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    Predicting lane changes of other vehicles
    1.
    发明授权
    Predicting lane changes of other vehicles 有权

    公开(公告)号：US11332134B2

    公开(公告)日：2022-05-17

    申请号：US16715572

    申请日：2019-12-16

    申请人： Robert Bosch GmbH

    发明人： Chun Yang ,  Laszlo Anka ,  Adam Rigo ,  Kornel Istvan Kis ,  Levente Kis

    IPC分类号： B60W30/095 ,  B60W50/14

    摘要： A method for predicting whether another vehicle in the driving-environment of an ego-vehicle will execute a lane-change, based on observations of the driving-environment of the ego-vehicle, including: the observations are supplied to individual classificators; based on at least a portion of the observations, each individual classificator, in accordance with an individual instruction, ascertains an individual probability that the other vehicle will change lanes; the driving situation in which the ego-vehicle finds itself is classified as a whole by a situation classificator into one of several discrete classes; a record of weighting factors, assigned to the class into which the situation-classificator has classified the driving-situation, is ascertained, that indicates the relative weighting of the individual classificators for this driving situation; the individual probabilities are set off against the weighting-factors to form an overall probability that the other vehicle will change lanes. A method for training weighting-factors and related computer-program are described.

    METHOD AND SYSTEM FOR GENERATING RADAR REFLECTION POINTS
    2.
    发明申请
    METHOD AND SYSTEM FOR GENERATING RADAR REFLECTION POINTS 审中-公开

    公开(公告)号：US20200379087A1

    公开(公告)日：2020-12-03

    申请号：US16878814

    申请日：2020-05-20

    申请人： Robert Bosch GmbH

    发明人： Chun Yang ,  Sebastian Muenzner ,  Zoltan Karasz ,  Fabian Timm ,  Jasmin Ebert ,  Laszlo Anka

    IPC分类号： G01S7/41 ,  G01S13/89

    摘要： A method for generating radar reflection points comprising the steps of: providing a plurality of predefined radar reflection points of at least one first object detected by a radar and at least one first scenario description describing a first environment related to the detected first object; converting the predefined radar reflection points into at least one first power distribution pattern image related to a distribution of a power returning from the detected first object; training a model based on the first power distribution pattern image and the first scenario description; providing at least one second scenario description describing a second environment related to a second object; generating at least one second power distribution pattern image related to a distribution of a power returning from the second object based on the trained model and the second scenario description; and sampling the second power distribution pattern image.