2021-09-21
申请号：US15982658
申请日：2018-05-17
Applicant: Zoox, Inc.
Inventor： Tencia Lee , Kai Zhenyu Wang , James William Vaisey Philbin
Abstract: Techniques for determining lighting states of a tracked object, such as a vehicle, are discussed herein. An autonomous vehicle can include an image sensor to capture image data of an environment. Objects such can be identified in the image data as objects to be tracked. Frames of the image data representing the tracked object can be selected and input to a machine learning algorithm (e.g., a convolutional neural network, a recurrent neural network, etc.) that is trained to determine probabilities associated with one or more lighting states of the tracked object. Such lighting states include, but are not limited to, a blinker state(s), a brake state, a hazard state, etc. Based at least in part on the one or more probabilities associated with the one or more lighting states, the autonomous vehicle can determine a trajectory for the autonomous vehicle and/or can determine a predicted trajectory for the tracked object.
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公开(公告)号：US11126179B2
公开(公告)日：2021-09-21
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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Patent Agency Ranking
公开(公告)号：US11055624B1
公开(公告)日：2021-07-06
申请号：US15807521
申请日：2017-11-08
Applicant: Zoox, Inc.
Inventor： Benjamin Sapp , Yilun Wang , James William Vaisey Philbin
Abstract: The generation and/or use of probabilistic heat maps for use in predicting the behavior of entities in an environment is described. In an example, a computing device(s) can receive sensor data from sensors of a vehicle in an environment. The computing device(s) can determine, based at least in partly on the sensor data, a location of an entity in the environment, and a first characteristic associated with the entity or the environment (type, velocity, orientation, etc.). The computing device(s) can access, from a database, a heat map generated from previously collected sensor data associated with the environment. The computing device(s) can perform a look-up using the heat map based at least partly on the location of the entity and the first characteristic, and can determine a predicted behavior of the entity at a predetermined future time based on a pattern of behavior associated with a cell in the heat map.
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公开(公告)号：US10936922B2
公开(公告)日：2021-03-02
申请号：US16013729
申请日：2018-06-20
Applicant: Zoox, Inc.
Inventor： Sarah Tariq , James William Vaisey Philbin , Kratarth Goel
Abstract: Improved techniques for training a machine learning (ML) model are discussed herein. Training the ML model can be based on a subset of examples. In particular, the training can include identifying a reference region associated with an area of the image representing an object, and selecting, based at least in part on a first confidence score associated with a first bounding box, a first hard example for inclusion in the subset of examples. In some cases, the first confidence score and the first bounding box can be associated with a first portion of the feature map. Next, the training can include determining that a first degree of alignment of the first bounding box to the reference region is above a threshold degree of alignment, and in response, replacing the first hard example with a second hard example.
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公开(公告)号：US20200380316A1
公开(公告)日：2020-12-03
申请号：US16941815
申请日：2020-07-29
Applicant: Zoox, Inc.
Inventor： Tencia Lee , James William Vaisey Philbin
IPC: G06K9/62 , G06K9/46 , G06T3/40 , G06T7/73 , G06K9/66 , G06N3/04 , G06N3/08 , G05D1/00 , G05D1/02
Abstract: Systems and methods for estimating a height of an object from a monocular image are described herein. Objects are detected in the image, each object being indicated by a region of interest. The image is then cropped for each region of interest and the cropped image scaled to a predetermined size. The cropped and scaled image is then input into a convolutional neural network (CNN), the output of which is an estimated height for the object. The height may be represented by a mean of a probability distribution of possible sizes, a standard deviation, as well as a level of confidence. A location of the object may be determined based on the estimated height and region of interest. A ground truth dataset may be generated for training the CNN by simultaneously capturing a LIDAR sequence with a monocular image sequence.
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公开(公告)号：US10817740B2
公开(公告)日：2020-10-27
申请号：US16013764
申请日：2018-06-20
Applicant: Zoox, Inc.
Inventor： Sarah Tariq , James William Vaisey Philbin , Kratarth Goel
Abstract: Techniques for using instance segmentation with machine learning (ML) models are discussed herein. An image can be provided as input to a ML model, which can generate, as an output from the ML model, a feature map comprising a plurality of features. Each feature of the plurality of features can comprise a confidence score, classification information, and a region of interest (ROI) determined in accordance with a non-maximal suppression (NMS) technique. Individual ROIs that are similar can be associated together for segmentation purposes. That is, instead of requiring a second ML model and/or a second operation to segment the image (e.g., identify which pixels correspond with the detected object, for example, by outputting a mask or set of lines and/or curves), the techniques discussed herein substantially simultaneously detect an object (e.g., determine an ROI) and segment the image.
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公开(公告)号：US20190354786A1
公开(公告)日：2019-11-21
申请号：US15982658
申请日：2018-05-17
Applicant: Zoox, Inc.
Inventor： Tencia Lee , Kai Zhenyu Wang , James William Vaisey Philbin
Abstract: Techniques for determining lighting states of a tracked object, such as a vehicle, are discussed herein. An autonomous vehicle can include an image sensor to capture image data of an environment. Objects such can be identified in the image data as objects to be tracked. Frames of the image data representing the tracked object can be selected and input to a machine learning algorithm (e.g., a convolutional neural network, a recurrent neural network, etc.) that is trained to determine probabilities associated with one or more lighting states of the tracked object. Such lighting states include, but are not limited to, a blinker state(s), a brake state, a hazard state, etc. Based at least in part on the one or more probabilities associated with the one or more lighting states, the autonomous vehicle can determine a trajectory for the autonomous vehicle and/or can determine a predicted trajectory for the tracked object.
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