Patent application EP21176922A (EP), filed 2021-05-31
Publication number: EP4099211A1
Title: METHOD AND DEVICE FOR TRAINING A MACHINE LEARNING ALGORITHM
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.
IPC codes: G06K   9/    00            A I
G06K   9/    62            A I
G06F  18/   214            A I
G06F  18/  2413            A I
G06F  18/    25            A I
G06V  10/    25            A I
G06V  10/    42            A I
G06V  10/   774            A I
G06V  10/    82            A I
G06V  20/    56            A I
Claims: number: 1
text: Computer implemented method for training a machine learning algorithm configured to process primary data captured by at least one primary sensor (13) in order to determine at least one property of entities in the environment of the at least one primary sensor (13), the method comprising:
number: 2
text: Method according to claim 1, wherein
the predetermined threshold for the confidence value is zero.
number: 3
text: Method according to claim 1 or 2, wherein
number: 4
text: Method according to claim 3, wherein
number: 5
text: Method according to claim 4, wherein
number: 6
text: Method according to claim 4 or 5, wherein
number: 7
text: Method according to claim 6, wherein
the expected range rate is estimated for each label (19) based on a speed vector which is estimated for a respective label (19) by using differences of label positions determined based on the auxiliary data (23, 33, 35) at different points in time.
number: 8
text: Method according to anyone of claims 3 to 7, wherein
number: 9
text: Method according to claim 8, wherein
number: 10
text: Method according to claim 9, wherein
number: 11
text: Method according to anyone of the preceding claims, wherein
number: 12
text: Device for training a machine learning algorithm, the device comprising:
number: 13
text: Device according to claim 12, wherein
the at least one primary sensor (13) includes at least one radar sensor and the at least one auxiliary sensor (15) includes at least one Lidar sensor and/or at least one a camera.
number: 14
text: Computer system, the computer system being configured to carry out the computer implemented method of at least one of claims 1 to 11.
number: 15
text: Non-transitory computer readable medium comprising instructions for carrying out the computer implemented method of at least one of claims 1 to 11.
Office action: search_report, dated 2022-12-07
Prior art cited: ref_id: US2021156963A1
ref_type: patent
severity: background
ref_id: US2021103027A1
ref_type: patent
severity: background
ref_id: - MANUEL CARRANZA-GARC\'IA ET AL: "Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 8 April 2021 (2021-04-08), XP081936390, DOI: 10.1016/J.NEUCOM.2021.04.001
ref_type: npl
severity: background
ref_id: US2021103027
ref_type: patent
severity: background
ref_id: US2021156963
ref_type: patent
severity: background
ref_id: - MANUEL CARRANZA-GARC\'IA ET AL, "Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20210408), doi:10.1016/J.NEUCOM.2021.04.001, XP081936390 [A] 1-15 * section 3.5.2 *
ref_type: npl
severity: background
Final disposition: pending (2022-12-07)