The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 12, 2023

Filed:

Sep. 09, 2020
Applicant:

Nec Laboratories America, Inc., Princeton, NJ (US);

Inventors:

Jianwu Xu, Titusville, NJ (US);

Haifeng Chen, West Windsor, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G07C 5/08 (2006.01); B60W 50/02 (2012.01); G01R 31/317 (2006.01); G06N 3/082 (2023.01); B60W 50/06 (2006.01); B60W 60/00 (2020.01); B60R 16/023 (2006.01); G06N 3/088 (2023.01); G06V 20/20 (2022.01); G06V 20/56 (2022.01); G06F 18/214 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G05D 1/00 (2006.01);
U.S. Cl.
CPC ...
G07C 5/0808 (2013.01); B60R 16/0231 (2013.01); B60W 50/0205 (2013.01); B60W 50/06 (2013.01); B60W 60/001 (2020.02); B60W 60/0027 (2020.02); G01R 31/3172 (2013.01); G01R 31/31707 (2013.01); G06F 18/2148 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/082 (2013.01); G06N 3/088 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06V 20/56 (2022.01); G06V 20/588 (2022.01); G05D 1/0088 (2013.01); G05D 2201/0213 (2013.01);
Abstract

A computer-implemented method for implementing electronic control unit (ECU) testing optimization includes capturing, within a neural network model, input-output relationships of a plurality of ECUs operatively coupled to a controller area network (CAN) bus within a CAN bus framework, including generating the neural network model by pruning a fully-connected neural network model based on comparisons of maximum values of neuron weights to a threshold, reducing signal connections of a plurality of collected input signals and a plurality of collected output signals based on connection weight importance, ranking importance of the plurality of collected input signals based on the neural network model, generating, based on the ranking, a test case execution sequence for testing a system including the plurality of ECUs to identify flaws in the system, and initiating the test case execution sequence for testing the system.


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