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公开(公告)号：US20220114489A1
公开(公告)日：2022-04-14
申请号：US17495332
申请日：2021-10-06
发明人： Jittu Kurian , Jan Siegemund , Mirko Meuter
IPC分类号： G06N20/00
摘要： A computer-implemented method for training a machine-learning method comprises the following steps carried out by computer hardware components: determining measurement data from a first sensor; determining approximations of ground truths based on a second sensor; and training the machine-learning method based on the measurement data and the approximations of ground truths; wherein approximations of ground truths of lower-approximation quality have a lower effect on the training than approximations of ground truths of higher-approximation quality.
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公开(公告)号：US10861160B2
公开(公告)日：2020-12-08
申请号：US16143741
申请日：2018-09-27
发明人： Ido Freeman , Jan Siegemund
摘要： A device for assigning one of a plurality of predetermined classes to each pixel of an image, the device is configured to receive an image captured by a camera, the image comprising a plurality of pixels; use an encoder convolutional neural network to generate probability values for each pixel, each probability value indicating the probability that the respective pixel is associated with one of the plurality of predetermined classes; generate for each pixel a class prediction value from the probability values, the class prediction value predicting the class of the plurality of predetermined classes the respective pixel is associated with; use an edge detection algorithm to predict boundaries between objects shown in the image, the class prediction values of the pixels being used as input values of the edge detection algorithm; and assign a label of one of the plurality of predetermined classes to each pixel of the image.
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公开(公告)号：US20220402504A1
公开(公告)日：2022-12-22
申请号：US17807631
申请日：2022-06-17
发明人： Jan Siegemund , Jittu Kurian , Sven Labusch , Dominic Spata , Adrian Becker , Simon Roesler , Jens Westerhoff
摘要： A computer-implemented method for generating ground truth data may include the following steps carried out by computer hardware components: for a plurality of points in time, acquiring sensor data for a respective point in time; and for at least a subset of the plurality of points in time, determining ground truth data of the respective point in time based on the sensor data of at least one present and/or past point of time and at least one future point of time.
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公开(公告)号：US11093762B2
公开(公告)日：2021-08-17
申请号：US16406356
申请日：2019-05-08
发明人： Jan Siegemund , Christian Nunn
摘要： A method for validation of an obstacle candidate identified within a sequence of image frames comprises the following steps: A. for a current image frame of the sequence of image frames, determining within the current image frame a region of interest representing the obstacle candidate, dividing the region of interest into sub-regions, and, for each sub-region, determining a Time-To-Contact (TTC) based on at least the current image frame and a preceding or succeeding image frame of the sequence of image frames; B. determining one or more classification features based on the TTCs of the sub-regions determined for the current image frame; and C. classifying the obstacle candidate based on the determined one or more classification features.
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公开(公告)号：US20220383146A1
公开(公告)日：2022-12-01
申请号：US17804652
申请日：2022-05-31
发明人： Markus Schoeler , Jan Siegemund , Christian Nunn , Yu Su , Mirko Meuter , Adrian Becker , Peet Cremer
摘要： 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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公开(公告)号：US20220026568A1
公开(公告)日：2022-01-27
申请号：US17384493
申请日：2021-07-23
发明人： Mirko Meuter , Jittu Kurian , Yu Su , Jan Siegemund , Zhiheng Niu , Stephanie Lessmann , Saeid Khalili Dehkordi , Florian Kästner , Igor Kossaczky , Sven Labusch , Arne Grumpe , Markus Schoeler , Moritz Luszek , Weimeng Zhu , Adrian Becker , Alessandro Cennamo , Kevin Kollek , Marco Braun , Dominic Spata , Simon Roesler
摘要： A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.
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公开(公告)号：US20230037900A1
公开(公告)日：2023-02-09
申请号：US17817466
申请日：2022-08-04
发明人： Mirko Meuter , Christian Nunn , Jan Siegemund , Jittu Kurian , Alessandro Cennamo , Marco Braun , Dominic Spata
IPC分类号： G01S13/931 , G01S13/89
摘要： The present disclosure is directed at systems and methods for determining objects around a vehicle. In aspects, a system includes a sensor unit having at least one radar sensor arranged and configured to obtain radar image data of external surroundings to determine objects around a vehicle. The system further includes a processing unit adapted to process the radar image data to generate a top view image of the external surroundings of the vehicle. The top view image is configured to be displayed on a display unit and useful to indicate a relative position of the vehicle with respect to determined objects.
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公开(公告)号：US20220261653A1
公开(公告)日：2022-08-18
申请号：US17661912
申请日：2022-05-03
发明人： Weimeng Zhu , Jan Siegemund
摘要： A method of processing image data in a connectionist network includes: determining, a plurality of offsets, each offset representing an individual location shift of an underlying one of the plurality of output picture elements, determining, from the plurality of offsets, a grid for sampling from the plurality of input picture elements, wherein the grid comprises a plurality of sampling locations, each sampling location being defined by means of a respective pair of one of the plurality of offsets and the underlying one of the plurality of output picture elements, sampling from the plurality of input picture elements in accordance with the grid, and transmitting, as output data for at least a subsequent one of the plurality of units of the connectionist network, a plurality of sampled picture elements resulting from the sampling, wherein the plurality of sampled picture elements form the plurality of output picture elements.
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公开(公告)号：US11386329B2
公开(公告)日：2022-07-12
申请号：US16202688
申请日：2018-11-28
发明人： Weimeng Zhu , Jan Siegemund
摘要： A method of processing image data in a connectionist network includes: determining, a plurality of offsets, each offset representing an individual location shift of an underlying one of the plurality of output picture elements, determining, from the plurality of offsets, a grid for sampling from the plurality of input picture elements, wherein the grid comprises a plurality of sampling locations, each sampling location being defined by means of a respective pair of one of the plurality of offsets and the underlying one of the plurality of output picture elements, sampling from the plurality of input picture elements in accordance with the grid, and transmitting, as output data for at least a subsequent one of the plurality of units of the connectionist network, a plurality of sampled picture elements resulting from the sampling, wherein the plurality of sampled picture elements form the plurality of output picture elements.
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公开(公告)号：US20220172485A1
公开(公告)日：2022-06-02
申请号：US17457339
申请日：2021-12-02
发明人： Marco Braun , Moritz Luszek , Jan Siegemund
摘要： A method is provided for semantic segmentation of an environment of a vehicle. Via a processing device, a grid of cells is defined dividing the environment of the vehicle. A radar point cloud is received from a plurality of radar sensors, and at least one feature of the radar point cloud is assigned to each grid cell. By using a neural network including deterministic weights, high-level features are extracted for each grid cell. Several classes are defined for the grid cells. For layers of a Bayesian neural network, various sets of weights are determined probabilistically. Via the Bayesian neural network, confidence values are determined for each class and for each grid cell based on the high-level features and based on the various sets of weights in order to determine a predicted class and an extent of uncertainty for the predicted class for each grid cell.
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