OVERCOMING TECHNICAL CHALLANGES OF WORKING IN A VERY HIGH DIMENSIONAL SPACE

    公开(公告)号:US20240232591A1

    公开(公告)日:2024-07-11

    申请号:US18405500

    申请日:2024-01-05

    CPC classification number: G06N3/0495

    Abstract: A method for generating a sparse representation of a group of neural network features, the method includes (i) obtaining a group of neural network features (NNFs); and (ii) generating a lossless and sparse representation of the group of NNF, wherein the generating includes: (a) determining, by an allocation unit and based on one or more attributes of the group of NNFs, one or more relevant sparse representation generators (SRGs) out of a set of SRGs; (b) generating, by the one or more relevant SRGs, one or more relevant sparse outputs; (c) processing the one or more relevant sparse outputs to provide the lossless and sparse representation of the group of NNFs; and (d) outputting the lossless and sparse representation of the group of NNFs.

    LOCAL BASED DRIVING
    56.
    发明公开
    LOCAL BASED DRIVING 审中-公开

    公开(公告)号:US20240116534A1

    公开(公告)日:2024-04-11

    申请号:US18459423

    申请日:2023-09-01

    Inventor: Igal Raichelgauz

    CPC classification number: B60W60/001 B60W40/02 B60W50/0097 B60W2050/0005

    Abstract: A method for localized driving, the method includes (a) obtaining information about locations that are associated with multi-domain identifiers (MDIs) statistics, MDIs of each location are indicative of elements affecting a vehicle at the location; (b) obtaining an expected local path of a vehicle; (c) identifying path related locations, by a processing circuit, based on the expected local path and the information about the locations; and (d) determining, by the processing circuit, expected local path MDIs statistics for use in at least partially autonomous driving of a vehicle through the expected local path.

    TRAINING AND TESTING A MACHINE LEARNING PROCESS

    公开(公告)号:US20240083431A1

    公开(公告)日:2024-03-14

    申请号:US18510591

    申请日:2023-11-15

    CPC classification number: B60W30/165 G06F11/3688

    Abstract: A method for training and testing a machine learning process, the method includes (a) learning virtual fields based on simulations of behaviors of a vehicle when faced with situations involving objects within environments of the vehicle, the virtual fields represent potential impacts of objects on the behaviors of the vehicle, wherein the learning is based on a virtual physical mode; (b) training the machine learning process to generate the virtual fields by applying a training process that uses outcomes of the simulations to provide a trained machine learning process; and (c) testing the trained machine learning process by feeding the trained machine learning process with other situations to provide test results.

    School zone alert
    58.
    发明授权

    公开(公告)号:US11897497B2

    公开(公告)日:2024-02-13

    申请号:US17301379

    申请日:2021-03-31

    Abstract: A method for generating at least one school zone indicator, the method may include receiving by a vehicle computerized system, school zone indicators, wherein the school zone indicators are indicative of school zone elements; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching for one or more school zone indicators of the school zone indicators; wherein the school zone element is selected out of (i) a school zone object and (ii) a school zone situation; autonomously determining, when finding at least one of the one or more school zone identifiers, that the vehicle is driving towards a school zone or is within the school zone; and generating an alert when determining that the vehicle is driving towards the school zone or is within the school zone.

    PROVIDING A PREDICTION OF A ROLL ANGLE OF A MOTORCYCLE

    公开(公告)号:US20230410357A1

    公开(公告)日:2023-12-21

    申请号:US18338307

    申请日:2023-06-20

    Abstract: A method for detecting a state of a motorcycle, the method includes (i) obtaining, by a camera of the motorcycle, one or images of an environment of the vehicle; wherein there is a known angular relationship between the field of view of the camera and predefined part or a vertical axis of the motorcycle; (ii) generating signatures of the one or more images; (iii) searching, using the signatures, multiple groups of concept data structures, for one or more matching concept data structures; wherein different groups of concept data structures represent images virtually acquired at different roll angles; and (iv) determining a roll angle of the motorcycle based on an outcome of the searching and the known angular relationship.

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