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    AUTOMATICALLY GENERATING TRAINING DATA FOR A LIDAR USING SIMULATED VEHICLES IN VIRTUAL SPACE
    21.
    发明申请
    AUTOMATICALLY GENERATING TRAINING DATA FOR A LIDAR USING SIMULATED VEHICLES IN VIRTUAL SPACE 审中-公开

    公开(公告)号：US20200074266A1

    公开(公告)日：2020-03-05

    申请号：US16559945

    申请日：2019-09-04

    Applicant: Luminar Technologies, Inc.

    Inventor： Miguel Alexander Peake ,  Benjamin Englard

    IPC: G06N3/00 ,  G06N20/00 ,  G06N5/04 ,  G06F13/28 ,  G06K9/62 ,  G06T7/246 ,  G06K9/00 ,  G06T7/50 ,  G05D1/00 ,  G05D1/02 ,  G01S7/48 ,  G01C21/28

    Abstract: Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.

    AUTOMATICALLY GENERATING TRAINING DATA FOR A LIDAR USING SIMULATED VEHICLES IN VIRTUAL SPACE
    22.
    发明申请
    AUTOMATICALLY GENERATING TRAINING DATA FOR A LIDAR USING SIMULATED VEHICLES IN VIRTUAL SPACE 审中-公开

    公开(公告)号：US20200074233A1

    公开(公告)日：2020-03-05

    申请号：US16560018

    申请日：2019-09-04

    Applicant: Luminar Technologies, Inc.

    Inventor： Benjamin Englard ,  Miguel Alexander Peake

    IPC: G06K9/62 ,  G06N3/00 ,  G06T11/60 ,  G08G1/0962

    Abstract: Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.

    DETERMINING RELATIVE VELOCITY USING CO-LOCATED PIXELS
    23.
    发明申请
    DETERMINING RELATIVE VELOCITY USING CO-LOCATED PIXELS 审中-公开

    公开(公告)号：US20200043176A1

    公开(公告)日：2020-02-06

    申请号：US16196630

    申请日：2018-11-20

    Applicant: LUMINAR TECHNOLOGIES, INC.

    Inventor： Tomi P. Maila ,  Pranav Maheshwari ,  Benjamin Englard

    IPC: G06T7/246 ,  G06T7/521 ,  G06K9/00 ,  G06T7/00 ,  G06T7/73 ,  G01S17/58 ,  G06T5/00

    Abstract: A computer-implemented method of determining relative velocity between a vehicle and an object. The method includes receiving sensor data generated by one or more sensors of the vehicle configured to sense an environment by following a scan pattern comprising component scan lines. The method includes obtaining, based on the sensor data, a point cloud frame. Additionally, the method includes identifying a first pixel and a second pixel that are co-located within a field of regard and overlap a point cloud object within the point cloud frame and calculating a difference between a depth associated with the first pixel and a depth associated with the second pixel. The method includes determining a relative velocity of the point cloud object by dividing the difference in depth data by a time difference between when the depth associated with the first pixel was sensed and the depth associated with the second pixel was sensed.

    Autonomous vehicle technology for facilitating operation according to motion primitives
    24.
    发明授权
    Autonomous vehicle technology for facilitating operation according to motion primitives 有权

    公开(公告)号：US10394243B1

    公开(公告)日：2019-08-27

    申请号：US16138582

    申请日：2018-09-21

    Applicant: LUMINAR TECHNOLOGIES, INC.

    Inventor： Vahid R. Ramezani ,  Benjamin Englard ,  Tomi P. Maila

    IPC: G05D1/02 ,  G05D1/00 ,  B60W50/029

    Abstract: Various software techniques for managing operation of autonomous vehicles based on sensor data are disclosed herein. A computing system may generate, based on a set of signals descriptive of a current state of an environment in which the autonomous vehicle is operating, a normal path plan separate from a safe path plan, or a hybrid path plan including a normal path plan and a safe path plan. In generating the safe path plan, the computing system may generate and concatenate a set of motion primitives. When a fault condition occurs, the computing device may transition from executing the normal path plan to executing the safe path plan to safely stop the autonomous vehicle.

    Processing point clouds of vehicle sensors having variable scan line distributions using two-dimensional interpolation and distance thresholding
    25.
    发明授权
    Processing point clouds of vehicle sensors having variable scan line distributions using two-dimensional interpolation and distance thresholding 有权

    公开(公告)号：US10338223B1

    公开(公告)日：2019-07-02

    申请号：US16176649

    申请日：2018-10-31

    Applicant: LUMINAR TECHNOLOGIES, INC.

    Inventor： Benjamin Englard ,  Eric C. Danziger

    IPC: G01S17/89 ,  G05D1/02 ,  B60W30/18 ,  G06T7/50 ,  G06K9/42 ,  G06N3/08 ,  G05D1/00 ,  G06K9/62 ,  G06K9/00 ,  G06T7/20

    Abstract: A method for processing point clouds having variable spatial distributions of scan lines includes receiving a point cloud frame generated by a sensor configured to sense a vehicle environment. Each of the points in the frame has associated two-dimensional coordinates and an associated parameter value. The method also includes generating a normalized point cloud frame by adding interpolated points not present in the received frame, at least by, for each interpolated point, identifying one or more neighboring points having associated two-dimensional coordinates that are within a threshold distance of two-dimensional coordinates for the interpolated point, and calculating an estimated parameter value of the interpolated point using, for each of the identified neighboring points, a distance between the two-dimensional coordinates and the parameter value associated with the identified neighboring point. The method also includes generating, using the normalized point cloud frame, signals descriptive of a current state of the vehicle environment.

    ADJUSTING AREA OF FOCUS OF VEHICLE SENSORS BY CONTROLLING SPATIAL DISTRIBUTIONS OF SCAN LINES
    26.
    发明申请
    ADJUSTING AREA OF FOCUS OF VEHICLE SENSORS BY CONTROLLING SPATIAL DISTRIBUTIONS OF SCAN LINES 审中-公开

    公开(公告)号：US20190179026A1

    公开(公告)日：2019-06-13

    申请号：US16176624

    申请日：2018-10-31

    Applicant: LUMINAR TECHNOLOGIES, INC.

    Inventor： Benjamin Englard ,  Eric C. Danziger ,  Austin K. Russell

    IPC: G01S17/89 ,  G01S17/02 ,  G01S17/66 ,  G01S7/48

    Abstract: A method for controlling at least a first sensor of a vehicle, which senses an environment through which the vehicle is moving by producing a plurality of scan lines arranged according to a spatial distribution, includes receiving sensor data generated by one or more sensors. The one or more sensors are configured to sense the environment through which the vehicle is moving. The method also includes identifying, by one or more processors and based on the received sensor data, one or more areas of interest in the environment, and causing, by one or more processors and based on the areas of interest, the spatial distribution of the plurality of scan lines produced by the first sensor to be adjusted.

    TRAINING A MACHINE LEARNING BASED MODEL OF A VEHICLE PERCEPTION COMPONENT BASED ON SENSOR SETTINGS
    27.
    发明申请
    TRAINING A MACHINE LEARNING BASED MODEL OF A VEHICLE PERCEPTION COMPONENT BASED ON SENSOR SETTINGS 审中-公开

    公开(公告)号：US20190178988A1

    公开(公告)日：2019-06-13

    申请号：US16176529

    申请日：2018-10-31

    Applicant: LUMINAR TECHNOLOGIES, INC.

    Inventor： Benjamin Englard ,  Eric C. Danziger

    IPC: G01S7/40 ,  G01S13/93 ,  G01S7/41 ,  G01S13/42 ,  G05D1/00 ,  G05D1/02 ,  B60W30/18 ,  B60W10/04 ,  G06N3/08

    Abstract: A method for configuring a perception component of a vehicle having one or more sensors includes generating a first set of training data that includes first sensor data corresponding to a first setting of one or more sensor parameters, and an indicator of the first setting. The method also includes generating a second set of training data that includes second sensor data corresponding to a second setting of the sensor parameter(s), and an indicator of the second setting. The method further includes training the perception component, at least by training a machine learning based model using the first and second training data sets. The trained perception component is configured to generate signals descriptive of a current state of the vehicle environment by processing sensor data generated by the sensor(s), and one or more indicators indicating which setting of the sensor parameter(s) corresponds to which portions of the generated sensor data.

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