SYSTEM AND METHOD FOR VEHICLE NAVIGATION USING TERRAIN TEXT RECOGNITION

    公开(公告)号:US20210239485A1

    公开(公告)日:2021-08-05

    申请号:US16782683

    申请日:2020-02-05

    Abstract: A method of vehicle navigation using terrain text recognition includes receiving, via an electronic controller arranged on a vehicle and having access to a map of the terrain, a navigation route through the terrain. The method also includes receiving, via the controller, a signal from a global positioning system (GPS) to determine a current position of the vehicle relative to the terrain. The method additionally includes determining, via the controller, a location of a next waypoint on the navigation route and relative to the current vehicle position. The method also includes detecting and communicating to the controller, via a vehicle sensor an image frame displaying a text indicative of the next waypoint and correlating, via the controller, the detected text to the next waypoint on the map. Furthermore, the method includes setting, via the controller, an in-vehicle alert indicative of the detected text having been correlated to the next waypoint.

    TRAJECTORY TRACKING FOR VEHICLE LATERAL CONTROL USING NEURAL NETWORK

    公开(公告)号:US20190248411A1

    公开(公告)日:2019-08-15

    申请号:US15896352

    申请日:2018-02-14

    Abstract: A processor-implemented method in a vehicle for performing automatic vehicle lateral control using a neural network for trajectory tracking is provided. The method includes receiving a desired vehicle trajectory; constructing, from the desired trajectory, desired waypoint data for a small number of desired waypoints at different look ahead distances in front of the center of gravity of the vehicle; and generating a steering angle command using a feedforward artificial neural network (ANN) as a function of vehicle speed and the desired waypoint data without modeling vehicle or tire dynamics. The vehicle steering actuator system provides steering control to cause vehicle steering to attempt to achieve the steering angle command.

    SEMANTIC PRESERVED STYLE TRANSFER
    5.
    发明申请

    公开(公告)号:US20200311986A1

    公开(公告)日:2020-10-01

    申请号:US16366393

    申请日:2019-03-27

    Abstract: A method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.

    System and Method of Automatic Image View Alignment for Camera-Based Road Condition Detection on a Vehicle

    公开(公告)号:US20240046491A1

    公开(公告)日:2024-02-08

    申请号:US17815760

    申请日:2022-07-28

    CPC classification number: G06T7/337 G06V20/588 G06T2207/30252

    Abstract: A system and method of automatic image view alignment for a camera-based road condition detection on a vehicle. The method includes transforming a fisheye image into a non-distorted subject image, comparing the subject image with a reference image, aligning the subject image with the reference image, and analyzing the aligned subject image to detect and identify road conditions in real-time as the vehicle is in operation. The subject image is aligned with the reference image by determining a distance (d) between predetermined feature points of the subject and reference images, estimating a pitch of a projection center based on the distance d, and generating an aligned subject image by applying a rectification transformation on the fisheye image by relocating a center of projection of the fisheye image by the pitch angle .

    Semantic preserved style transfer

    公开(公告)号:US10832450B2

    公开(公告)日:2020-11-10

    申请号:US16366393

    申请日:2019-03-27

    Abstract: A method for image style transfer using a Semantic Preserved Generative Adversarial Network (SPGAN) includes: receiving a source image; inputting the source image into the SPGAN; extracting a source-semantic feature data from the source image; generating, by the first decoder, a first synthetic image including the source semantic content of the source image in a target style of a target image using the source-semantic feature data extracted by the first encoder of the first generator network, wherein the first synthetic image includes first-synthetic feature data; determining a first encoder loss using the source-semantic feature data and the first-synthetic feature data; discriminating the first synthetic image against the target image to determine a GAN loss; determining a total loss as a function of the first encoder loss and the first GAN loss; and training the first generator network and the first discriminator network.

    System for calibrating extrinsic parameters for a camera in an autonomous vehicle

    公开(公告)号:US12112506B2

    公开(公告)日:2024-10-08

    申请号:US17688157

    申请日:2022-03-07

    Inventor: Farui Peng Hao Yu

    CPC classification number: G06T7/80 G06T7/246 G06T7/292 G06T7/73 G06T2207/30248

    Abstract: A system for determining calibrated camera extrinsic parameters for an autonomous vehicle includes a camera mounted to the autonomous vehicle collecting image data including a plurality of image frames. The system also includes one or more automated driving controllers in electronic communication with the camera that executes instructions to determine a vehicle pose estimate based on position and movement of the autonomous vehicle by a localization algorithm. The one or more automated driving controllers determine the calibrated camera extrinsic parameters based on three dimensional coordinates for specific feature points of interests corresponding to two sequential image frames, the specific feature points of interests corresponding to the two sequential image frames, and the camera pose corresponding to the two sequential image frames.

    SYSTEM FOR CALIBRATING EXTRINSIC PARAMETERS FOR A CAMERA IN AN AUTONOMOUS VEHICLE

    公开(公告)号:US20230281872A1

    公开(公告)日:2023-09-07

    申请号:US17688157

    申请日:2022-03-07

    Inventor: Farui Peng Hao Yu

    CPC classification number: G06T7/80 G06T7/73 G06T7/246 G06T7/292 G06T2207/30248

    Abstract: A system for determining calibrated camera extrinsic parameters for an autonomous vehicle includes a camera mounted to the autonomous vehicle collecting image data including a plurality of image frames. The system also includes one or more automated driving controllers in electronic communication with the camera that executes instructions to determine a vehicle pose estimate based on position and movement of the autonomous vehicle by a localization algorithm. The one or more automated driving controllers determine the calibrated camera extrinsic parameters based on three dimensional coordinates for specific feature points of interests corresponding to two sequential image frames, the specific feature points of interests corresponding to the two sequential image frames, and the camera pose corresponding to the two sequential image frames.

    Trajectory tracking for vehicle lateral control using neural network

    公开(公告)号:US10737717B2

    公开(公告)日:2020-08-11

    申请号:US15896352

    申请日:2018-02-14

    Abstract: A processor-implemented method in a vehicle for performing automatic vehicle lateral control using a neural network for trajectory tracking is provided. The method includes receiving a desired vehicle trajectory; constructing, from the desired trajectory, desired waypoint data for a small number of desired waypoints at different look ahead distances in front of the center of gravity of the vehicle; and generating a steering angle command using a feedforward artificial neural network (ANN) as a function of vehicle speed and the desired waypoint data without modeling vehicle or tire dynamics. The vehicle steering actuator system provides steering control to cause vehicle steering to attempt to achieve the steering angle command.

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