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    Methods and systems for object detection
    1.
    发明授权
    Methods and systems for object detection 有权

    公开(公告)号：US11604272B2

    公开(公告)日：2023-03-14

    申请号：US16904835

    申请日：2020-06-18

    Applicant: APTIV TECHNOLOGIES LIMITED

    Inventor： Yu Su ,  Weimeng Zhu ,  Florian Kästner ,  Adrian Becker

    IPC: G01S13/931 ,  G01S13/89

    Abstract: A computer implemented method for object detection includes: determining a grid, the grid comprising a plurality of grid cells; determining, for a plurality of time steps, for each grid cell, a plurality of respective radar detection data, each radar detection data indicating a plurality of radar properties; determining, for each time step, a respective radar map indicating a pre-determined radar map property in each grid cell; converting the respective radar detection data of the plurality of grid cells for the plurality of time steps to a point representation of pre-determined first dimensions; converting the radar maps for the plurality of time steps to a map representation of pre-determined second dimensions, wherein the pre-determined first dimensions and the pre-determined second dimensions are at least partially identical; concatenating the point representation and the map representation to obtain concatenated data; and carrying out object detection based on the concatenated data.

    Partially-Learned Model for Speed Estimates in Radar Tracking
    2.
    发明申请
    Partially-Learned Model for Speed Estimates in Radar Tracking 有权

    公开(公告)号：US20220308205A1

    公开(公告)日：2022-09-29

    申请号：US17644464

    申请日：2021-12-15

    Applicant: Aptiv Technologies Limited

    Inventor： Simon Roesler ,  Adrian Becker ,  Jan K. Schiffmann

    IPC: G01S13/931 ,  G01S13/89 ,  G01S13/58 ,  G06N3/02

    Abstract: This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

    Method and Device for Training a Machine Learning Algorithm
    6.
    发明申请
    Method and Device for Training a Machine Learning Algorithm 有权

    公开(公告)号：US20220383146A1

    公开(公告)日：2022-12-01

    申请号：US17804652

    申请日：2022-05-31

    Applicant: Aptiv Technologies Limited

    Inventor： Markus Schoeler ,  Jan Siegemund ,  Christian Nunn ,  Yu Su ,  Mirko Meuter ,  Adrian Becker ,  Peet Cremer

    IPC: G06N5/02 ,  G01S7/41

    Abstract: A method is provided for training a machine-learning algorithm which relies on primary data captured by at least one primary sensor. Labels are identified based on auxiliary data provided by at least one auxiliary sensor. A care attribute or a no-care attribute is assigned to each label by determining a perception capability of the primary sensor for the label based on the primary data and based on the auxiliary data. Model predictions for the labels are generated via the machine-learning algorithm. A loss function is defined for the model predictions. Negative contributions to the loss function are permitted for all labels. Positive contributions to the loss function are permitted for labels having a care attribute, while positive contributions to the loss function for labels having a no-care attribute are permitted only if a confidence of the model prediction for the respective label is greater than a threshold.

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