Method and device for identifying and classifying objects
    33.
    发明申请
    Method and device for identifying and classifying objects 有权
    用于识别和分类对象的方法和设备

    公开(公告)号:US20100097200A1

    公开(公告)日:2010-04-22

    申请号:US11919622

    申请日:2006-04-26

    Abstract: A method and device for identifying and classifying objects, electromagnetic radiation being emitted by a sensor, the radiation components reflected on objects being received by the sensor, the received signals being analyzed by comparison with stored characteristic values and the class of the reflecting object being deduced on the basis of the analysis. To this end, an analyzer is provided for analyzing the received signals, a memory is provided for storing characteristic patterns, its stored patterns being compared with the analyzed signals and thus the class of the reflecting objects being deducible on the basis of the comparison.

    Abstract translation: 用于识别和分类物体的方法和装置,由传感器发射的电磁辐射,由传感器接收的物体上反射的辐射分量,通过与所存储的特征值和反射物体的类别进行比较来分析接收信号 在分析的基础上。 为此,提供了一种用于分析接收信号的分析器,提供存储特征图案的存储器,将其存储的图案与分析的信号进行比较,从而基于比较可推导出反射对象的类别。

    METHOD AND DEVICE FOR OBJECT TRACKING IN A DRIVER ASSISTANCE SYSTEM OF A MOTOR VEHICLE
    34.
    发明申请
    METHOD AND DEVICE FOR OBJECT TRACKING IN A DRIVER ASSISTANCE SYSTEM OF A MOTOR VEHICLE 有权
    电动汽车驾驶员辅助系统中对象跟踪的方法和装置

    公开(公告)号:US20100017180A1

    公开(公告)日:2010-01-21

    申请号:US12305346

    申请日:2007-10-09

    Abstract: A method for predicting object movements in a driver assistance system of a motor vehicle, in which the movements of objects located periodically by a locating device (12) are precalculated by dynamic modeling of the objects, characterized in that a plurality of dynamic models (16) are held in readiness which are based on different hypotheses about the object, and that the models are selected and/or weighted as a function of the situation in accordance with the correctness probability (P1, P2) of the hypotheses for the prediction.

    Abstract translation: 一种用于预测机动车辆的驾驶员辅助系统中的物体运动的方法,其中由定位装置(12)周期性定位的物体的运动通过对象的动态建模进行预先计算,其特征在于,多个动态模型(16 )基于关于对象的不同假设的准备状态,并且根据预测的假设的正确性概率(P1,P2)来选择和/或加权模型作为情况的函数。

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