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公开(公告)号：US20220176949A1
公开(公告)日：2022-06-09
申请号：US17504380
申请日：2021-10-18
申请人： Robert Bosch GmbH
发明人： Christian Heinzemann , Andreas Heyl , Christoph Gladisch , Jens Oehlerking , Martin Butz , Martin Herrmann , Michael Rittel , Nadja Schalm , Tino Brade
摘要： A method for controlling a vehicle. In the method, data of a digital road map are read in, zones are determined for the digital road map, and possible sequences of trips along a road of the digital road map are ascertained as a function of the determined zones. Furthermore, it is ascertained, as a function of sensor data and/or current driving data of the vehicle, whether a current or predicted traffic situation is outside the possible sequences or corresponds to a possible sequence that is determined as being outside an intended operating range. If the current or predicted traffic situation is outside the possible sequences or corresponds to the possible sequence outside the intended operating range, a measure is determined and the vehicle is controlled as a function of the measure that is taken.
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公开(公告)号：US20220262103A1
公开(公告)日：2022-08-18
申请号：US17649175
申请日：2022-01-27
申请人： Robert Bosch GmbH
IPC分类号： G06V10/776 , G06V10/774 , G06V10/98
摘要： A computer-implemented method for testing conformance between images generated by a synthetic image generator and images obtained from authentic visual data. A conformance test result results from comparing results of global sensitivity analyses used to rank the effect of visual parameters on the computer vision model both for synthetic and authentic visual data.
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公开(公告)号：US20220222926A1
公开(公告)日：2022-07-14
申请号：US17646914
申请日：2022-01-04
申请人： Robert Bosch GmbH
IPC分类号： G06V10/776 , G06V10/764 , G06V10/766 , G06V10/774
摘要： Modifying a visual parameter specification characterising the operational design domain of the computer vision model by improving the visual parameter specification according to a sensitivity analysis of the computer vision model.
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公开(公告)号：US11908178B2
公开(公告)日：2024-02-20
申请号：US17646911
申请日：2022-01-04
申请人： Robert Bosch GmbH
IPC分类号： G06V10/776 , G06V10/762 , G06V10/764
CPC分类号： G06V10/776 , G06V10/762 , G06V10/764
摘要： Reducing the number of parameters in a visual parameter set based on a sensitivity analysis of how a given visual parameter affects the performance of a computer vision model to provide a verification parameter set having a reduced size.
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公开(公告)号：US11416371B2
公开(公告)日：2022-08-16
申请号：US16878908
申请日：2020-05-20
申请人： Robert Bosch GmbH
发明人： Joachim Sohns , Christoph Gladisch , Thomas Heinz
IPC分类号： G06F11/34 , G06F30/20 , G06F30/15 , G06F30/17 , G06F30/3308 , G06F119/02
摘要： A method for evaluating a simulation model. In the method, for selected test cases, a first performance index is calculated in the simulation model. For the same test case, a second performance index is ascertained in a real test environment. For each of the test cases, a difference is calculated between the first performance index and the second performance index, and a signal metric is determined. For each of the signal metrics, an interrelation between the difference and the respective signal metric is investigated. The signal metric that exhibits the closest interrelation with the difference is selected.
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公开(公告)号：US20220230418A1
公开(公告)日：2022-07-21
申请号：US17646967
申请日：2022-01-04
申请人： Robert Bosch GmbH
IPC分类号： G06V10/774 , G06T7/11 , G06V10/82 , G06N3/04 , G06N3/08 , G06V10/764
摘要： A computer-implemented method for training a computer vision model to characterise elements of observed scenes parameterized using visual parameters. During the iterative training of the computer vision model, the latent variables of the computer vision model are altered based upon a (global) sensitivity analysis used to rank the effect of visual parameters on the computer vision model.
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公开(公告)号：US20220230072A1
公开(公告)日：2022-07-21
申请号：US17646958
申请日：2022-01-04
申请人： Robert Bosch GmbH
IPC分类号： G06N5/00 , G06V10/778 , G06V10/776 , G06V10/771 , G06V10/774 , G06V10/82 , G06V10/58
摘要： Facilitating the description or configuration of a computer vision model by generating a data structure comprising a plurality of language entities defining a semantic mapping of visual parameters to a visual parameter space based on a sensitivity analysis of the computer vision model.
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公开(公告)号：US20210364393A1
公开(公告)日：2021-11-25
申请号：US17195275
申请日：2021-03-08
申请人： Robert Bosch GmbH
发明人： Christoph Gladisch , Ji Su Yoon , Joachim Sohns , Philipp Glaser , Thomas Heinz , Daniel Seiler-Thull
IPC分类号： G01M99/00
摘要： A method for testing a technical system. Tests are carried out with the aid of a simulation of the system. The tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and different error measures of the simulation. On the basis of the fulfillment measure and each of the error measures, a classification of the tests is carried out as either reliable or unreliable case by case. A selection among the error measures is made on the basis of a number of the tests classified as reliable.
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公开(公告)号：US12051234B2
公开(公告)日：2024-07-30
申请号：US17646914
申请日：2022-01-04
申请人： Robert Bosch GmbH
IPC分类号： G06V10/776 , G06V10/764 , G06V10/766 , G06V10/774
CPC分类号： G06V10/776 , G06V10/764 , G06V10/766 , G06V10/774
摘要： Modifying a visual parameter specification characterising the operational design domain of the computer vision model by improving the visual parameter specification according to a sensitivity analysis of the computer vision model.
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10.发明公开公开(公告)号：US20240046614A1
公开(公告)日：2024-02-08
申请号：US18264569
申请日：2022-01-25
申请人： Robert Bosch GmbH
IPC分类号： G06V10/764 , G06V20/40 , G06V20/56 , G06V10/82
CPC分类号： G06V10/764 , G06V20/41 , G06V20/56 , G06V10/82
摘要： A computer-implemented method for generating reliability indication data of a computer vision model. The method includes: obtaining visual data including an input image or sequence representing an observed scene, the visual data being characterizable by a first set of visual parameters; analysing the observed scene in the visual data using a computer vision reliability model sensitive to a second set of visual parameters, the second set of visual parameters includes a subset of the first set of visual parameters, and is obtained from the first set of visual parameters according to a sensitivity analysis applied to a plurality of parameters in the first set of visual parameters, the sensitivity analysis is performed during an offline training phase of the computer vision reliability model; generating reliability indication data of the observed scene using the analysis of the observed scene; and outputting the reliability indication data of the computer vision model.
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