TRAFFIC SIMULATION METHOD FOR CREATING AN OPTIMIZED OBJECT MOTION PATH IN THE SIMULATOR

    公开(公告)号:US20230169228A1

    公开(公告)日:2023-06-01

    申请号:US17779606

    申请日:2020-11-25

    CPC classification number: G06F30/20 G06F2111/20

    Abstract: Provided is a traffic simulation for controlling a motion of an object, such as a vehicle, a pedestrian moving on a road or a pavement, in a driving simulation, an autonomous driving simulation, or the like. A traffic simulation method according to an embodiment of the present disclosure includes the steps of: importing a new moving object into a simulation environment of a simulator; retrieving data of a moving path and a start point of the moving object which is created based on a function, among pre-stored data; calculating 3D coordinates regarding a position of the moving object; moving the moving object along the moving path in the simulation environment, based on the calculated 3D coordinates; and calculating a next position of the moving object. Accordingly, a motion of an object within a simulator may be precisely created and reliability of validation regarding an operation of an algorithm mounted in an autonomous driving vehicle may be enhanced.

    STEREO VISION SOC AND PROCESSING METHOD THEREOF
    13.
    发明申请
    STEREO VISION SOC AND PROCESSING METHOD THEREOF 审中-公开
    立体视觉及其处理方法

    公开(公告)号:US20160191889A1

    公开(公告)日:2016-06-30

    申请号:US14952218

    申请日:2015-11-25

    CPC classification number: H04N13/128 H04N5/3572 H04N2013/0081

    Abstract: A stereo vision SoC and a processing method thereof are provided. The stereo vision SoC extracts first support points from an image and adds second support points, performs triangulation based on the first support points and the second support points; and extracts disparity using a result of the triangulation. Accordingly, depth image quality is improved and HW is easily implemented in the stereo vision SoC.

    Abstract translation: 提供立体视觉SoC及其处理方法。 立体视觉SoC从图像中提取第一个支持点,并添加第二个支持点,基于第一个支持点和第二个支持点执行三角测量; 并使用三角测量的结果来提取视差。 因此,提高了深度图像质量,并且在立体视觉SoC中容易地实现HW。

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