UNMANNED VEHICLE DRIVING APPARATUS AND METHOD FOR OBSTACLE AVOIDANCE
    7.
    发明申请
    UNMANNED VEHICLE DRIVING APPARATUS AND METHOD FOR OBSTACLE AVOIDANCE 审中-公开
    不可避免的车辆驾驶装置和方法

    公开(公告)号:US20150336576A1

    公开(公告)日:2015-11-26

    申请号:US14597722

    申请日:2015-01-15

    Abstract: An unmanned vehicle driving apparatus which allows obstacle avoidance and the method of it is commenced. The unmanned vehicle driving apparatus according to an exemplary embodiment include: a routing part which generates or receives a vehicle's fundamental driving route and generates a moved-driving route by adding or subtracting a route changing value to the fundamental driving route to avoid obstacles while driving; an obstacle detecting part which detects obstacles while driving; and a route driving part which drives the vehicle on the fundamental driving route and when an obstacle is detected, drives the vehicle on the moved-driving route to avoid it.

    Abstract translation: 允许避免障碍的无人驾驶的车辆驾驶装置及其方法开始。 根据示例性实施例的无人驾驶车辆驱动装置包括:路线部分,其生成或接收车辆的基本驾驶路线,并且通过将基本驾驶路线的路线变化值相加或减去来产生移动驾驶路线,以在驾驶时避免障碍物; 在驾驶时检测障碍物的障碍物检测部; 以及在基本驾驶路线上驱动车辆的路线驱动部,当检测到障碍物时,将车辆驱动在移动驾驶路线上以避免。

    METHOD AND APPARATUS OF FILTERING DYNAMIC OBJECTS IN RADAR-BASED EGO-EMOTION ESTIMATION

    公开(公告)号:US20240230842A9

    公开(公告)日:2024-07-11

    申请号:US18487276

    申请日:2023-10-16

    CPC classification number: G01S7/417 G01S7/415 G01S13/50

    Abstract: A method of filtering dynamic objects in radar-based ego-motion estimation includes converting measurement value at current time, measured by radar sensor, into point cloud, classifying the point cloud into points of a first object predicted as static object and points of a second object predicted as dynamic object, based on position value of dynamic object tracked at previous time, classifying the points of the first object into the points of the static object predicted as normal value and the points of the dynamic object predicted as outlier, based on outlier filtering algorithm, classifying the points of the second object into points of a candidate static object and points of a candidate dynamic object, based on velocity model of the static object, and tracking a position value of the dynamic object at current time, based on the points of the dynamic object and the points of the candidate dynamic object.

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