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公开(公告)号：US11216674B2
公开(公告)日：2022-01-04
申请号：US16853517
申请日：2020-04-20
Applicant: Waymo LLC
Inventor： Abhijit Ogale , Alexander Krizhevsky
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting locations in an environment of a vehicle where objects are likely centered and determining properties of those objects. One of the methods includes receiving an input characterizing an environment external to a vehicle. For each of a plurality of locations in the environment, a respective first object score that represents a likelihood that a center of an object is located at the location is determined. Based on the first object scores, one or more locations from the plurality of locations are selected as locations in the environment at which respective objects are likely centered. Object properties of the objects that are likely centered at the selected locations are also determined.
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公开(公告)号：US20210364637A1
公开(公告)日：2021-11-25
申请号：US17394861
申请日：2021-08-05
Applicant: Waymo LLC
Inventor： Yu-Han Chen , Wan-Yen Lo , Alper Ayvaci , Abhijit Ogale , Hyman Murveit
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a location of a particular object relative to a vehicle. In one aspect, a method includes obtaining sensor data captured by one or more sensors of a vehicle. The sensor data is processed by a convolutional neural network to generate a sensor feature representation of the sensor data. Data is obtained which defines a particular spatial region in the sensor data that has been classified as including sensor data that characterizes the particular object. An object feature representation of the particular object is generated from a portion of the sensor feature representation corresponding to the particular spatial region. The object feature representation of the particular object is processed using a localization neural network to generate the location of the particular object relative to the vehicle.
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公开(公告)号：US11181914B2
公开(公告)日：2021-11-23
申请号：US16714065
申请日：2019-12-13
Applicant: Waymo LLC
Inventor： Dave Ferguson , Abhijit Ogale
IPC: G05D1/00 , B60W30/00 , G06K9/00 , G01S13/02 , G01S17/02 , G01S19/13 , G05D1/02 , B60W30/10 , B60W40/04 , B60W40/06
Abstract: Methods and systems for the use of detected objects for image processing are described. A computing device autonomously controlling a vehicle may receive images of the environment surrounding the vehicle from an image-capture device coupled to the vehicle. In order to process the images, the computing device may receive information indicating characteristics of objects in the images from one or more sources coupled to the vehicle. Examples of sources may include RADAR, LIDAR, a map, sensors, a global positioning system (GPS), or other cameras. The computing device may use the information indicating characteristics of the objects to process received images, including determining the approximate locations of objects within the images. Further, while processing the image, the computing device may use information from sources to determine portions of the image to focus upon that may allow the computing device to determine a control strategy based on portions of the image.
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公开(公告)号：US11105924B2
公开(公告)日：2021-08-31
申请号：US16151880
申请日：2018-10-04
Applicant: Waymo LLC
Inventor： Yu-Han Chen , Wan-Yen Lo , Alper Ayvaci , Abhijit Ogale , Hyman Murveit
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a location of a particular object relative to a vehicle. In one aspect, a method includes obtaining sensor data captured by one or more sensors of a vehicle. The sensor data is processed by a convolutional neural network to generate a sensor feature representation of the sensor data. Data is obtained which defines a particular spatial region in the sensor data that has been classified as including sensor data that characterizes the particular object. An object feature representation of the particular object is generated from a portion of the sensor feature representation corresponding to the particular spatial region. The object feature representation of the particular object is processed using a localization neural network to generate the location of the particular object relative to the vehicle.
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公开(公告)号：US10883844B2
公开(公告)日：2021-01-05
申请号：US15662007
申请日：2017-07-27
Applicant: Waymo LLC
Inventor： Abhijit Ogale , Mayank Bansal , Alexander Krizhevsky
Abstract: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.
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Patent Agency Ranking
公开(公告)号：US20200174490A1
公开(公告)日：2020-06-04
申请号：US16628086
申请日：2018-07-27
Applicant: Waymo LLC
Inventor： Abhijit Ogale , Mayank Bansal , Alexander Krizhevsky
Abstract: Systems, methods, devices, and other techniques for planning a trajectory of a vehicle. A computing system can implement a trajectory planning neural network configured to, at each time step of multiple time steps: obtain a first neural network input and a second neural network input. The first neural network input can characterize a set of waypoints indicated by the waypoint data, and the second neural network input can characterize (a) environmental data that represents a current state of an environment of the vehicle and (b) navigation data that represents a planned navigation route for the vehicle. The trajectory planning neural network may process the first neural network input and the second neural network input to generate a set of output scores, where each output score in the set of output scores corresponds to a different location of a set of possible locations in a vicinity of the vehicle.
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公开(公告)号：US20190034794A1
公开(公告)日：2019-01-31
申请号：US15662031
申请日：2017-07-27
Applicant: Waymo LLC
Inventor： Abhijit Ogale , Mayank Bansal , Alexander Krizhevsky
Abstract: Systems, methods, devices, and other techniques for training a trajectory planning neural network system to determine waypoints for trajectories of vehicles. A neural network training system can train the trajectory planning neural network system on the multiple training data sets. Each training data set can include: (i) a first training input that characterizes a set of waypoints that represent respective locations of a vehicle at each of a series of first time steps, (ii) a second training input that characterizes at least one of (a) environmental data that represents a current state of an environment of the vehicle or (b) navigation data that represents a planned navigation route for the vehicle, and (iii) a target output characterizing a waypoint that represents a target location of the vehicle at a second time step that follows the series of first time steps.
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公开(公告)号：US20180345958A1
公开(公告)日：2018-12-06
申请号：US15611397
申请日：2017-06-01
Applicant: Waymo LLC
Inventor： Wan-Yen Lo , Abhijit Ogale , David Ferguson
Abstract: In some implementations, an autonomous or semi-autonomous vehicle is capable of using a collision prediction system to determine a confidence that any objects detected within a vicinity of the vehicle are on a trajectory that will collide with the vehicle. Laser obstacle points derived from recent sensor readings of one or more sensors of a vehicle are initially obtained. The laser obstacle points are projected into a pose coordinate system to generate an occupancy grid of a vicinity of the vehicle. A confidence that any objects represented by the laser obstacle points are on a trajectory that will collide with the vehicle is determined by applying a particle filter to the occupancy grid.
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公开(公告)号：US20180101176A1
公开(公告)日：2018-04-12
申请号：US15837501
申请日：2017-12-11
Applicant: Waymo LLC
Inventor： Abhijit Ogale
CPC classification number: G05D1/0231 , B60W30/17 , B60W2420/42 , G06K9/00791 , G06K9/00805 , G06K9/3241 , G06K9/6293 , G06K2209/23
Abstract: The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes a vehicle detection system. The vehicle detection system includes receiving an image of a field of view of the vehicle and identifying a region-pair in the image with a sliding-window filter. The region-pair is made up of a first region and a second region. Each region is determined based on a color of pixels within the sliding-window filter. The vehicle detection system also determines a potential second vehicle in the image based on the region-pair. In response to determining the potential second vehicle in the image, the vehicle detection system performs a multi-stage classification of the image to determine whether the second vehicle is present in the image. Additionally, the vehicle detection system provides instructions to control the first vehicle based at least on the determined second vehicle.
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公开(公告)号：US09727795B1
公开(公告)日：2017-08-08
申请号：US15018077
申请日：2016-02-08
Applicant: Waymo LLC
Inventor： Wan-Yen Lo , David Ian Franklin Ferguson , Abhijit Ogale
CPC classification number: G06K9/00798 , B60R1/00 , G06K9/00791 , G06K9/00805 , G06K9/00845 , G06K9/4647 , G06K9/4652 , G06K9/52 , G06K9/6215 , G06K2009/4666 , G06T7/20 , G06T7/408 , G06T2207/30256
Abstract: Methods and systems for real-time road flare detection using templates and appropriate color spaces are described. A computing device of a vehicle may be configured to receive an image of an environment of the vehicle. The computing device may be configured to identify a given pixels in the plurality of pixels having one or more of: (i) a red color value greater than a green color value, and (ii) the red color value greater than a blue color value. Further, the computing device may be configured to make a comparison between one or more characteristics of a shape of an object represented by the given pixels in the image and corresponding one or more characteristics of a predetermined shape of a road flare; and determine a likelihood that the object represents the road flare.
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